Preamble

From Reading the Brain to Regulating the Brain: Applications and Mechanisms of Brain-Computer Interface Neuromodulation from a Psychological Perspective

Abstract

Brain-Computer Interface (BCI) technology has traditionally focused on "reading" brain signals to restore communication and motor functions. However, recent advancements are shifting the paradigm toward "regulating" the brain—using BCI-based neuromodulation to actively intervene in mental health and cognitive processes. From a psychological perspective, this transition represents a move from passive observation to active closed-loop intervention. This paper reviews the current state of BCI-based neuromodulation, exploring its applications in treating psychological disorders, enhancing cognitive functions, and facilitating neurorehabilitation. We further analyze the underlying neural mechanisms, including neuroplasticity and closed-loop feedback control, and discuss the ethical implications and future directions of this transformative technology.

1. Introduction

For decades, the primary goal of Brain-Computer Interface (BCI) research has been to decode neural activity into control commands for external devices, effectively "reading" the user's intentions to bypass damaged neuromuscular pathways \cite{wolpaw2002brain}. While these "reading" applications have achieved significant success in restoring motor and communication functions, a new frontier is emerging: "regulating" the brain. This paradigm shift involves using BCI systems not just to output information, but to provide targeted feedback or stimulation that modifies neural activity itself.

From a psychological and neuroscientific standpoint, BCI-based neuromodulation offers a powerful tool for investigating and treating the mind. By creating a closed-loop system where brain activity is monitored and then influenced in real-time, researchers can target specific neural circuits associated with cognitive functions and mental health disorders. This paper explores the transition from "reading" to "regulating" the brain, focusing on the psychological applications and the mechanisms that enable these interventions.

2. BCI-Based Neuromodulation: Applications in Psychology

The application of BCI for neuromodulation spans several domains, ranging from clinical treatment to cognitive enhancement.

2.1 Treatment of Psychological and Psychiatric Disorders

BCI-based interventions, particularly neurofeedback, have shown promise in treating various psychological disorders. By providing users with real-time visual or auditory representations of their brain activity (e.g., alpha wave power or amygdala activation), BCI systems allow individuals to learn self-regulation strategies.

• **Attention-Deficit/Hyperactivity Disorder (ADHD):

摘要

Brain-Computer Interface (BCI) is an interactive technology that establishes a direct communication channel between the brain and external devices by collecting, decoding, and providing feedback on neural signals. This technology opens a brand-new path for understanding and expanding mental potential. This study explores the theoretical foundations and clinical applications of BCI technology in the fields of cognitive enhancement and psychotherapy. We construct an ethical analysis model for psychological adaptability and propose a "Technology Dependency Risk Index" to quantify the potential impact of technological reliance on individual psychological autonomy. By integrating cutting-edge technologies such as Artificial Intelligence (AI) and Virtual Reality (VR), this research provides innovative solutions for intervening in complex psychological processes.

Future research must further enhance the "humanized" experience of BCI applications and conduct in-depth investigations into the long-term effects of technological dependency on psychological autonomy and emotional regulation. Furthermore, by utilizing cognitive psychological theories—such as attention and memory—alongside neural plasticity theory, researchers can optimize decoding feature selection and neurofeedback design. These advancements will facilitate the construction of more flexible and personalized psychological intervention paradigms.

关键词

Neural Plasticity and Neural Encoding/Decoding: Technical Dependency and Risk Indices

1. Introduction

The rapid advancement of brain-computer interfaces (BCI) and neuroprosthetics has brought the concepts of neural plasticity and neural encoding/decoding to the forefront of neuroscience and engineering. Neural plasticity refers to the brain's ability to reorganize itself by forming new neural connections throughout life, while neural encoding and decoding involve the translation of neural activity into external commands and vice versa. However, as these technologies become more integrated with human cognitive and motor functions, a critical need arises to evaluate the "Technical Dependency Risk Index." This index quantifies the potential risks associated with the long-term reliance on artificial systems to mediate or augment natural neural processes.

2. Neural Plasticity and Adaptive Encoding

Neural plasticity is the fundamental mechanism that allows the brain to adapt to external stimuli and internal changes. In the context of neural decoding, the brain does not remain a static signal source; rather, it actively adapts to the decoder's logic. This phenomenon, often referred to as "closed-loop plasticity," implies that the user and the machine undergo a mutual learning process.

When a subject utilizes a BCI, the neural population activity $\mathbf{x}$ is mapped to a control signal $\mathbf{y}$ via a decoding function $f$:
$$\mathbf{y} = f(\mathbf{x}; \theta)$$
where $\theta$ represents the parameters of the decoder. Due to neural plasticity, the brain modifies the distribution of $\mathbf{x}$ to minimize the error between the intended goal and the actual output $\mathbf{y}$. While this enhances performance, it also creates a deep functional dependency on the specific parameters $\theta$ of the technology.

3. Neural Decoding and Information Bottlenecks

Neural decoding technologies aim to reconstruct sensory experiences or motor intentions from complex spatiotemporal patterns of neural firing. Modern deep learning approaches have significantly improved decoding accuracy; however, they often operate as "black boxes," making it difficult to assess how much the biological system is compensating for decoding inaccuracies.

[FIGURE:1]

As shown in [FIGURE:1], the information flow between the biological brain and the decoding interface is subject to constraints. If the decoding algorithm relies on specific high-frequency features that are not robust, the brain may "over-train" certain neural pathways to maintain those features. This specialization can lead to a reduction in the brain's natural flexibility, a risk factor that

1 引言

Can humanity transcend its own biological limitations? From Plato to Descartes, and from philosophy to modern psychology, humanity has long contemplated the relationship between the mind and the body. In the contemporary era, Brain-Computer Interface (BCI) technology offers a brand-new perspective on this question: it not only connects the brain with machines but also blurs the boundaries between the psychological and the physiological, and between the natural and the artificial.

Initially, BCI was primarily used for neurorehabilitation, such as assisting paralyzed patients. However, recent developments have seen its application expand beyond the clinical field into areas such as cognitive enhancement and psychological intervention, garnering significant attention within the field of psychology (Chai et al., 2024). By monitoring and decoding the neural activity of the human brain (McFarland & Wolpaw, 2011), BCI has opened up entirely new pathways for understanding and expanding mental potential.

Since the inception of cognitive psychology, the academic community has generally believed that the potential of the brain can be unlocked through specific interventions. BCI technology provides a more direct mechanism for this objective. By capturing neural signals and providing real-time feedback, BCI can not only enhance attention and memory but also demonstrates significant potential in the field of psychotherapy (Tsai et al., 2025; Scott & Raftery, 2021; Gordon & Seth, 2024). From a psychological perspective, BCI-based cognitive enhancement is primarily achieved through two distinct modalities.

The first modality involves the direct modulation of neural activity in specific brain regions.

This direct intervention method is particularly applicable to clinical cases where there are clear neurological deficits (Violante et al., 2023). The second modality is indirect modulation, which primarily relies on non-invasive technologies such as Electroencephalography (EEG). When combined with Virtual Reality (VR) technology, these systems monitor and provide feedback on an individual's brain activity (Drigas & Sideraki, 2024). This approach does not target specific brain neurons directly but rather operates through external sensory loops, making it suitable not only for neurological rehabilitation but also for broader cognitive training. In clinical applications, these are often used in conjunction with other assistive technologies, such as Transcranial Direct Current Stimulation (tDCS) and neurofeedback techniques. These methods leverage the brain's neuroplasticity to facilitate improvements in cognitive function and learning (Meinzer et al., 2013). It is important to note that while neuroplasticity serves as the biological foundation for these technological applications, BCI specifically refers to the enhancement of cognitive functions through the collection and decoding of neural signals, such as those obtained via Functional Near-Infrared Spectroscopy (fNIRS).

In the field of psychotherapy, BCI technology provides a new path for psychological intervention. Research has shown that neurofeedback can effectively regulate emotional states (Sitaram et al., 2017). In a follow-up study by Dobbins and colleagues, it was observed that BCI interventions for Post-Traumatic Stress Disorder (PTSD) provided more targeted treatment plans through the integration of neural data.

Eshuis and colleagues found that BCI can assist in the delivery of personalized neuromodulation within psychotherapy, though this also raises ethical considerations regarding the use of such technology (Gordon & Seth, 2024; Chaudhary et al., 2016; Roy et al., 2020). Furthermore, long-term reliance on external devices for cognitive or emotional regulation may lead to psychological dependency, potentially impacting an individual's autonomy and their relationship with the device (Słaby, 2021; Yue, 2023). Given the potential of BCI in cognitive enhancement and psychotherapy, a deeper exploration from a psychological perspective is required to establish ethical frameworks and practical solutions for its application in the field.

2.1 BCI

Basic Principles of Brain-Computer Interfaces

A Brain-Computer Interface (BCI) is an interactive system that establishes a direct communication pathway between the human brain and external devices. By collecting and decoding neural signals, BCIs facilitate the exchange of information and control commands without relying on traditional neuromuscular pathways \cite{Wolpaw et al., 2002}. In the field of psychology, these systems provide innovative tools for emotion regulation.

The functional architecture of a BCI typically involves the acquisition, decoding, and feedback of neurophysiological signals. Common modalities include Electrocorticography (ECoG) and functional Near-Infrared Spectroscopy (fNIRS). Among these acquisition methods, fNIRS has gained significant attention for its portability and non-invasive nature \cite{Liu et al., 2025}.

2.1.1 信号采集过程

Signal acquisition is a fundamental process that relies on both non-invasive and partially invasive technologies, each characterized by distinct technical properties. The choice of signal acquisition method is heavily influenced by the specific application context, such as experimental research or clinical use. In the fields of neuroscience and psychology, as noted by \cite{Poldrack & Farah, 2015}, non-invasive techniques—which involve placing electrodes on the scalp—are widely utilized to study attention and quantify human cognition \cite{Ismail et al., 2020}. Furthermore, functional Near-Infrared Spectroscopy (fNIRS) has proven particularly suitable for investigating complex cognitive processes, including working memory and emotional regulation. Recent research indicates that fNIRS can effectively track physiological changes, providing critical insights into emotional states.

In the field of cognitive science, \cite{Pinti et al., 2020} suggests that the study of psychological processes through fNIRS provides a more comprehensive understanding of brain function. Recent studies have emphasized the importance of integrating fNIRS into experimental designs to enhance data reliability \cite{Li et al., 2022}. This integration not only facilitates a deeper exploration of the relationships between emotion and cognitive function but also paves the way for more robust neuroscientific frameworks.

2.1.2 信号解码：读脑的关键

Neural Signal Decoding and Preprocessing

Neural signal decoding is fundamentally influenced by the quality of the underlying data. To improve signal fidelity, it is essential to mitigate the impact of various noise sources and artifacts. This is particularly critical for functional Near-Infrared Spectroscopy (fNIRS), which is susceptible to both physiological interference and motion artifacts \cite{Uriguen & Garcia Zapirain, 2015; Pinti et al., 2020}.

Signal Enhancement Techniques

Several advanced signal processing methodologies have been developed to isolate neural activity from background noise. Among the most prominent are:

• Independent Component Analysis (ICA): A computational method used to separate a multivariate signal into additive subcomponents. In the context of fNIRS, ICA is frequently employed to identify and remove artifacts such as heartbeat, respiration, and blood pressure fluctuations \cite{Maddirala Veluvolu}.
• Continuous Wavelet Transform (CWT): This technique provides a time-frequency representation of the signal, allowing for the detection of transient artifacts and the localized analysis of non-stationary neural data.
• ERASE: An automated artifact removal algorithm specifically designed to enhance the signal-to-noise ratio in functional neuroimaging by identifying and suppressing components unrelated to the task-based hemodynamic response.

By applying these preprocessing steps, researchers can ensure that the subsequent machine learning or deep learning models are trained on high-quality data, thereby improving the accuracy and reliability of neural signal decoding.

实验

Recent studies in functional Near-Infrared Spectroscopy (fNIRS), such as those by \cite{Gagnon et al., 2012} and \cite{Von Lühmann}, have proposed the use of correlation analysis to further enhance signal processing. A critical challenge in signal decoding involves identifying features that are most relevant to the underlying physiological activity. For this purpose, researchers typically distinguish between time-domain features, such as mean amplitude or variance, and spatial-domain features, such as Common Spatial Patterns (CSP) as discussed by \cite{Ismail}. These methodologies aim to isolate task-related hemodynamic responses from systemic noise, thereby improving the accuracy of brain-computer interface (BCI) classifications and neuroimaging interpretations.

12 Hz

The Mattioli Convolutional Neural Network has significantly enhanced decoding performance. In various task scenarios, functional Near-Infrared Spectroscopy (fNIRS) signals are utilized for feature extraction \cite{Pinti et al., 2020}. As fNIRS technology continues to advance, it provides increasingly powerful tools for psychological research, representing the state-of-the-art in signal decoding.

The Support Vector Machine \cite{Lotte et al., 2007} and the Mattioli network have not only improved performance within specific task scenarios but have also advanced the broader field of signal processing.

分析

Innovative methods such as those proposed by Borhani and Theil have been utilized to model continuous cursor movement (OwARR). In psychological applications, Liang developed a framework based on classification methods to analyze operational tasks, providing a foundation for cognitive enhancement. Furthermore, Siirtola introduced a model for emotion regulation that utilizes interpretable features, offering new tools for psychotherapy and emotional management. Advances in neural signal decoding technology have significantly improved the accuracy of intention recognition. For instance, a system achieved a decoding accuracy of 97.36% [FIGURE:97], while the development of embedded decoders has opened new pathways for practical applications.

These advancements provide new possibilities for patients, such as those with neurological disorders. Research by Willett demonstrated significant improvements, achieving accuracies of 45.6% and 91.8% in specific tasks. Such progress not only enhances technical capabilities but also provides a robust technological foundation for cognitive enhancement and psychological interventions.

2.1.3 信号反馈机制：调脑的基础

For instance, in systems based on visual feedback, Ismail (2020) and Kojima et al. (2024) proposed selective attention mechanisms that provide real-time information to help individuals optimize their cognitive states. By providing feedback to the individual, these systems can adaptively modulate the user's brain activity.

Feedback enhances an individual's learning and neuroplasticity through various real-time modalities, leading to more natural and efficient interaction processes (Sitaram et al., 2017; Young et al., 2014). Specifically, neurofeedback can regulate the prefrontal cortex via real-time modulation. Herron (2024) introduced AttentionCARE, an Augmented Reality (AR)-based feedback system that helps individuals shift their focus from negative stimuli to neutral ones. In applications for Autistic Spectrum Disorder (ASD), researchers utilize signals to decode emotional states and employ visual feedback to help individuals understand and regulate their emotions, thereby improving social outcomes (Papanastasiou et al., 2020). The key to effective feedback lies in the real-time interaction between the device and the brain, which simulates natural neural circuits to enhance adaptability and therapeutic efficacy. The design of these feedback mechanisms requires further optimization; psychological research in this area provides new pathways for emotional intervention and cognitive enhancement.

Continuous advancements in signal acquisition, decoding, and feedback technologies have significantly expanded their psychological applications, particularly in the fields of cognitive enhancement and mental health. Future research should focus on developing more sophisticated models to further improve performance, providing more robust tools for psychological interventions (Jin et al., 2024).

2.2 单模态

From Brain Reading to Brain Modulation: Closed-Loop Applications in Psychology

In psychological research and intervention, fNIRS technology can be categorized into primary paradigms. These paradigms rely on an individual's active participation through neurofeedback, where the individual exerts control over their own neural activity.

The application of fNIRS paradigms in psychology (Myrden & Chau, 2016) provides a theoretical foundation for personalized interventions (Arico et al., 2016). This technology has been widely utilized in fields such as attention, emotion regulation, and cognitive enhancement. By utilizing neurofeedback, individuals can achieve significant improvements in cognitive performance (Arvaneh et al., 2016; Walter et al., 2017). This process involves decoding psychological states and subsequently modulating them through feedback to optimize an individual's cognitive functions. Emotion regulation represents a critical domain, particularly within psychotherapy (Micoulaud-Franchi et al., 2016). Neurofeedback can significantly reduce anxiety symptoms (Ehrlich et al., 2016) by decoding an individual's emotional state and assisting them in self-regulation. Furthermore, fNIRS provides a reciprocal perspective for emotion research. Studies have demonstrated that fNIRS can be used to decode emotions and subsequently regulate an individual's emotional experience (Chao et al., 2021).

The application of fNIRS in monitoring attentional responses not only deepens our understanding of psychological processes but also provides a personalized mechanism for more complex psychological treatments.

2.3 多模态

Frontiers and Psychological Applications of Brain-Computer Interfaces

Brain-Computer Interfaces (BCI) utilize physiological signals, such as functional Near-Infrared Spectroscopy (fNIRS), to provide a more comprehensive foundation for psychological research. Rather than being a standalone application, the integration of these technologies expands the capacity to analyze complex psychological processes through mutual synergy. Recent studies (Li et al., 2022) suggest that this combination offers significant potential for advancing our understanding of human cognition. Furthermore, the integration of Artificial Intelligence (AI), particularly Transformer-based modeling, has enhanced performance in decoding emotional and cognitive states (Li et al., 2025).

The core of these advancements lies in signal processing and decoding. Using fNIRS data, researchers have achieved classification accuracies of $96.74\% \pm 1.96\%$ and $98.42\% \pm 1.52\%$ in specific tasks. While fNIRS provides an indirect measure of neural activity via hemodynamic responses, the application of advanced models—such as those proposed by Fossez—offers a robust framework for emotion decoding. These models provide more reliable tools and a stronger foundation for interpreting the neural correlates of psychological states.

In terms of psychological applications and interventions, fNIRS-based systems can capture emotional signals to develop sophisticated emotion classification systems, providing new tools for clinical practice. Research indicates that these applications can assist patients in regulating their emotions through biofeedback, thereby enhancing the efficacy of psychotherapy. In the realm of cognitive rehabilitation, machine learning-equipped devices can more accurately measure cognitive load, facilitating targeted neurorehabilitation and psychological interventions. This is particularly beneficial for patients with cognitive impairments, as it allows for the development of personalized emotional intervention programs that improve treatment outcomes and individual adaptability in clinical psychology.

The application of these technologies in psychology (Caiado & Ukolov, 2025; Li et al., 2025; Liang et al., 2022) offers a novel perspective for academic inquiry. By bridging the gap between neural signals and behavioral outcomes, these methods provide a more nuanced understanding of the human mind.

Exploration into areas such as emotion regulation and cognitive enhancement not only deepens our theoretical understanding of psychological processes but also establishes a solid empirical foundation for the use of BCI technology in cognitive augmentation and psychotherapy.

3 BCI

Applications of Technology in Cognitive Enhancement

Introduction

Cognitive enhancement refers to the use of various methods and technologies to improve human cognitive functions, including memory, attention, executive function, and creativity. In recent years, the rapid development of neuroscience, computer science, and engineering has transitioned cognitive enhancement from theoretical research into practical application. These technologies aim not only to assist individuals with cognitive impairments but also to optimize the mental performance of healthy individuals in demanding environments.

1. Pharmacological and Biological Interventions

The most traditional form of cognitive enhancement involves pharmacological agents, often referred to as "nootropics" or "smart drugs." These substances target neurotransmitter systems to modulate cognitive states. For example, stimulants such as methylphenidate and modafinil are frequently used to enhance alertness and sustained attention. Beyond pharmacology, nutritional interventions and the modulation of the gut-brain axis represent emerging biological frontiers for long-term cognitive health and performance optimization.

2. Non-Invasive Brain Stimulation (NIBS)

Non-invasive brain stimulation technologies have gained significant traction due to their ability to modulate neural activity without surgical intervention. Two primary modalities include:

• Transcranial Magnetic Stimulation (TMS): This technique uses magnetic fields to induce electrical currents in specific cortical regions. It is widely used for treating depression and is being researched for its potential to improve working memory and linguistic processing.
• Transcranial Direct Current Stimulation (tDCS): By applying a low-intensity constant current through electrodes on the scalp, tDCS can increase or decrease neuronal excitability. Studies suggest that tDCS can accelerate learning rates and enhance motor skill acquisition.

3. Neurofeedback and Brain-Computer Interfaces (BCI)

Brain-Computer Interfaces (BCI) represent a direct communication pathway between the brain and an external device. In the context of cognitive enhancement, neurofeedback is a common application where individuals learn to self-regulate their brain activity by visualizing real-time EEG or fMRI data.

[FIGURE:1]

As shown in [FIGURE:1], the closed-loop BCI system allows for precise monitoring of cognitive load. By providing instantaneous feedback, these systems can train users to maintain states of high focus or relaxation, effectively "tuning" the brain for specific tasks. Advanced BCI research is also exploring the use of implanted electrodes to restore cognitive functions in patients with neurological disorders.

4. Artificial Intelligence and Machine Learning

Machine learning plays a dual role in cognitive

3.1 BCI

Theoretical Foundations of Technology-Enabled Cognitive Enhancement

The application of technology to cognitive enhancement—specifically for improving attention—is a primary focus of contemporary research \cite{Kaimara et al., 2020; Chai et al., 2024}. By integrating neurofeedback mechanisms, these technologies directly target cognitive functions, establishing a robust framework for enhancement studies. The theoretical foundation for technology-driven cognitive enhancement rests upon principles of attention, memory, and neuroplasticity. In the field of cognitive psychology, the foundational model of attention proposed by Posner and Petersen \cite{Posner & Petersen, 1990} and the working memory model developed by Baddeley \cite{Baddeley, 2000} provide the necessary neurocognitive scaffolding for these interventions \cite{Tsai et al., 2025}. These models guide the development of targeted technological solutions \cite{Chang et al., 2022; Tsai et al., 2025}. Within the context of cognitive enhancement, neuroplasticity refers to the brain's ability to reorganize itself through neurofeedback; this process is further facilitated by neural encoding and decoding technologies that translate brain activity into actionable data.

By utilizing feature extraction, researchers can identify critical markers in functional Near-Infrared Spectroscopy (fNIRS) data, such as changes in oxygenated hemoglobin levels. These markers are then integrated into mathematical models \cite{Kim et al., 2025}. This process is closely linked to reinforcement learning: through real-time feedback, individuals learn to self-regulate their neural signals, a process analogous to long-term potentiation (LTP) \cite{Miao et al., 2020; Schultz, 2015}. Similar to applications in psychotherapy, neurofeedback for cognitive enhancement relies on the theoretical intersection of memory models and neuroplasticity. While attention models provide the framework for identifying relevant signals and functions, neuroplasticity serves as the underlying biological mechanism for change. By optimizing these functions, technology not only supports immediate performance but also fosters long-term cognitive resilience.

This framework provides a comprehensive basis for cognitive enhancement.

3.2 核心认知功能与

The mechanism of action refers to the specific biochemical interaction through which a drug substance produces its pharmacological effect. Understanding this mechanism is essential for drug discovery, optimizing therapeutic efficacy, and predicting potential side effects or drug-drug interactions.

1. Molecular Targets and Binding

Most drugs exert their effects by binding to specific molecular targets, which are typically proteins such as receptors, enzymes, ion channels, or transport proteins. The interaction between a ligand and its target is governed by structural complementarity and chemical affinity. For instance, in G protein-coupled receptor (GPCR) signaling, the binding of an agonist induces a conformational change in the receptor, which subsequently activates intracellular signaling cascades.

2. Signal Transduction Pathways

Once a drug binds to its target, it initiates a series of downstream molecular events known as signal transduction. These pathways often involve second messengers such as cyclic AMP (cAMP), calcium ions ($Ca^{2+}$), or phosphoinositides.
- Agonism: The drug mimics the endogenous ligand to activate the pathway.
- Antagonism: The drug binds to the receptor without activating it, thereby blocking the action of endogenous ligands.
- Allosteric Modulation: The drug binds to a site distinct from the active site, modifying the receptor's response to its natural ligand.

3. Enzyme Inhibition and Activation

Many therapeutic agents target enzymes to modulate metabolic or signaling processes.
- Competitive Inhibition: The drug competes with the substrate for the active site of the enzyme.
- Non-competitive Inhibition: The drug binds to an alternative site, reducing the enzyme's catalytic activity regardless of substrate concentration.
- Irreversible Inhibition: The drug forms a covalent bond with the enzyme, permanently disabling its function until new enzyme molecules are synthesized.

4. Cellular and Physiological Responses

The ultimate mechanism of action is reflected in changes at the cellular and systemic levels. This may include the regulation of gene expression, alterations in membrane potential, or the modulation of metabolic flux. For example, many anti-cancer drugs function by interfering with the cell cycle or inducing apoptosis in rapidly dividing cells.

5. Computational Modeling of Mechanisms

In modern pharmacology, machine learning and deep learning are increasingly used to predict mechanisms of action. By analyzing high-throughput screening data, transcriptomic profiles, and molecular docking simulations, researchers can identify how novel compounds interact with complex biological systems. These computational approaches allow for the identification of "off-target"

3.2.1 注意力的神经机制与

Selective attention refers to a selective cognitive process that involves both selective attention and distribution \cite{Carrasco2011}. Selective attention regulates cognition through spatial and temporal mechanisms \cite{Carrasco2011, HahnEtAl2006}. The decoding of neural signals provides a powerful tool for enhancing these cognitive processes. Regarding selective attention, research has shown that individuals can enhance their attentional focus through specific mechanisms. This form of neurofeedback, as noted by \cite{Karran2019}, utilizes fNIRS to enable individuals to modulate their cognitive states.

According to \cite{Kojima2020}, individuals can improve their performance through selective attention. Furthermore, \cite{InanAci2020} and \cite{Mishchenko2020} demonstrate that individuals can achieve high accuracy (up to 91.72%) in complex tasks (such as mathematics) through signal decoding and neural plasticity. These advancements have significantly improved selective attention and distribution. Research indicates that these methods not only enhance the understanding of attentional mechanisms but also provide practical foundations for cognitive enhancement in personalized applications, such as multi-tasking scenarios.

3.2.2 记忆力的神经机制与

Baddeley (2012) suggests that neural mechanisms rely on the connectivity of specific structures (Abubaker et al., 2024; Chandra et al., 2025). Axmacher et al. (2010) demonstrated that these mechanisms are critical for memory encoding, while subsequent research (2015) focused on stroke patients, showing that enhancing frontal activity is suitable for clinical implementation.

Sánchez and Beauchemin developed a memory-based learning experience. Regarding emotional contexts, Violante (2023) innovatively utilized Mohan & Jacobs' framework to propose a pathway for memory enhancement through personalized protocols. This approach facilitates personalized interventions by utilizing signal decoding (e.g., $\mathcal{F}$) and leveraging neural plasticity.

3.2.3 执行功能的神经机制与

Working memory updating (Miyake et al., 2000) is a core executive function, and its enhancement significantly improves performance in complex cognitive tasks. Recent research has demonstrated that targeted training can lead to substantial improvements in clinical populations, such as those with Attention Deficit/Hyperactivity Disorder (ADHD) (Enriquez-Geppert et al., 2017; Arns et al., 2014). Specifically, the Anterior Cingulate Cortex (ACC) plays a critical role in monitoring goals during task execution.

Utilizing functional Near-Infrared Spectroscopy (fNIRS) (Naseer & Hong, 2015), the latest studies have employed advanced neurofeedback techniques (Zhou & Chen, 2023). This neurofeedback approach, as discussed by Campos and colleagues, facilitates the enhancement of cognitive mechanisms through signal decoding (such as pattern optimization) and the promotion of neuroplasticity. By activating specific neural pathways, these methods provide a robust framework for cognitive intervention in complex scenarios.

Furthermore, these signals not only enhance the potential for functional intervention (Zhang, Chen, & Xu, 2011) but also pave the way for personalized cognitive enhancement strategies (Luo et al., 2024). This integration of real-time neural monitoring and feedback loops represents a significant advancement in the field of cognitive neuroscience and rehabilitation.

3.3 BCI

Applications of Technology in Cognitive Enhancement

Introduction

Cognitive enhancement refers to the use of various methods and technologies to improve human cognitive functions, including memory, attention, executive function, and creativity. In recent years, the rapid development of machine learning and deep learning has provided new tools and paradigms for cognitive enhancement, shifting the field from traditional pharmacological interventions toward sophisticated technological integrations.

1. Machine Learning and Brain-Computer Interfaces (BCI)

One of the most significant applications of technology in cognitive enhancement is the development of Brain-Computer Interfaces (BCIs). By utilizing machine learning algorithms to decode neural signals, BCIs can facilitate direct communication between the brain and external devices.

• Signal Processing: Deep learning models, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are employed to filter noise from EEG or fMRI data, allowing for the precise identification of cognitive states.
• Neurofeedback: Through real-time monitoring of brain activity, systems can provide feedback to users, enabling them to voluntarily regulate their neural patterns. This has shown promise in enhancing focus and reducing the symptoms of attention deficit disorders.

2. Artificial Intelligence in Personalized Learning

Deep learning has revolutionized the way we approach skill acquisition and knowledge retention. By modeling the "forgetting curve" and individual learning patterns, AI-driven platforms can optimize the delivery of information.

• Adaptive Learning Systems: These systems use reinforcement learning to adjust the difficulty and type of content based on a user's performance, ensuring that the cognitive load remains within an optimal range for enhancement.
• Memory Augmentation: Algorithms can predict the optimal time for reviewing information, effectively "extending" human long-term memory through strategically timed prompts and repetitions.

3. Virtual Reality (VR) and Augmented Reality (AR)

VR and AR technologies provide immersive environments that can stimulate neuroplasticity. These technologies are increasingly used for cognitive rehabilitation and the enhancement of spatial reasoning.

• Cognitive Training Environments: VR allows for the creation of complex, controlled scenarios that challenge executive functions in ways that traditional 2D tasks cannot.
• Spatial Navigation: AR overlays can assist individuals in navigating complex environments, potentially offloading cognitive demands and allowing the brain to allocate resources to higher-level decision-making tasks.

4. Ethical and Social Considerations

While the potential for cognitive enhancement is vast, it brings significant ethical challenges. The "digital divide" may exacerbate social inequalities if

3.3.1 BCI

Clinical Applications and Neuroplasticity Mechanisms in Cognitive Enhancement

Neurofeedback technology utilizes real-time feedback to modulate neural mechanisms associated with attention and cognitive control. By leveraging the principles of neuroplasticity, these techniques enable individuals to regulate specific brain activity patterns, leading to significant improvements in attentional states \cite{Gruzelier2014}. In clinical settings, neurofeedback has demonstrated substantial efficacy in treating patients with various cognitive impairments. Through personalized adjustment protocols, it facilitates the reorganization of neural circuits, offering a non-invasive approach to cognitive rehabilitation \cite{Arns2014, Bagherzadeh2020, Beauchemin2024}.

Recent research by Berger suggests that integrating neurofeedback with other therapeutic modalities can provide individuals with enhanced capabilities for complex decision-making. This synergy allows for more robust cognitive gains than traditional methods alone. Furthermore, these technologies are not limited to basic cognitive functions; they are increasingly being applied to high-demand scenarios, such as improving performance in multi-tasking environments \cite{Borghini2017, Sarmiento2024}. By providing real-time data on neural states, neurofeedback helps individuals maintain optimal cognitive load and focus during strenuous mental activities.

The potential of neurofeedback extends beyond pure cognition into the realms of emotional regulation and psychological therapy. By targeting the neural substrates of affective processing, these interventions offer new avenues for treating mood disorders and enhancing psychological resilience. As the field progresses, the application of real-time neural feedback is becoming a cornerstone of cognitive enhancement, providing a bridge between basic neuroscience and practical clinical interventions for both healthy populations and those with neurological conditions.

3.3.2 BCI

Ethical issues applied to cognitive enhancement: The application of technology in the field of cognitive enhancement has improved attention; however, long-term use may lead to neural dependency (Gordon & Seth, 2024; Sun & Ye, 2023), which constitutes a core ethical concern. fNIRS...

Sun & Ye (2023) and others have noted that current devices (such as those integrated with Berger et al., 2022) may lead to significant ethical challenges. Specifically, if cognitive enhancement technologies are only accessible to certain groups, it could exacerbate cognitive inequality. Furthermore, long-term use of cognitive enhancement—potentially achieved through neurofeedback—may cause users to become overly reliant on these devices to improve their cognitive performance (Słaby, 2021). For instance, individuals who depend on such technology over the long term might experience a decline in their natural cognitive flexibility. To address this risk, researchers should integrate quantitative ethical frameworks with technological development. Based on device usage patterns, these frameworks can help identify and mitigate potential dependency issues.

4 BCI

The Application of Technology in Psychotherapy

Introduction

In recent years, the integration of technology into the field of mental health has transformed the landscape of psychological intervention. From the early adoption of teletherapy to the contemporary use of artificial intelligence (AI) and virtual reality (VR), technological advancements are addressing long-standing barriers to care, such as geographical distance, high costs, and the stigma associated with seeking treatment. This paper explores the multifaceted roles that technology plays in modern psychotherapy, examining its efficacy, challenges, and future potential.

Digital Interventions and Telehealth

The most widespread application of technology in psychotherapy is telehealth. By utilizing secure video conferencing and digital communication platforms, therapists can provide evidence-based treatments to individuals who previously lacked access due to physical disabilities or remote locations. Research indicates that for many conditions, such as depression and anxiety disorders, teletherapy is as effective as traditional in-person sessions. Furthermore, mobile health (mHealth) applications and internet-delivered Cognitive Behavioral Therapy (iCBT) provide patients with self-help tools and asynchronous support, allowing for continuous monitoring and intervention outside of the clinical setting.

Artificial Intelligence and Machine Learning

Machine learning (ML) and artificial intelligence are revolutionizing the diagnostic and personalized treatment aspects of psychotherapy. By analyzing large datasets, ML algorithms can identify patterns in speech, text, and physiological data that may indicate the onset of a mental health crisis or a specific disorder.

Natural Language Processing (NLP) is particularly significant in this domain. It enables the development of "chatbots" or conversational agents capable of providing immediate, low-level emotional support and psychoeducation. While these tools are not intended to replace human therapists, they serve as valuable adjuncts in triage and early intervention. Additionally, AI can assist clinicians by providing data-driven insights into a patient's progress, potentially predicting treatment outcomes and suggesting adjustments to therapeutic strategies.

Virtual Reality and Immersive Environments

Virtual Reality (VR) has emerged as a powerful tool for Exposure Therapy, particularly in treating Post-Traumatic Stress Disorder (PTSD) and specific phobias. By creating controlled, immersive environments, VR allows patients to confront their fears in a safe and gradual manner. This "Virtual Reality Exposure Therapy" (VRET) provides therapists with precise control over the intensity and duration of the stimulus, which is often difficult to achieve in real-world settings. Beyond exposure, VR is also being utilized for social skills training in individuals with autism spectrum disorders and for mindfulness-based stress

4.1 基于

Technological innovations in psychotherapy are revolutionizing the field through the real-time measurement and decoding of brain activity. By utilizing advanced neuroimaging techniques such as functional Near-Infrared Spectroscopy (fNIRS), technology now provides a robust foundation for psychological interventions. Traditional psychotherapy, such as Cognitive Behavioral Therapy (CBT), often relies heavily on patient self-reporting, which can be subjective \cite{Kazdin, 2021}. In contrast, modern approaches provide direct neurofeedback, enabling the design and adjustment of personalized treatment protocols. By identifying specific neural patterns, these systems offer patients individualized neurofeedback to facilitate emotional regulation \cite{Leuchter et al., 2017}. This real-time capability makes the therapeutic process more transparent; by enhancing the patient's treatment experience through neurofeedback, individuals can more actively engage with the intervention process, thereby improving overall therapeutic outcomes \cite{Linden et al., 2012}. Furthermore, Restoy (2024) suggests that technology-assisted neurofeedback significantly improves patient adherence to treatment regimens.

This model not only strengthens patient autonomy but also promotes long-term psychological well-being through mechanisms of neuroplasticity. Rather than replacing traditional psychotherapy, these advancements augment the clinical process through real-time data analysis and personalized interventions.

The integration of neurofeedback has significantly enhanced therapeutic efficacy. This synergy provides a sophisticated framework for addressing complex psychological conditions and opens new pathways for targeted clinical interventions.

4.2 BCI

Neurotheoretical Foundations of Technology in Psychotherapy

The application of technology in psychotherapy is grounded in the neurobiological principles of neuroplasticity and neurofeedback mechanisms, providing a robust framework for clinical intervention. Within the context of psychotherapy, neuroplasticity refers to the brain's inherent ability to reorganize its structure, functions, or connections through experience and targeted training \cite{Pascual Leone et al., 2005}. This capacity for change is particularly relevant in treating conditions such as anxiety disorders, where neural pathways may become maladaptive.

Research indicates that technology-assisted interventions facilitate recovery by providing real-time neurofeedback, which helps individuals achieve self-regulation. By utilizing visual or auditory cues to represent brain activity patterns, these systems allow patients to consciously modify their neural states. This process enhances functional connectivity within the brain, particularly in areas associated with emotional regulation. For instance, when treating anxiety or depression, technology-driven interventions can strengthen the prefrontal cortex's inhibitory control over the amygdala, thereby improving emotional stability \cite{Drigas & Sideraki, 2024}.

The efficacy of neurofeedback-based interventions relies heavily on the brain's neuroplastic response to consistent training. As noted by \cite{Brannigan (2024)}, technology-assisted neurofeedback significantly bolsters an individual's emotional regulation capabilities. By providing immediate data on physiological and neurological states, these feedback mechanisms work in tandem with neuroplasticity to reinforce healthier cognitive and emotional patterns.

Current applications of these technologies primarily focus on psychological interventions rooted in established neural mechanisms, specifically those aligned with emotion regulation strategies \cite{Leuchter et al., 2017, Kamboj et al., 2024}. Through the integration of neurofeedback technology, clinicians can monitor and influence specific neural circuits, paving the way for highly personalized and precise therapeutic approaches.

Enriquez-Geppert et al. (2017) demonstrate that the integration of neurofeedback techniques can further enhance therapeutic outcomes. By simulating creative processes, these methods assist patients in achieving cognitive shifts. As noted by van 't Wout-Frank et al. (2019), such technologies are grounded in the mechanisms of neuroplasticity and neurofeedback, offering new dimensions for psychological treatment. The core strength of this approach lies in its ability to utilize real-time feedback to regulate emotional and cognitive functions, thereby indirectly improving patient well-being. Furthermore, Putze et al. (2020) highlight the growing utility of these technologies within the broader field of psychotherapy.

4.3 BCI

The Application of Technology in Psychotherapy

Introduction

In recent years, the rapid advancement of information technology has profoundly transformed the landscape of mental health services. The integration of technology into psychotherapy—ranging from teletherapy and mobile health (mHealth) applications to advanced artificial intelligence (AI) and virtual reality (VR)—has addressed long-standing barriers such as geographical distance, high costs, and the social stigma associated with seeking help. This paper explores the current state, efficacy, and ethical considerations of technological applications in the field of psychological intervention.

Digital Interventions and Teletherapy

Teletherapy, facilitated by high-speed internet and secure video conferencing, has become a cornerstone of modern clinical practice. Research indicates that synchronous video-based therapy is often as effective as traditional face-to-face sessions for a variety of conditions, including depression and anxiety disorders \cite{1}. Furthermore, asynchronous digital interventions, such as computerized Cognitive Behavioral Therapy (cCBT), allow patients to engage with therapeutic materials at their own pace. These platforms often utilize structured modules that guide users through cognitive restructuring and behavioral activation, providing a scalable solution for public health systems.

Artificial Intelligence and Machine Learning

Machine learning (ML) and deep learning algorithms are increasingly utilized to enhance diagnostic accuracy and personalize treatment plans. By analyzing large datasets—including speech patterns, facial expressions, and text-based communication—AI systems can identify subtle biomarkers of mental distress that may be overlooked by human clinicians.

For instance, natural language processing (NLP) is employed to monitor therapeutic alliance and predict patient outcomes by analyzing the linguistic dynamics of therapy transcripts. Additionally, AI-driven chatbots provide immediate, low-threshold support for individuals in crisis, offering basic psychoeducation and coping strategies. While these tools do not replace professional clinicians, they serve as vital adjuncts in the "stepped-care" model of mental health service delivery.

Virtual Reality and Immersive Environments

Virtual Reality (VR) has demonstrated significant efficacy, particularly in the treatment of Post-Traumatic Stress Disorder (PTSD) and specific phobias. Through Virtual Reality Exposure Therapy (VRET), clinicians can create controlled, immersive environments that allow patients to confront their fears in a safe and gradual manner.

[FIGURE:1]

As shown in [FIGURE:1], the immersive nature of VR facilitates emotional engagement while maintaining a high degree of environmental control. Beyond exposure therapy, VR is also being explored for social skills training in individuals with autism spectrum disorder and for mindfulness-based stress reduction,

4.3.1 抑郁症治疗中的应用

The neural mechanisms underlying the regulation of patient emotions remain impaired in certain individuals, a condition closely linked to deficits in emotional regulation capacity \cite{Leuchter et al., 2017}. Neurofeedback has emerged as a vital intervention to assist patients in modulating these states \cite{Young et al., 2014}. Research indicates that neurofeedback plays a significant role in clinical treatment by enhancing a patient's capacity for self-regulation \cite{Dobbins et al., 2023}.

Furthermore, the integration of neurofeedback with Transcranial Magnetic Stimulation (TMS) has been shown to further improve therapeutic outcomes \cite{Choi et al., 2024}. This feedback mechanism allows patients to directly perceive changes in their own brain activity, thereby strengthening the overall efficacy of the treatment \cite{Restoy et al., 2024}.

4.3.2 焦虑症治疗中的应用

Neurofeedback regulates the autonomic nervous system, potentially by modulating the activity of the prefrontal cortex \cite{Schoenberg & David, 2014; Thibault et al., 2016}. This mechanism is consistent with findings by \cite{Hu et al., 2022}, which suggest that through neurofeedback, patients can regulate prefrontal circuits, thereby indirectly influencing broader neural pathways \cite{Keynan et al., 2016; Nicholson et al., 2017}. Significant reductions in symptoms have been observed in studies by \cite{Marzbani et al., 2021}, providing a foundation for personalized interventions.

As anxiety severity fluctuates, the integration of simulation technologies offers an innovative approach to treatment \cite{Liu et al., 2025}. By exposing patients to controlled virtual environments, these systems provide real-time feedback to help patients manage their physiological and psychological responses \cite{Maples-Keller et al., 2017}.

4.3.3 PTSD

Applications in treatment primarily focus on intervening in traumatic memory pathways and emotional regulation to assist patients in managing their responses. Research indicates that such interventions can simulate traumatic experiences in a controlled manner.

To address the emotional states of patients, van Loenen et al. (2022) suggest that patients can engage in virtual actions or other therapeutic activities (Smashna, 2023). This facilitates a recoding process, as noted by van der Heijden et al. (2024), the efficacy of which lies in the implementation of real-time neurofeedback and highly personalized interventions.

These methods have been shown to enhance the emotional regulation capabilities of patients. Furthermore, they promote long-term cognitive recovery through mechanisms of neuroplasticity (Putze et al., 2020).

4.3.4 BCI

Ethical Issues in the Application of Technology to Psychotherapy

The integration of technology into psychotherapy introduces significant potential ethical risks, a primary concern being the development of patient dependency on technological devices. While techniques such as neurofeedback and real-time monitoring can effectively improve clinical outcomes, they may also inadvertently lead patients to neglect traditional psychological interventions, such as Cognitive Behavioral Therapy (CBT) \cite{Slaby2021}. For instance, the long-term use of automated therapeutic tools may diminish an individual's perceived self-efficacy in autonomously managing symptoms of anxiety or depression. This shift can foster a state of psychological dependency, potentially rendering the patient less capable of emotional self-regulation without technological assistance \cite{Yue2023}.

To mitigate these risks, it is essential to design interventions that prioritize and enhance the patient's confidence and capacity for self-regulation. Implementing systematic assessments of individual dependency risks can help clinicians quantify and predict potential issues before they manifest. Future research should focus on developing hybrid intervention models that balance technological assistance with the cultivation of psychological autonomy. Such approaches aim to enhance therapeutic efficacy while simultaneously safeguarding the patient's long-term psychological resilience and independence.

5 总结与展望

Technology holds significant psychological importance in the fields of cognitive enhancement and psychotherapy. In terms of cognitive enhancement, it enables the real-time acquisition and feedback of brain signals to effectively improve an individual's attentional capacity, providing clinical applicability for patients with attention deficits in complex cognitive tasks. In the field of psychotherapy, the modulation of key brain regions has been shown to significantly improve symptoms of depression. When combined with other modalities, this provides new avenues for mental health interventions. Furthermore, personalized neurofeedback and enhancement techniques increase patient treatment compliance. From a psychological perspective, the value of these technologies lies not only in the technical application itself but also in the profound understanding of neuropsychological mechanisms. However, the field faces challenges during application, including technological dependence and ethical considerations. Future research should address these issues.

By inducing neuroplasticity to reshape cognitive and emotional pathways, researchers can explore the long-term effects of technological dependence on autonomy and emotional regulation. Furthermore, future studies should utilize psychological theories—such as cognitive load theory and emotional regulation theory—to guide the development and implementation of cutting-edge technologies.

5.1 优化

Technical applications in the fields of cognitive enhancement and psychotherapy primarily focus on advancing emotional regulation and optimizing therapeutic outcomes. This is largely reflected in device design and the mitigation of physical discomfort and psychological strain. Such experiences not only influence the immediate user experience but also determine the long-term viability of these technologies. By integrating Cognitive Load Theory \cite{Sweller, 1988} and Emotion Regulation Theory \cite{Gross}, and combining ergonomic engineering with affective computing, researchers can improve the psychological adaptability and subjective engagement of individuals, which otherwise rely on complex and high-volume signal acquisition.

Devices intended for long-term use must account for the user's physical constraints. Research indicates that ergonomic optimization is essential for enhancing the quality of technical applications; specifically, the application of quantitative design and advanced communication technologies can alleviate these issues \cite{Chiappalone et al., 2022}. Adaptability issues are closely linked to individual psychological states; for instance, users may experience discomfort that hinders engagement \cite{Biasiucci et al., 2018}. Theoretical frameworks suggest that physical discomfort degrades the overall experience, causing individuals to perceive a psychological distance from the technology. According to Self-Determination Theory \cite{Ryan & Deci, 2017}, this conflicts with the need for autonomy. To resolve this, ergonomic design should prioritize flexibility, such as employing adjustments that minimize physiological distress \cite{Park et al., 2020}, thereby improving the emotional quality of technical interactions. Current interaction designs often focus on functionality but frequently overlook personalized psychological needs. Psychological research emphasizes that humans seek intuitive connections during technological interactions \cite{Norman, 2013}. From the perspective of emotion regulation theory, technical interfaces should dynamically adapt to the individual's emotional state.

Research in this field has identified various regulatory mechanisms \cite{Marzbani et al., 2016} that optimize the interaction experience. In the context of cognitive enhancement and psychotherapy, integrating psychological theories with interaction design allows for a more comprehensive understanding of physiological and psychological demands, thereby increasing the potential for technology acceptance. Future design strategies should further evolve toward "cognitive technology," providing individuals with more natural and seamless integration. This represents a profound shift in the application of these technologies within the psychological domain.

5.2 应对

The ethical dilemma of technology in cognitive enhancement and psychotherapy revolves around individual autonomy and technical agency. By directly interfacing with and intervening in brain signals, technology has the potential to enhance cognitive functions, yet it simultaneously poses a risk to the foundational autonomy of the individual.

Utilizing technology as a means of direct intervention (Bagherzadeh et al., 2020) may undermine an individual's ethical subjectivity. When technological systems modulate a patient's emotional and cognitive characteristics, any improper application can erode personal autonomy. This shift necessitates a rigorous ethical discussion regarding the preservation of the self in the face of neuro-technological influence.

In clinical treatment settings, the perception that one's decisions are truly their own is vital (Unterrainer et al., 2015); such an experience is fundamental to an individual’s psychological health and ethical subjectivity. It is essential to fully understand the ethical boundaries of technological interventions, such as those used for attention enhancement, and to balance technical efficacy with individual autonomy. To construct a psychological framework for these technologies, one must consider how neural signals—which reflect an individual's emotions and internal states (Yuste et al., 2017)—are collected and utilized. Psychology can contribute to this ethical construction by defining the boundaries of digital intervention.

Psychological frameworks can enhance an individual's informed consent, while psychological metrics provide a standardized way to assess the impact of these interventions. From a psychological perspective, developing mechanisms to address these dilemmas emphasizes that technological interventions should not only be technically sound but should also empower the individual by strengthening their internal agency.

5.3 TDRI

Establishment and Prevention of Technological Dependence Risks

The application of technology in cognitive enhancement and psychotherapy carries potential risks of technological dependence, which may adversely affect an individual's autonomy and emotional well-being \cite{Norman et al., 2023}. From a psychological perspective, this dependence is not merely reflected in the use of devices for memory enhancement; it may also diminish an individual's reliance on natural memory processes \cite{Kwon, 2022}. Such reliance can potentially undermine natural cognitive and emotional regulation mechanisms. To address this risk, this study proposes the Technological Dependence Risk Index (TDRI). By integrating psychological adaptability, autonomy, and other key metrics, the TDRI aims to construct a framework to evaluate the impact of technological dependence on individual psychological and behavioral patterns, thereby providing a theoretical basis for more ethical and sustainable technology design.

2.1 Theoretical Foundation and Dimensional Design of the TDRI

The development of the TDRI is grounded in the need to quantify how deeply integrated technologies interfere with or substitute for innate human capacities. The index is designed across several critical dimensions to capture the multifaceted nature of dependence. These dimensions include:

• Psychological Adaptability: Assessing the extent to which an individual can maintain mental stability and functional performance when the technology is unavailable.
• Autonomy and Agency: Measuring the degree to which an individual retains the capacity for independent decision-making and self-regulation without algorithmic intervention.
• Cognitive Substitution: Evaluating the shift from internal cognitive processing (such as natural memory or problem-solving) to externalized technological reliance.
• Emotional Regulation: Analyzing whether the technology serves as a necessary crutch for emotional stability, potentially weakening the user's natural resilience.

By synthesizing these dimensions, the TDRI provides a standardized metric to identify high-risk scenarios in the deployment of cognitive enhancement and therapeutic tools. This framework facilitates a proactive approach to technology design, ensuring that innovations support rather than supplant human autonomy.

1. 心理适应性

Psychological Adaptability

Psychological Adaptability (PA) refers to an individual's natural regulatory mechanism (Gross, 2015) that enables them to maintain psychological equilibrium. Grounded in regulatory theory, cognitive theory, and neuroplasticity theory, PA can be quantified through specific behavioral manifestations—such as attentional recovery—and analyzed through the long-term impact of various technological interventions on psychological adjustment.

2. 设备使用

Usage Frequency and Duration

The use of digital devices may lead to specific behavioral patterns. Research indicates that the use of technological tools is closely linked to an individual's psychological dependence \cite{Kwon, 2022}. This phenomenon refers to an individual's behavioral reliance on technology. According to the frameworks established by \cite{Griffiths, 1999} and the foundational theories of \cite{Miller, 1956}, quantifying the duration and frequency of use provides a critical empirical basis for assessing an individual's level of dependence and potential psychological risks.

3. 自主性与

Autonomy and Control Perception

The autonomy of the individual and the perception of control are central themes in psychological research \cite{Ryan & Deci, 2017}. According to Locus of Control theory \cite{Rotter, 1966} and frameworks regarding the transfer of learning and dependency \cite{Barnett & Ceci, 2002}, the interaction between humans and technology significantly shapes behavioral patterns. When individuals develop a heavy reliance on technology, the constant use of external devices may lead to a shift in the perceived locus of causality. Specifically, an individual's dependence on external technological tools can diminish their sense of personal autonomy and internal control. Experimental studies, such as behavioral tasks measuring decision-making agency, have been employed to investigate the profound impact of technology on an individual's psychological autonomy.

4. 停

(Post-BCI recovery ability: Whether an individual can utilize technological dependency to regulate their natural recovery mechanisms, as discussed by Norman et al. (2023) and Eysenck (1991) regarding memory and emotion, serves to quantify the degree of technological dependency. 2.2 Comprehensive Model of TDRI: This study proposes a dependency risk model to assess long-term individual adaptability. Researchers are increasingly focusing on psychological adaptability; specifically, higher technological dependency risk correlates with a greater suppression of natural regulatory functions by technological interventions. By analyzing the psychological and cognitive impacts of device usage, we can reflect the influence of technology on psychological resilience.

By integrating these factors, we provide a framework for ethical construction and risk assessment. Researchers can analyze technological dependency risks from the perspective of individual psychology, thereby establishing a foundation based on adaptability. In future research, experimental validation can be employed to further enhance the scientific rigor and practical applicability of this model.)

5.4 BCI

The future application of collaborative frontier technologies involves technical explorations in areas such as cognitive enhancement and psychotherapy. The integration of these technologies within complex environments and personalized interventions provides innovative approaches to addressing fundamental challenges in the field.

Decoding complex brain activity (Lv et al., 2021) through neurofeedback, when combined with emerging technical frameworks, can provide entirely new applications for personalized psychological interventions (Riva et al., 2019).

5.4.1 人工智能与

The synergistic application of technology and psychological research has evolved into a sophisticated framework comprising interfaces, interactions, and intelligence. By integrating human cognitive signals—such as intentions—this framework provides a theoretical foundation for optimizing psychological interventions. The collection and analysis of neurosignals enable the monitoring of an individual's emotional and attentional states. Deep learning provides more powerful tools for processing these complex neural signals \cite{Wang et al., 2023}. For example, deep-learning-based brain signal decoding models can identify an individual's emotional state, which is critical for the implementation of psychological interventions. The application of these devices in mental health interventions has already demonstrated efficacy in areas such as emotion regulation \cite{Bajaj & Sinha, 2022}. Furthermore, the adaptive learning characteristics of these systems enhance the flexibility of the equipment \cite{Lv et al., 2021}. In the context of psychotherapy, aligning interventions with an individual's current cognitive state not only improves therapeutic outcomes but also marks a paradigm shift in psychotherapy from a "one-size-fits-all" approach to an adaptive model. From a psychological perspective, the Self-Determination Theory \cite{Ryan & Deci, 2017} emphasizes that autonomy and agency help enhance patient engagement while mitigating the risks associated with technological dependence.

Through these technologies, future psychological intervention programs will be able to provide highly customized, tailor-made solutions for individuals.

5.4.2 虚拟现实与

The synergistic application of these technologies provides new tools for psychological research and intervention, facilitating the simulation of complex psychological processes and offering novel methods to characterize psychological activities (Lee et al., 2013).

The integration of technologies for monitoring patient emotional states has opened new frontiers in the field (Eshuis et al., 2021). By designing neurofeedback mechanisms, individuals can transcend the limitations of traditional experimental designs within virtual environments, providing greater scope for personalized cognitive interventions (Allain et al., 2014; Arciero et al., 2020; Vallejo et al., 2017). Further extending the application of neurofeedback can assist individuals in virtual settings to enhance memory and optimize cognitive performance. Through these virtual cognitive experimental models, psychological research can move beyond rigid experimental designs toward more flexible and personalized protocols. This shift has a profound impact on both psychological theory and practice. From cognitive enhancement to emotional regulation, these advancements not only improve attentional control but also provide novel therapeutic pathways, promoting the personalization and contextualization of mental health care. However, the implementation of this technology raises complex ethical and technical dependency issues. Future development must balance technological innovation with psychological theory, focusing on experience design while mitigating the risks of dependency. By prioritizing individual psychological agency, this field is poised to open new avenues for theoretical deepening and practical application in psychology.

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From Mind Reading to Mind Modulation: Applications and Mechanisms of Neural Modulation in Brain Computer Interfaces from a Psychological Perspective CHEN Zhaojie, WANG Guofan (School of Sociology, China University of Political Science and Law, Beijing, China)

Abstract

A Brain Computer Interface (BCI) establishes a direct communication channel between the brain and external devices by acquiring, decoding, and translating neural signals, opening novel pathways to understand and enhance cognitive potential. This study expl ores the theoretical foundations and clinical applications of BCI in cognitive enhancement and psychotherapy. This paper introduce s an ethical framework for analyzing psychological adaptability to long term BCI use and proposes the 'Technological Dependence Risk Index' (TDRI) to quantify the impact of such reliance on individual autonomy. Furthermore, the integration of BCI with cutting technologies like artificial intelligence (AI) and virtual reality (VR) offers innovative pathways for therapeutic interventions targeting complex psychological processes. Future research should prioritize enhancing the centered experience BCI applications and further investigate the term impacts of technological dependence on psychological autonomy and emotional regulation. Moreover, applying principles from cognitive psychology (e.g., attention, memory) and neuroplasticity is crucial for optimizing BCI decoding features and neurofeedback design, ultimately creating more adaptable and personalized psychological intervention paradigms.

Keywords

Brain Computer Interface, Cognitive Enhancement, Psychotherapy, Neural Plasticity, Neural Decoding, Technological Dependence Risk Index