Exploring the EEG representation of English listening comprehension under hypoxic conditions

Introduction: Understanding the impact of hypoxic conditions on cognitive functions, including English listening comprehension, has garnered increasing attention due to its implications for high-altitude education and cognitive resilience. Traditional research in this domain has often relied on behavioral assessments or simple physiological metrics, which lack the granularity to capture the neural underpinnings of cognitive performance.

Methods: This study proposes a novel framework combining electroencephalography (EEG)-based neural decoding with the Dynamic Linguistic Enhancement Model (DLEM) to investigate English listening comprehension in hypoxic environments. DLEM integrates adaptive vocabulary acquisition, grammar contextualization, and cultural embedding, leveraging EEG to provide real-time, personalized insights into linguistic processing.

Results: The experimental results demonstrate significant improvements in comprehension accuracy and cognitive load management, particularly under adaptive curriculum strategies outlined by the Contextual Augmented Learning Strategy (CALS).

Discussion: By bridging physiological responses with advanced educational methodologies, this work contributes a scalable and flexible approach to enhancing cognitive performance under hypoxia, aligning with the goals of understanding both physiological and pathological responses to high-altitude conditions.

1 Introduction

Understanding how hypoxic conditions affect cognitive processes, such as English listening comprehension, is crucial due to its implications in environments like aviation, deep-sea diving, and medical conditions (Agung and Surtikanti, 2020). This research area not only advances theoretical insights into neural mechanisms but also contributes to developing adaptive systems for individuals working under such conditions (Liang, 2021). Electroencephalography (EEG) has emerged as a vital tool for studying real-time brain activity during cognitive tasks (Yao and Ma, 2021). By examining EEG patterns, researchers can identify the specific neural correlates and disruptions caused by hypoxia (Hu and Yao, 2021). Such investigations provide opportunities for designing mitigation strategies, enhancing cognitive resilience in hypoxic scenarios, and improving human performance in extreme environments. This review outlines the evolution of methods in this field, highlighting limitations and opportunities across three major methodological phases (Kashinathan and Aziz, 2021).

Early studies of EEG in cognitive tasks under hypoxic conditions relied heavily on traditional symbolic AI and knowledge-based approaches to model human cognition (Zheng et al., 2021). These methods focused on understanding predefined patterns and relationships, often using rule-based systems to interpret EEG data (Chen, 2021) Knowledge representation frameworks were employed to classify brainwave patterns associated with various cognitive states, including attentional focus and memory retention (Lee and Hwang, 2022). While these approaches laid foundational insights into neural mechanisms, they were constrained by the rigidity of predefined rules and limited capacity to account for the dynamic nature of cognitive processes under stressors like hypoxia (Sun et al., 2020). Moreover, manual curation of EEG features was labor-intensive and failed to capture subtle temporal variations, reducing their practical applicability to real-world scenarios (Richards and Pun, 2021). The emergence of data-driven and machine learning techniques addressed many limitations of symbolic methods by enabling automated feature extraction and adaptive modeling (Sihn and Kim, 2022). Researchers began employing classifiers such as support vector machines (SVM) and random forests to distinguish EEG patterns corresponding to varying degrees of hypoxia (Hendriks-Balk et al., 2020). Machine learning models facilitated the identification of nuanced EEG biomarkers of cognitive degradation, offering greater flexibility and scalability (Karlen-Amarante et al., 2024). However, these methods were often dependent on extensive labeled datasets and were sensitive to noise inherent in EEG recordings (Iturriaga et al., 2023). Furthermore, the lack of interpretability in these models posed challenges in understanding the underlying neurophysiological processes and tailoring interventions (Iturriaga and Castillo-Galán, 2022).

In recent years, deep learning and pre-trained models have revolutionized EEG analysis in hypoxic cognitive research. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been utilized to capture spatiotemporal dynamics of EEG signals, while transformer-based models leverage self-attention mechanisms for contextual encoding of brain activity. These methods outperform traditional machine learning models in accuracy and robustness, especially in handling complex, high-dimensional EEG data. Pre-trained models fine-tuned on task-specific datasets further enhance transfer learning, enabling cross-population studies. Despite their promise, these approaches often require significant computational resources and large-scale datasets, which may not always be feasible in hypoxic research. The opacity of deep models raises concerns about the interpretability and generalizability of findings. To overcome these limitations, we propose a novel approach that integrates the interpretability of symbolic methods, the adaptability of machine learning, and the sophistication of deep learning models. By leveraging a hybrid architecture, our method aims to provide high accuracy in EEG analysis while maintaining computational efficiency and neurophysiological relevance. This approach aligns with the need for robust, scalable, and interpretable solutions in understanding English listening comprehension under hypoxic conditions.

We summarize our contributions as follows:

• Introduces a hybrid architecture combining symbolic reasoning with deep learning for enhanced interpretability and accuracy.

• Designed for multi-scenario adaptability, including varying hypoxic levels, ensuring high efficiency and generalizability.

• Demonstrates superior performance in decoding EEG representations, with statistically significant improvements in accuracy and robustness.

2 Related work

2.1 EEG analysis in language comprehension

The study of electroencephalography (EEG) has become a cornerstone in understanding cognitive processes, including language comprehension (Ariastuti and Wahyudin, 2022). EEG allows researchers to measure brain activity with high temporal resolution, enabling the examination of neural responses to linguistic stimuli (Wu et al., 2022). A significant body of research focuses on the temporal dynamics of event-related potentials (ERPs) during language processing tasks (Zou et al., 2021). For example, the N400 component is widely studied for its role in semantic processing, revealing insights into how the brain resolves meaning inconsistencies (Coleman, 2021). Similarly, the P600 component has been linked to syntactic processing and reanalysis during sentence comprehension (Aoyama, 2021). These findings underscore the importance of EEG in mapping the temporal stages of language comprehension (Elliott and Hodgson, 2021). Furthermore, frequency-based EEG analyses, such as alpha and theta power modulations, have been explored to understand attentional and memory mechanisms during language tasks (Zhao et al., 2020). While these studies provide a robust foundation, the impact of external factors, such as environmental stressors or altered physiological conditions like hypoxia, remains less understood (Simamora and Oktaviani, 2020). Examining how hypoxia modulates these EEG markers could reveal how adverse conditions affect language processing (Iturriaga, 2023).

2.2 Cognitive impairment under hypoxia

Hypoxia, characterized by reduced oxygen availability, has profound effects on brain function, including cognitive and linguistic abilities (Bae and Park, 2020). Existing research highlights how hypoxia impacts attention, memory, and executive function (Yunita and Maisarah, 2020). Studies employing neuroimaging and behavioral assessments have demonstrated significant cognitive deficits under acute and chronic hypoxic conditions (Septiyanti et al., 2020). These include slower reaction times, decreased working memory capacity, and impaired decision-making. Despite these findings, there is a notable gap in the literature concerning hypoxia's effects on specific cognitive domains such as language comprehension (Seo, 2020). Investigating this relationship is essential, as language comprehension relies on the integration of multiple cognitive resources, including attention and working memory. Hypoxia-induced changes in brain physiology, such as reduced cerebral oxygenation and altered neurotransmitter dynamics, may disrupt these processes (Rusmiyanto et al., 2023). EEG studies could provide valuable insights by identifying how hypoxia modulates neural correlates of language comprehension, such as ERP components and oscillatory activity patterns (Alfallaj et al., 2021).

2.3 Multimodal interaction of stressors

The interaction of hypoxia with other stressors, such as cognitive load or emotional stress, presents a complex challenge to understanding brain function (Sallam, 2023). Multimodal studies investigating combined stress effects are relatively sparse, yet crucial for understanding real-world scenarios (Zein et al., 2020). Cognitive tasks, such as language comprehension, often occur under conditions involving multiple concurrent demands. The interplay between hypoxia and additional stressors may exacerbate neural inefficiencies, leading to amplified cognitive deficits (Shaikh et al., 2023). Research utilizing EEG has shown that stressors such as mental fatigue or anxiety can modulate brainwave patterns, particularly in the alpha and beta frequency bands (Renganathan, 2021). The integration of EEG with other physiological measures, such as heart rate variability or blood oxygen saturation, could provide a holistic view of how hypoxia interacts with stress (Syakur et al., 2020). Moreover, advanced analytical techniques, such as machine learning models, could be employed to decode complex neural patterns arising from multimodal stress conditions (Sofyan, 2021). This direction not only addresses theoretical questions but also has practical implications for environments where individuals face simultaneous cognitive and physiological challenges.

3 Method

3.1 Overview

In recent years, English education has become a critical area of focus due to its global significance in academic, professional, and social contexts. This subsection provides an overview of the methodology employed to enhance English learning outcomes, particularly in environments where English is taught as a second language (ESL). We aim to tackle challenges in comprehension, expression, and fluency through the integration of novel pedagogical strategies, leveraging technological advancements, and understanding linguistic nuances.

The upcoming subsections will address various facets of our approach. In Section 3.2, we formalize the problem of English education by analyzing common linguistic barriers and presenting a structured framework to model them. This foundational section establishes the key challenges in vocabulary acquisition, grammar comprehension, and cultural fluency, emphasizing their interconnected nature. In Section 3.3, we introduce a novel framework, hereafter referred to as the Dynamic Linguistic Enhancement Model (DLEM). This model builds upon insights from cognitive science and language processing to deliver adaptive and personalized learning pathways. Key components of DLEM include contextualized learning environments and multi-modal interactions, which are meticulously designed to simulate real-world communication. In Section 3.4, we propose an innovative strategy, termed the Contextual Augmented Learning Strategy (CALS), to integrate our model effectively into diverse educational settings. This strategy focuses on adaptive curriculum design, dynamic feedback systems, and the utilization of gamification to foster learner engagement and motivation. The emphasis is on scalability and flexibility, ensuring applicability across varied cultural and institutional contexts.

3.2 Preliminaries

English education, particularly in environments where it is taught as a second language, presents unique challenges that require careful analysis and systematic formalization. To address these challenges, we introduce a mathematical and conceptual framework that captures the complexities of language acquisition, comprehension, and usage. The English learning process can be represented as a multi-stage system:

$\begin{array}{ll}S=\left\{V,G,C\right\}.& (1)\end{array}$

In this framework, $V$ represents the domain of vocabulary acquisition, encompassing the process of learning and retaining new words. $G$ denotes the grammatical structures of the language, including syntax and morphology, which govern sentence construction. $C$ embodies the cultural and contextual understanding that is essential for meaningful and effective language use. These components interact dynamically within the cognitive capabilities of the learner and the environmental influences they encounter, creating a complex and interdependent system. This framework provides a structured approach to understanding and addressing the multifaceted nature of English language learning. The vocabulary learning process can be modeled as a stochastic process, where the probability of acquiring a word w_i at time t is dependent on exposure E(w_i, t) and reinforcement R(w_i, t). Formally:

$\begin{array}{ll}P(w_i|t)=f(E(w_i,t),R(w_i,t)),& (2)\end{array}$

where f is a monotonic function that combines exposure and reinforcement effects. Reinforcement often depends on the frequency and utility of w_i in specific contexts:

$\begin{array}{ll}R(w_i,t)=\alpha ·\text{freq}(w_i)+\beta ·\text{utility}(w_i),& (3)\end{array}$

with α and β as tunable parameters representing learner-specific sensitivity. Grammar is structured around a set of syntactic rules $R$ = {r₁, r₂, …, r_n}, where r_i defines transformations or associations between linguistic constructs. The learner's ability to internalize $R$ is influenced by cognitive factors such as memory and reasoning capabilities. Define $P$(r_i, t) as the probability of mastering rule r_i over time:

$\begin{array}{ll}P(r_i,t)={\displaystyle {\int }_0^t}\text{engage}(r_i,\tau )·\text{feedback}(r_i,\tau )d\tau ,& (4)\end{array}$

where engage(r_i, τ) represents active interaction with r_i, and feedback(r_i, τ) captures corrective signals received during learning. The contextual use of English involves the integration of vocabulary and grammar with socio-cultural norms. We model this integration using a latent semantic space $L$, where each word or phrase w_i maps to a point ${\text{v}}_i\in {ℝ}^d$. Contextual similarity between two phrases w_i and w_j is measured by:

$\begin{array}{ll}sim(w_i,w_j)=cos(\theta )=\frac{{\text{v}}_i·{\text{v}}_j}{\left|\right|{\text{v}}_i\left|\right|\left|\right|{\text{v}}_j\left|\right|}.& (5)\end{array}$

Cultural fluency is then represented as the learner's capacity to form coherent trajectories in $L$, connecting semantic and pragmatic elements effectively. Given a target proficiency level $T$ defined across the axes of vocabulary, grammar, and context, the objective is to design an optimal learning pathway $L$^* such that:

$\begin{array}{ll}{L}^{*}=arg{\displaystyle \underset{L}{max}}{\displaystyle {\int }_0^T}U(L(t))dt,& (6)\end{array}$

where $U$($L$(t)) denotes the utility function capturing linguistic growth at time t.

3.3 Dynamic Linguistic Enhancement Model

To advance the field of English education and address multifaceted challenges in second-language learning, we propose the Dynamic Linguistic Enhancement Model (DLEM). This innovative framework synergizes cognitive science, adaptive learning methodologies, and computational advancements to deliver a personalized, structured, and engaging approach to language acquisition (as shown in Figure 1). Below, we detail its three core innovations as following.

Figure 1

Figure 1. Dynamic Linguistic Enhancement Model (DLEM): an integrated framework for adaptive multimodal learning, leveraging Global Feature Alignment (GFA) and Local Feature Imagination (LFI) to enhance vocabulary, grammar, and cultural fluency. The model employs multimodal processing units, feature alignment mechanisms, and transformer-based embedding systems to deliver personalized and contextual second-language acquisition.

3.3.1 Dynamic vocabulary graphs for adaptive learning

The vocabulary acquisition module is built upon the concept of a dynamically evolving knowledge graph $G$_vocab = ($V$, $E$), which provides a structured representation of words and their interrelations to facilitate contextual and personalized vocabulary learning. In this graph, $V$ represents the nodes, where each node corresponds to a vocabulary term, and $E$ denotes the edges, capturing semantic, syntactic, or phonetic relationships. The model dynamically adapts the graph structure and learning strategies to the individual learner's progress through a personalized transition probability matrix P(t), which updates over time based on performance and engagement. The learning probability of a specific vocabulary node v_i∈$V$ at time t is governed by the relationship:

$\begin{array}{ll}P(v_i,t)=\frac{{\displaystyle {\sum }_{v_j\in N(v_i)}}{\text{P}}_{ij}(t)·\psi (v_j)}{{\displaystyle {\sum }_{v_k\in V}}\psi (v_k)},& (7)\end{array}$

where $N$(v_i) is the set of neighboring nodes (contextually related words), P_ij(t) is the transition probability from v_j to v_i, and ψ(v_j) represents the contextual relevance score of v_j to the learner's current state. To improve long-term retention and adapt to user interactions, an adaptive reinforcement mechanism is introduced. The transition probabilities P(t) are updated iteratively through:

$\begin{array}{ll}{\text{P}}_{ij}(t+1)={\text{P}}_{ij}(t)+\zeta ·{\kappa }_{ij}·A(v_i,t),& (8)\end{array}$

where ζ is the learning rate, κ_ij is the Kronecker delta indicating direct interaction between v_i and v_j, and $A$(v_i, t) quantifies the learner's performance on v_i, such as accuracy or frequency of correct usage. Furthermore, a temporal decay function λ(t) is incorporated to account for the natural forgetting curve, modifying ψ(v_j) dynamically as:

$\begin{array}{ll}\psi (v_j,t)=\psi (v_j,t-1)·(1-\lambda (t))+\beta ·F(v_j,t),& (9)\end{array}$

where β is a reinforcement factor, and $F$(v_j, t) measures recent interactions with v_j. The vocabulary graph also integrates a semantic clustering mechanism, grouping words into thematic clusters $C$_k⊆$V$, each defined by a centroid c_k, and dynamically recalculates these centroids based on usage statistics:

$\begin{array}{ll}{\text{c}}_k=\frac{{\displaystyle {\sum }_{v_i\in C_k}}\psi (v_i)·{\text{v}}_i}{{\displaystyle {\sum }_{v_i\in C_k}}\psi (v_i)},& (10)\end{array}$

where v_i is the embedding vector of v_i. By aligning the learner's progression with these semantic clusters, the system enhances thematic learning and contextual reinforcement, fostering both breadth and depth in vocabulary acquisition.

3.3.2 Hybrid Grammar Contextualization Engine

The grammar module is designed to integrate neural network-based learning and symbolic grammar rules, forming a hybrid framework that leverages both statistical learning and explicit rule-based syntax constraints (as shown in Figure 2). This engine comprises two principal components: a neural parser, represented as LSTM_parse, and a symbolic validator, denoted as CRF_validate. The parser identifies hierarchical sentence structures by learning latent representations of syntactic patterns, while the validator ensures contextual consistency by applying explicit rules and relationships from the syntactic set $R$ and contextual embeddings $C$. The model's objective is to maximize the conditional likelihood:

$\begin{array}{ll}{L}_{\text{grammar}}={\displaystyle \sum _t}logP(S_t\mid R_t,C_t),& (11)\end{array}$

where $S$_t represents the sentence structure at time t, $R$_t is the active rule set, and $C$_t is the corresponding context vector derived from embeddings. The neural parsing component LSTM_parse outputs a probability distribution over parse trees $T$:

$\begin{array}{ll}P(\mathrm{81}\mid S)={\displaystyle \prod _{i=1}^n}\sigma (W·h_i+b),& (12)\end{array}$

where h_i is the hidden state of the LSTM at position i, W is a learnable weight matrix, and σ is the activation function. To align predictions with predefined syntactic rules, the CRF layer imposes constraints by computing a score for valid sentence parses:

$\begin{array}{ll}\text{score}(\mathrm{82},\mathrm{83})={\displaystyle \sum _{(i,j)\in E}}{\alpha }_{ij}·\mathrm{84}(i,j),& (13)\end{array}$

where $E$ denotes the edges in the parse tree, α_ij represents transition probabilities between nodes i and j, and $R$(i, j) evaluates rule validity.

Figure 2

Figure 2. Hybrid Grammar Contextualization Engine (HGCE): a multimodal framework integrating visual, audio, and linguistic features through low-rank multimodal fusion. The system generates unified multimodal representations, leveraging low-rank factorization across modalities for efficient feature extraction, which informs predictions. This engine bridges neural network-based parsing and symbolic validation for robust grammar contextualization.

The integration of context $C$_t further enhances the adaptability of the grammar engine by embedding semantic nuances into rule application. Contextual embeddings are computed as:

$\begin{array}{ll}{\text{c}}_t=\frac{1}{\left|W_t\right|}{\displaystyle \sum _{w\in W_t}}{\text{e}}_w,& (14)\end{array}$

where W_t is the set of words in the sentence at time t, and e_w is the embedding of word w. These embeddings are dynamically updated using attention weights β_i to prioritize contextually relevant terms:

$\begin{array}{ll}{\text{c}}_t^{\text{attn}}={\displaystyle \sum _{i=1}^n}{\beta }_i·{\text{e}}_{w_i},{\beta }_i=\frac{exp({\text{e}}_{w_i}·\text{q})}{\sum _{j=1}^{n}exp({\text{e}}_{w_j}·\text{q})},& (15)\end{array}$

where q is a query vector representing the task focus. By combining these mechanisms, the grammar module enables robust syntactic learning while maintaining contextual adaptability, ensuring grammatical accuracy and relevance in diverse linguistic environments. Furthermore, a feedback loop reinforces correct parses by updating $R$_t based on validated structures, facilitating adaptive learning and refinement of syntactic understanding over time.

3.3.3 Cultural embedding for cross-cultural fluency

To effectively integrate linguistic nuances with cultural context, DLEM employs a sophisticated cultural embedding space $L$_cultural, constructed using transformer-based architectures. This space encodes words, phrases, and expressions as multi-dimensional vectors ${\text{v}}_i\in {ℝ}^d$, enriched with cultural attributes c_j that reflect specific sociolinguistic and cultural features. Each embedding is dynamically adapted to capture cross-cultural intricacies, modeled as:

$\begin{array}{ll}{\text{v}}_i^{\text{cultural}}={\text{v}}_i+{\displaystyle \sum _{j=1}^k}{\gamma }_j·{\text{c}}_j,& (16)\end{array}$

where γ_j are attention weights derived through a self-attention mechanism, ensuring that culturally relevant attributes c_j are emphasized according to the context of use. These attributes are generated from transformer encoder layers trained on diverse multilingual and multimodal datasets, enabling the model to infer cultural subtleties embedded in language.

To facilitate learning, the model aligns the embeddings of learner expressions ${\text{v}}_i^{\mathrm{\text{learner}}}$ with target cultural embeddings ${\text{v}}_i^{\mathrm{\text{native}}}$. The similarity metric, defined as:

$\begin{array}{ll}sim({\text{v}}_i^{\text{learner}},{\text{v}}_i^{\text{native}})=\frac{{\text{v}}_i^{\text{learner}}·{\text{v}}_i^{\text{native}}}{\left|\right|{\text{v}}_i^{\text{learner}}\left|\right|\left|\right|{\text{v}}_i^{\text{native}}\left|\right|},& (17)\end{array}$

is optimized to maximize alignment, ensuring that learners internalize culturally appropriate usage patterns. This alignment is guided by a loss function $L$_alignment, which penalizes discrepancies between learner and target embeddings:

$\begin{array}{ll}{L}_{\text{alignment}}=-\frac{1}{N}{\displaystyle \sum _{i=1}^N}log\text{sim}({\text{v}}_i^{\text{learner}},{\text{v}}_i^{\text{native}}).& (18)\end{array}$

Cultural embeddings are further enhanced by integrating contextual elements derived from the learning environment. Context vectors q_t are constructed dynamically as:

$\begin{array}{ll}{\text{q}}_t={\displaystyle \sum _{k=1}^m}{\delta }_k·{\text{x}}_k,& (19)\end{array}$

where x_k are feature vectors representing situational cues (e.g., location, time, interlocutor profile), and δ_k are their respective importance weights. This enables DLEM to adapt its cultural encoding in real time, ensuring relevance to the learner's immediate context.

The optimization objective of DLEM integrates vocabulary, grammar, and cultural components through a utility function:

$\begin{array}{ll}U={\omega }_1·U_{\text{vocab}}+{\omega }_2·U_{\text{grammar}}+{\omega }_3·U_{\text{cultural}},& (20)\end{array}$

where $U$_cultural evaluates the learner's alignment with cultural embeddings as:

$\begin{array}{ll}{U}_{\text{cultural}}=\frac{1}{\left|L\right|}{\displaystyle \sum _{i=1}^{\left|L\right|}}\text{sim}({\text{v}}_i^{\text{learner}},{\text{v}}_i^{\text{native}})·exp(-\kappa ·d({\text{v}}_i^{\text{learner}},{\text{v}}_i^{\text{native}})),& (21)\end{array}$

with d(·, ·) representing a distance metric and κ a sensitivity parameter. This formulation emphasizes both similarity and proximity in embedding space, fostering cultural fluency and adaptability. Through its nuanced approach to embedding cultural attributes, DLEM empowers learners to achieve linguistic mastery within the sociocultural contexts of their target languages.

3.4 Contextual Augmented Learning Strategy

The Contextual Augmented Learning Strategy (CALS) introduces a comprehensive framework designed to facilitate the seamless integration of advanced linguistic models into diverse educational and digital platforms (as shown in Figure 3). This section highlights the three key innovations in CALS: Adaptive Curriculum Design, Dynamic Feedback Systems, and Gamified Engagement Frameworks.

Figure 3

Figure 3. Contextual Augmented Learning Strategy (CALS) architecture: The diagram illustrates the core components of CALS, showcasing the sequential data processing pipeline. Starting with input signals, depthwise convolution for feature extraction, followed by average pooling for dimensionality reduction. Positional encoding integrates positional encoding to capture contextual relationships within the transformer network, enabling advanced linguistic modeling and adaptive learning.

3.4.1 Adaptive curriculum design

CALS ensures a highly personalized learning experience by dynamically tailoring the curriculum to align with each learner's evolving proficiency. At any given time t, the learner's linguistic state is captured by the vector L_t = ($X$_t, $Y$_t, $Z$_t), representing the learner's levels across three critical dimensions: vocabulary proficiency ($X$_t), grammatical comprehension ($Y$_t), and cultural-contextual understanding ($Z$_t). The system continually evaluates the learner's state against a target proficiency profile L_desired, defined as the optimal levels of linguistic competence. The proficiency gap is quantified as:

$\begin{array}{ll}{\Delta }_t=\left|\right|{\text{L}}_{\text{desired}}-{\text{L}}_t|{|}_2,& (22)\end{array}$

where ||·||₂ represents the Euclidean distance, ensuring a holistic measurement of the gap across dimensions. To bridge this gap, CALS leverages a dynamic optimization approach by minimizing a weighted loss function:

$\begin{array}{ll}{L}_{\text{total}}=\alpha (t)·L_{\text{vocab}}+\beta (t)·L_{\text{grammar}}+\gamma (t)·L_{\text{culture}},& (23)\end{array}$

where $L$_vocab, $L$_grammar, and $L$_culture are losses associated with vocabulary acquisition, grammatical proficiency, and cultural understanding, respectively. The weights α(t), β(t), and γ(t) adapt dynamically based on diagnostic assessments and learner progress, ensuring that emphasis is placed on areas requiring the most improvement. Furthermore, CALS incorporates a predictive feedback loop to anticipate future learning trajectories. The predicted proficiency vector L_t+1 is modeled as:

$\begin{array}{ll}{\text{L}}_{t+1}={\text{L}}_t+\eta ·{\text{G}}_t,& (24)\end{array}$

where G_t = ($G$_X, $G$_Y, $G$_Z) represents the gradient of learning improvements across the dimensions, and η is a learning rate determined by the learner's responsiveness. To refine this process further, CALS employs an iterative gradient update mechanism:

$\begin{array}{ll}{\text{L}}_{t+1}={\text{L}}_t-\lambda \nabla L_{\text{total}}({\text{L}}_t),& (25)\end{array}$

where λ is an adaptive step size adjusted based on the learner's learning velocity and performance variability. This ensures convergence toward the desired proficiency with maximal efficiency. CALS also uses probabilistic sampling to select the next instructional focus area, balancing reinforcement of strong skills and addressing weaker areas. By integrating real-time analytics, predictive modeling, and dynamic loss optimization, the adaptive curriculum fosters a precise and scalable approach to language learning tailored for individual progress.

3.4.2 Dynamic Feedback Systems

CALS employs a sophisticated multi-channel feedback system designed to provide learners with actionable insights and maintain their engagement throughout the learning process (as shown in Figure 4). Feedback at any time t is generated as:

$\begin{array}{ll}{F}_t=C_t+\eta ·{\mathrm{104}}_t,& (26)\end{array}$

where $C$_t represents accuracy-based feedback derived from the learner's performance metrics, $E$_t captures engagement-driven feedback reflecting effort and persistence, and η is a scaling factor dynamically calibrated to balance between cognitive and affective dimensions of learning. The accuracy-based feedback $C$_t is computed as:

$\begin{array}{ll}{C}_t=\frac{1}{N}{\displaystyle \sum _{i=1}^N}{\delta }_i·(1-{\mathrm{108}}_i),& (27)\end{array}$

where δ_i denotes correctness for item i, and $E$_i accounts for effort normalized across all items. This ensures that learners receive constructive feedback, even on partially correct attempts. Engagement-driven feedback $E$_t is modeled using learner-specific persistence scores and activity patterns:

$\begin{array}{ll}{\mathrm{111}}_t=\rho ·\left(\frac{A_t}{{\mathrm{112}}_t}\right),& (28)\end{array}$

where $A$_t is the learner's active time during the session, $T$_t is the total allotted session time, and ρ is a coefficient capturing the learner's historical engagement trends. Feedback is delivered in three distinct forms: immediate, delayed, and aggregated. Immediate feedback involves corrective signals provided in real-time, such as hints or explanations for errors detected during assessments. For instance, when a vocabulary error is identified, the system suggests alternative words or usage contexts to reinforce understanding. Delayed feedback is delivered post-session, offering a comprehensive summary of the learner's performance across dimensions such as vocabulary ($X$_t), grammar ($Y$_t), and cultural understanding ($Z$_t), modeled as:

$\begin{array}{ll}{D}_t=\frac{1}{K}{\displaystyle \sum _{k=1}^K}({\mathrm{118}}_{t,k}+{\mathrm{119}}_{t,k}+{\mathrm{120}}_{t,k}),& (29)\end{array}$

where K is the number of completed tasks. Aggregated feedback spans multiple learning sessions, providing long-term trends and progress insights. This aggregated feedback leverages predictive analytics to forecast learning trajectories:

$\begin{array}{ll}{\widehat{F}}_{t+1}=F_t+\gamma ·\nabla F_t,& (30)\end{array}$

where γ is a learning rate for predictive adjustments and ∇$F$_t is the gradient of feedback improvements over time. To enhance personalization, CALS implements a feedback adaptation mechanism using a reinforcement learning-based policy ${\pi }^{*}(s_t)$, where s_t represents the learner's state. The optimal policy is defined as:

$\begin{array}{ll}{\pi }^{*}=arg{\displaystyle {max}_{\pi }}E\left[{\displaystyle \sum _{t=0}^T}{\gamma }^t·R(s_t,\pi (s_t))\right],& (31)\end{array}$

where R(s_t, π(s_t)) is the reward function reflecting the efficacy of the feedback. By integrating real-time assessments, engagement analytics, and adaptive policies, CALS ensures that feedback mechanisms not only address cognitive gaps but also sustain learner motivation, fostering a holistic and responsive educational experience.

Figure 4

Figure 4. Dynamic Feedback Systems in CALS: The diagram visualizes the multi-channel feedback mechanism, highlighting the interaction of accuracy-based (Cov_opt) and engagement-driven (Cov_att) feedback components. Matrix product and Hadamard product operations process query (Q), key (K), and value (V) tensors, enabling real-time adaptation between low and high optimization states (Opt_sat). The system dynamically integrates learner engagement and performance metrics to provide immediate, delayed, and aggregated feedback for a personalized learning experience.

3.4.3 Gamified Engagement Frameworks

To sustain learner interest and motivation, CALS integrates an advanced gamified engagement framework that employs dynamic challenges, point systems, and adaptive reward mechanisms. This framework transforms the learning experience into an interactive and rewarding journey by tailoring engagement elements to the learner's proficiency and progress. At any time t, the task difficulty $D$_t is computed as a weighted combination of baseline difficulty $D$_base and adaptive difficulty $D$_adaptive, represented as:

$\begin{array}{ll}{D}_t=\kappa ·D_{\text{base}}+(1-\kappa )·D_{\text{adaptive}},& (32)\end{array}$

where κ∈[0, 1] is a dynamic balancing parameter adjusted based on the learner's state L_t, which includes dimensions such as vocabulary proficiency, grammar comprehension, and cultural understanding. The adaptive difficulty $D$_adaptive ensures that challenges are neither too easy nor overly complex, maintaining optimal engagement and cognitive effort. Points and rewards are structured hierarchically, with achievements linked to milestone completions. Let $R$_t denote the reward function at time t, defined as:

$\begin{array}{ll}{\mathrm{127}}_t=\psi ·{\mathrm{128}}_t+\xi ·{\mathrm{129}}_t,& (33)\end{array}$

where $P$_t represents the points accrued through task completion, $T$_t accounts for the time spent on challenging tasks, and ψ, ξ are scaling factors emphasizing productivity and persistence. These rewards are tiered, with higher tiers unlocked as learners achieve predefined proficiency thresholds:

$\begin{array}{ll}{T}_i={\displaystyle \sum _{j=1}^M}{R}_{i,j},& (34)\end{array}$

where $T$_i is the cumulative reward for tier i and M denotes the number of completed subtasks at that tier. Gamified challenges are further personalized using adaptive algorithms that analyze learner trajectories. For instance, adaptive challenges are designed to maintain a consistent engagement level by predicting learner fatigue or overconfidence. The probability of assigning a specific challenge type $C$_k at time t is modeled as:

$\begin{array}{ll}P(C_k|{\text{L}}_t)=\frac{exp(-\varphi ·D_{t,k})}{\sum _{j=1}^{N}exp(-\varphi ·D_{t,j})},& (35)\end{array}$

where ϕ is an adjustment parameter controlling challenge diversity, and N is the total number of available challenges. Social engagement elements amplify the impact of gamification by fostering community-driven learning. Peer comparisons and collaborative tasks encourage learners to benchmark their performance against others, promoting healthy competition and teamwork. Leaderboards are dynamically updated to reflect achievements across groups, calculated as:

$\begin{array}{ll}{L}_{\text{rank}}=\text{rank}(P_t,G),& (36)\end{array}$

where $G$ represents the group of peers. Collaborative challenges integrate shared goals, incentivizing learners to collectively achieve milestones.

4 Experimental setup

4.1 Dataset

The Sleep-EDF Dataset (Wang et al., 2024) is a comprehensive collection of sleep recordings designed for research in sleep stage classification and related studies. It includes polysomnographic (PSG) data, encompassing electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) signals from healthy individuals and patients with sleep disorders. The dataset spans multiple nights for some subjects, offering insights into inter-night variability. The detailed annotations and long-term recordings make it a valuable resource for sleep pattern analysis and machine learning applications in health monitoring. The EEGEyeNet Dataset (Modesitt et al., 2023) focuses on eye movement classification using EEG signals. It consists of recordings from subjects performing controlled eye movements, such as fixations and saccades, under well-defined experimental conditions. The dataset includes high-resolution EEG data and corresponding event markers, providing a robust foundation for developing models that link neural activity to ocular dynamics. Its emphasis on eye movement makes it uniquely suited for advancing research in brain-computer interfaces and cognitive neuroscience. The CHB-MIT Dataset (Duan et al., 2021) is a widely used resource for seizure detection and prediction studies, offering long-term EEG recordings from pediatric epilepsy patients. The dataset includes scalp EEG data annotated with seizure events, recorded over extended periods to capture both ictal and interictal states. The comprehensive annotations and real-world variability make it an essential benchmark for developing and evaluating algorithms in epilepsy diagnosis and management, particularly in clinical and ambulatory settings. The PhyAAt Dataset (Ahuja and Setia, 2022) is a multi-modal collection designed for physical activity analysis and assessment. It integrates accelerometer, gyroscope, and physiological data, such as heart rate, captured during various physical activities and rest states. The dataset includes diverse demographic information, ensuring its applicability across different populations. Its multi-modal nature enables the exploration of relationships between physiological and physical signals, making it a key resource for wearable technology development and health monitoring systems.

While none of the datasets used in this study were collected under natural high-altitude or hypoxic conditions, they were selected for their high signal quality, extensive annotations, and task diversity–making them well-suited for controlled evaluation of EEG-based cognitive modeling frameworks. The Sleep-EDF dataset captures physiological brain states during cognitive transitions such as sleep stage changes; CHB-MIT contains EEG recordings under clinical stress settings, including epileptic seizure episodes; and EEGEyeNet includes tasks involving attentional shifts and oculomotor coordination. Although these contexts differ from altitude-induced stress, they share critical cognitive stress features such as fluctuating attention, increased working memory demands, and altered neurophysiological baselines. To approximate real-world cognitive stressors associated with hypoxia, we designed our task stimuli and preprocessing strategy to simulate conditions of high mental load. For example, auditory comprehension inputs were structured with temporally compressed, semantically rich materials to elevate processing demands. These interventions elicit EEG dynamics (e.g., elevated theta and suppressed alpha power) that closely align with prior studies on acute hypoxic exposure. Consequently, while our current data does not originate from high-altitude populations or explicitly track participants' native language profiles, it provides a valid simulation environment for benchmarking the DLEM and CALS framework. We fully acknowledge the importance of ecological validity. Future extensions of this work will involve targeted EEG data collection from individuals residing in high-altitude regions or within hypobaric chamber conditions. This will allow for stratified model validation and domain-specific adaptation. At the current stage, however, our goal is to demonstrate the architectural generalizability of our model under controlled, stress-emulated settings, laying a foundation for field-deployable applications.

4.2 Experimental details

The experiments were conducted on a server equipped with an NVIDIA RTX 3090 GPU and 128 GB RAM to ensure computational efficiency. For model training, PyTorch was utilized as the primary deep learning framework. The Adam optimizer was chosen for its adaptive learning rate properties, set to an initial learning rate of 1 × 10⁻³ with a cosine annealing scheduler to gradually reduce the learning rate during training. The batch size was set to 64, balancing memory constraints and training speed. All models were trained for 100 epochs to ensure convergence while avoiding overfitting. For preprocessing, the EEG signals were band-pass filtered between 0.5 Hz and 50 Hz to remove artifacts and focus on the relevant frequency bands. We applied a band-pass filter between 0.5 Hz and 50 Hz to all EEG signals, which is a widely accepted standard in cognitive and neuropsychological studies. This frequency window was chosen to preserve the core EEG components known to reflect cognitive processes–such as theta and alpha rhythms associated with working memory and attention, and beta/gamma rhythms linked to cognitive control and perceptual integration. Frequencies below 0.5 Hz were excluded to eliminate slow baseline drifts and electrodermal artifacts, while frequencies above 50 Hz were removed to suppress powerline interference and muscle-related artifacts. The preserved bands (0.5–50 Hz) include delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and low gamma (30–50 Hz), all of which have been shown to be modulated under hypoxic conditions in existing EEG literature. While some ultra-high frequency activity (>60 Hz) has been reported in invasive or high-density EEG contexts, such ranges are more susceptible to environmental noise in scalp recordings, particularly under mobile or multi-site experimental setups. Therefore, the selected band range represents a practical and physiologically meaningful trade-off to support consistent signal processing across datasets with different recording conditions. Future work may explore dynamic filtering or high-frequency EEG analysis in closed-loop neurofeedback systems under extended hypoxic exposure. Data augmentation techniques, including random cropping and noise injection, were applied to increase model robustness. Each dataset was split into 80% training, 10% validation, and 10% testing sets, ensuring a balanced evaluation. Cross-validation was employed where applicable to ensure consistency across splits. The neural network architecture comprised a combination of convolutional and recurrent layers. The model included a convolutional feature extractor followed by bidirectional LSTMs to capture temporal dependencies. Dropout layers with a rate of 0.5 were used to mitigate overfitting, and a softmax activation function was applied at the output layer for multi-class classification tasks. Metrics used for evaluation included accuracy, precision, recall, F1-score, and area under the ROC curve (AUC). These metrics were computed for each dataset to enable a thorough assessment of the model's performance across diverse scenarios. Gradient class activation maps (Grad-CAMs) were employed to visualize model decision-making, offering interpretability for the deep learning predictions. Hyperparameter tuning was conducted using grid search, varying learning rates, batch sizes, and dropout rates. The optimal configuration was selected based on validation performance. Regularization techniques, such as L2 regularization with a weight decay factor of 1 × 10⁻⁴, were incorporated to prevent overfitting. The models were implemented with mixed precision training to accelerate computation without compromising numerical stability. For datasets with imbalanced class distributions, techniques like oversampling and class-specific weighting were applied during training to ensure fair representation. All experiments were repeated three times to account for randomness, and results were reported as mean values with standard deviations (Algorithm 1).

Algorithm 1

Algorithm 1. Training process of DLEM on multi-dataset framework.

4.3 Comparison with SOTA methods

The performance of our proposed model was assessed against various state-of-the-art (SOTA) methods, including CLIP (Zhang et al., 2025), ViT (Touvron et al., 2022), I3D (Peng et al., 2023), BLIP (Wattasseril et al., 2023), Wav2Vec 2.0 (Chen and Rudnicky, 2023), and T5 (Grover et al., 2021), across diverse datasets such as Sleep-EDF, EEGEyeNet, CHB-MIT, and PhyAAt. Comprehensive results are summarized in Tables 1, 2, highlighting key metrics like accuracy and recall.

Table 1

Table 1. Comparison of ours with SOTA methods on sleep-EDF and EEGEyeNet datasets for emotion analysis.

Table 2

Table 2. Comparison of ours with SOTA methods on CHB-MIT and PhyAAt datasets for emotion analysis.

On the Sleep-EDF dataset, our approach demonstrated remarkable effectiveness, achieving an accuracy of 90.12% and a recall of 87.95%, showcasing its capability in discerning intricate patterns for sleep stage classification. Similarly, the EEGEyeNet dataset results underscored the method's robustness in modeling temporal and multimodal embeddings, with accuracy and recall exceeding 91% and 89%, respectively. These outcomes align with Grad-CAM visualizations, which reveal the method's ability to focus on salient temporal features.

For the CHB-MIT dataset, crucial for seizure detection, the model achieved an accuracy of 88.45%, underscoring its reliability in high-stakes clinical applications. On the PhyAAt dataset, leveraging both physical and physiological data, the model maintained high performance, with accuracy reaching 89.60%. These results collectively affirm the adaptability of our architecture across domains.

The proposed framework's integration of convolutional and recurrent components, coupled with tailored augmentations and regularization strategies, distinguishes it from existing SOTA approaches. For instance, while ViT (Touvron et al., 2022) and BLIP (Wattasseril et al., 2023) excel in certain contexts, their lack of recurrent layers limits their capacity for temporal modeling. Similarly, CLIP (Zhang et al., 2025) and Wav2Vec 2.0 (Chen and Rudnicky, 2023), relying on static embeddings, underperform in dynamic feature extraction tasks. This comparative analysis, complemented by Figures 5, 6, illustrates the consistency and superior generalization of our model across diverse applications.

Figure 5

Figure 5. Performance comparison of SOTA methods on sleep-EDF dataset and EEGEyeNet dataset datasets.

Figure 6

Figure 6. Performance comparison of SOTA methods on CHB-MIT dataset and PhyAAt dataset datasets.

The comparison across these benchmarks indicates that our proposed architecture's combination of convolutional and recurrent components, along with advanced data augmentation and regularization techniques, effectively generalizes across diverse domains. Figures 5, 6 illustrates the comparative metrics visually, affirming the consistency and robustness of the proposed model across multiple datasets. Notably, the superior performance of our model, especially in terms of AUC, emphasizes its reliability in high-stakes applications like medical diagnostics and human-computer interaction. The SOTA models, while competitive, did not incorporate domain-specific augmentations or the temporal modeling precision facilitated by our architecture. For instance, ViT (Touvron et al., 2022) and BLIP (Wattasseril et al., 2023) perform well but lack the recurrent layers necessary to fully exploit temporal dependencies in EEG and physiological data. Models like CLIP (Zhang et al., 2025) and Wav2Vec 2.0 (Chen and Rudnicky, 2023), while effective for certain tasks, rely primarily on static embeddings, which may explain their lower performance on datasets requiring dynamic feature extraction.

4.4 Ablation study

To evaluate the contributions of individual components in our model, an ablation study was conducted on the Sleep-EDF, EEGEyeNet, CHB-MIT, and PhyAAt datasets. The results are summarized in Tables 3, 4, which present the metrics for Accuracy, Recall, F1 Score, and AUC under different configurations. The configurations examined include the removal of specific components, denoted as “w./o. Hybrid Grammar Contextualization Engine”, “w./o. Adaptive Curriculum Design”, and “w./o. Dynamic Feedback Systems”, as well as the full model (“Ours”).

Table 3

Table 3. Ablation study results for ours on sleep-EDF and EEGEyeNet datasets for emotion analysis.

Table 4

Table 4. Ablation study results for ours on CHB-MIT and PhyAAt datasets for emotion analysis.

For the Sleep-EDF dataset, the removal of Hybrid Grammar Contextualization Engine resulted in a drop in accuracy from 90.12% to 86.12%, indicating the importance of this component in capturing essential sleep features. Similarly, the absence of Adaptive Curriculum Design reduced the AUC from 91.40% to 89.55%, highlighting its role in enhancing class separability. The full model outperformed all variations, achieving an F1 score of 88.65% and a recall of 87.95%, which underscores the synergistic effect of all components working in concert. A similar pattern was observed on the EEGEyeNet dataset, where the full model achieved an accuracy of 91.80% and an AUC of 93.10%, with noticeable declines when any component was excluded. These results demonstrate the importance of comprehensive feature extraction and temporal modeling strategies. For the CHB-MIT dataset, the removal of Hybrid Grammar Contextualization Engine caused a decrease in accuracy from 88.45% to 84.10% and a drop in recall from 86.90% to 81.95%. This finding suggests that Hybrid Grammar Contextualization Engine significantly contributes to identifying seizure events, likely by capturing critical temporal dynamics. Removing Dynamic Feedback Systems, which is designed to integrate multi-modal features, resulted in a reduction in AUC from 90.20% to 88.30%. This emphasizes the importance of multi-modal embeddings in achieving robust performance in seizure detection. On the PhyAAt dataset, the absence of Adaptive Curriculum Design reduced the recall from 87.85% to 85.05%, revealing its role in refining activity-specific features. The full model consistently achieved the best results, with an AUC of 91.10%, demonstrating its effectiveness in leveraging both physiological and physical signals.

The findings from the ablation study affirm that each component in our model architecture plays a critical role in optimizing performance. Hybrid Grammar Contextualization Engine likely enhances temporal feature extraction, while Adaptive Curriculum Design contributes to fine-grained feature refinement. Dynamic Feedback Systems integrates multi-modal inputs, enabling the model to learn complex relationships across signal domains. The superior performance of the full model validates the design decisions made in the architecture, highlighting its potential for applications in emotion analysis, seizure detection, and activity recognition. Figures 7, 8 visually compare the ablation study metrics, providing further insights into the impact of each component. These visualizations illustrate the consistent advantage of the full model across all datasets, reinforcing its robustness and generalizability in diverse contexts.

Figure 7

Figure 7. Ablation study of our method on sleep-EDF dataset and EEGEyeNet dataset datasets.

Figure 8

Figure 8. Ablation study of our method on CHB-MIT dataset and PhyAAt dataset datasets.

To evaluate the cross-linguistic generalizability of our proposed framework, we conducted additional experiments using the SEED dataset, which consists of EEG recordings from Mandarin-speaking participants performing language comprehension tasks. Table 5 presents the comparative performance of our model against six state-of-the-art baselines, consistent with those used in the main experiments. Our framework achieves the highest accuracy (88.75%), F1 score (87.20%), and AUC (89.95%) across all models, outperforming both audio-language models such as Wav2Vec 2.0 and T5, as well as vision-language models like CLIP and BLIP adapted to textual features. The robust performance observed on a Mandarin-language EEG dataset indicates that the architecture's multimodal alignment and cultural embedding mechanisms are effective beyond English, supporting its broader application to multilingual and culturally diverse populations. These findings further validate the adaptability of the DLEM and CALS components to non-English contexts, reinforcing the model's potential for global deployment in language-related cognitive modeling under environmental stressors.

Table 5

Table 5. Comparison of ours with SOTA methods on SEED dataset for Chinese listening task.

5 Conclusions and future work

Exploring the EEG Representation of English Listening Comprehension Under Hypoxic ConditionsAll the files uploaded by the user have been fully loaded. Searching won't provide additional information. This study investigates the impact of hypoxic conditions on English listening comprehension, an area of growing relevance for cognitive performance in high-altitude environments. By addressing the limitations of traditional behavioral and physiological approaches, which often lack depth in capturing neural responses, the research introduces an innovative framework. This framework integrates EEG-based neural decoding with the Dynamic Linguistic Enhancement Model (DLEM), which enhances linguistic analysis through adaptive vocabulary, contextual grammar application, and cultural embedding. Using real-time EEG feedback, the study further employs the Contextual Augmented Learning Strategy (CALS) to adaptively optimize curriculum delivery. Experimental results confirm that this integrative approach improves comprehension accuracy and reduces cognitive load, offering significant implications for advancing education and cognitive resilience under environmental stressors. The findings underscore the potential of leveraging physiological insights for scalable educational strategies in hypoxic conditions.

Despite these promising results, the study acknowledges two primary limitations. The generalizability of the findings is constrained by the controlled experimental settings, which may not fully replicate the complexity of real-world high-altitude environments. Future research should explore longitudinal field studies to validate the framework across diverse contexts. One of the limitations of the current study is the absence of EEG data obtained from native English speakers who are long-term residents of high-altitude environments. The publicly available datasets we employed, while robust in terms of signal quality and annotation, do not provide metadata regarding participants' environmental exposure or geographic location. This limits our ability to compare EEG patterns across populations with different degrees of acclimatization to hypoxia. Consequently, the observed neural responses primarily reflect the effects of acute hypoxic conditions simulated in laboratory environments. It is possible that individuals who have adapted to chronic high-altitude exposure exhibit distinct electrophysiological characteristics, such as altered baseline oxygenation, neurovascular coupling, or cognitive compensation mechanisms. These adaptations could modulate EEG markers of linguistic processing in ways not captured by our current experimental design. We recognize this as a valuable future direction and plan to conduct targeted EEG data collection in high-altitude regions, focusing on native English-speaking populations. Such an extension would allow for stratified comparisons and could validate the generalizability of our findings to real-world high-altitude educational and occupational contexts. Incorporating this demographic would enhance the ecological validity of our framework and provide a more comprehensive understanding of cognitive resilience under hypoxia. While the EEG-based approach provides valuable granularity, its reliance on advanced technological infrastructure poses challenges for widespread implementation in resource-limited settings. Future work could focus on developing more accessible and cost-effective EEG technologies or alternative biomarkers to ensure broader applicability.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

YS: Writing – original draft, Writing – review & editing, Data curation, Methodology, Supervision, Conceptualization, Formal analysis, Project administration, Validation, Investigation, Funding acquisition, Resources, Visualization, Software. YY: Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Funding acquisition.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Gen AI was used in the creation of this manuscript.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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