Depression is a prevalent mental health disorder characterized by persistent sadness and a diminished interest in daily activities, early detection of depression facilitates timely intervention, mitigating its adverse effects. Electroencephalography (EEG) signals and eye movements are emerging as promising biomarkers for depression detection due to their non-invasive nature and cost-effectiveness. Nevertheless, existing studies suffer from methodological constraints, including low specificity, insufficient sample sizes, limited generalizability, and difficulties in large-scale replication, which collectively undermine their clinical utility. To address these challenges, we collected a large-scale depression dataset comprising EEG and eye movements from 1,060 individuals diagnosed with depression and 1,308 healthy controls. To efficiently leverage multimodal data for automatic depression detection, we propose the EEG-Eye Movements Model (E2Mo). E2Mo employs modality-specific encoders to extract discriminative multi-view features from each modality and incorporates a mixture-of-modality-experts architecture with multi pretraining tasks to achieve efficient and robust modality alignment and fusion. Our approach achieves a 70.06% balanced accuracy by leveraging multi-modal data, demonstrating the effectiveness of integrating EEG signals and eye movements for automatic depression detection.

Emotion recognition (ER) plays an important role in the field of modern technology and human-computer interaction. Traditional emotion recognition approaches usually utilize videos as stimuli. However, watching video lacks interaction. Recently, more and more game-related stimuli have been used. To explore emotion recognition in a more natural state, this study pioneers the use of Genshin Impact game and gameplay-related videos to evoke positive and neutral emotions. As Electroencephalogram (EEG) feature distributions of video and game scenarios are very different, we propose the Double Domain Converter Transformer network (DDCT) to enhance EEG-based cross-scenario emotion recognition, making full use of information from different domains. Our model has two converters that can convert input data into the source domain and the target domain, diminishing distribution difference in two feature spaces. Experimental results demonstrate that our model achieves a remarkable prediction accuracy of 84.95% in video-game mix-scenario emotion recognition and 74.76% in video to game cross-scenario emotion recognition. Our code is available at https://github.com/AlbertMentat/DDCT.

Research in emotion recognition using electroencephalography (EEG) has advanced rapidly, and affective EEG-based Brain-computer Interface (aBCI) technology is increasingly moving from lab research to real-world application. Nevertheless, EEG signals are inherently delicate and prone to noise and artifacts, especially in real-world environments where data quality often lags behind laboratory standards. This disparity poses substantial challenges for models trained on high-quality datasets. Conventional methods, such as data interpolation or exclusion, limit model efficacy. To overcome these challenges, we introduce the Spatial-Temporal Autoencoder for EEG Restoration (STAR). STAR leverages dynamic channel and temporal masking to mimic real-world signal degradation and incorporates a spatial-temporal alternating attention mechanism to encapsulate intricate spatiotemporal dynamics within EEG data. Our evaluations on three premium emotion recognition EEG datasets reveal that STAR effectively restores signals across varying corruption levels, significantly bolstering the performance of emotion recognition models in suboptimal conditions.

This paper introduces a novel Myanmar multimodal dataset called SEED-MYA, which is the first culturally and linguistically tailored multimodal emotion recognition dataset for the Burmese speakers. The SEED-MYA dataset consists of EEG and eye movement data collected using Myanmar video stimuli, addressing the underrepresentation of minority cultures in emotion recognition research. To investigate the fundamental characteristics of emotion recognition based on EEG and eye movement data from Myanmar participants, and to validate the quality and effectiveness of the SEED-MYA dataset, we implement the Multimodal Adaptive Emotion Transformer with Cross-Modal Attention (MAET-CMA) as a benchmarking tool. From our experiments on SEED-MYA, we have three main findings: (a) beta and gamma bands play a critical role in distinguishing positive, neutral, and negative emotional states; (b) combining EEG and eye movement data significantly enhances emotion recognition accuracy, with MAET-CMA achieving a maximum accuracy of 91.78%; and (c) EEG signals excel in recognizing negative and positive emotional states, while eye movement data are particularly effective at differentiating neutral emotion in our experimental setup. Our neural activity analysis reveals distinct patterns of activation in temporal, parietal, and prefrontal regions, providing insights into potential culture-related neural responses. We further compare our findings with established benchmarks from Chinese participants (SEED dataset) to explore cultural similarities and differences in emotion recognition. This analysis is structured into three components: band-level EEG comparisons, modality-specific performance analysis, and neural activity differences, providing a multi-level analysis of cultural effects. While some similarities in emotion recognition are observed across two cultures, our cross-cultural performance comparison between SEED and SEED-MYA further indicates that the Chinese dataset generalizes better as a test set. These findings underscore the importance of incorporating culturally diverse datasets in the development of globally applicable emotion recognition systems.

Meditation has been shown to improve mental health by reducing stress and enhancing cognitive functions. To monitor meditation quality, Electroencephalogram (EEG) offers an objective alternative to self-evaluation questionnaires, which suffer from subjectivity and delays. However, previous EEG protocols often inflated classification performance due to temporal correlation and auditory interference. This study introduces a new data acquisition protocol to address these issues. We constructed a dataset from 32 subjects, each participating on three sessions in different days to eliminate temporal interference. During each session, subjects alternated between audio-guided meditation and silent unguided meditation. Using the Multi-view Spectral-Spatial-Temporal Masked Autoencoder (MV-SSTMA) model which was pre-trained on the Emotion EEG dataset SEED, the model achieved superior classification performance in F1 score, accuracy, precision, and recall. These findings offer valuable insights for improving EEG-based meditation monitoring in biomedical applications.