Decoding brain activity from non-invasive electroencephalography (EEG) is crucial for brain-computer interfaces (BCIs) and the study of brain disorders. Notably, end-to-end EEG decoding has gained widespread popularity in recent years owing to the remarkable advances in deep learning research. However, many EEG studies suffer from limited sample sizes, making it difficult for existing deep learning models to effectively generalize to highly noisy EEG data. To address this fundamental limitation, this paper proposes a novel end-to-end EEG decoding algorithm that utilizes a low-rank weight matrix to encode both spatio-temporal filters and the classifier, all optimized under a principled sparse Bayesian learning (SBL) framework. Importantly, this SBL framework also enables us to learn hyperparameters that optimally penalize the model in a Bayesian fashion. The proposed decoding algorithm is systematically benchmarked on five motor imagery BCI EEG datasets ( $N=192$ ) and an emotion recognition EEG dataset ( $N=45$ ), in comparison with several contemporary algorithms, including end-to-end deep-learning-based EEG decoding algorithms. The classification results demonstrate that our algorithm significantly outperforms the competing algorithms while yielding neurophysiologically meaningful spatio-temporal patterns. Our algorithm therefore advances the state-of-the-art by providing a novel EEG-tailored machine learning tool for decoding brain activity.

In motor imagery-based brain-computer interfaces (BCIs), the spatial covariance features of electroencephalography (EEG) signals that lie on Riemannian manifolds are used to enhance the classification performance of motor imagery BCIs. However, the problem of subject-specific bandpass frequency selection frequently arises in Riemannian manifold-based methods. In this study, we propose a multiple Riemannian graph fusion (MRGF) model to optimize the subject-specific frequency band for a Riemannian manifold. After constructing multiple Riemannian graphs corresponding to multiple bandpass frequency bands, graph embedding based on bilinear mapping and graph fusion based on mutual information were applied to simultaneously extract the spatial and spectral features of the EEG signals from Riemannian graphs. Furthermore, with a support vector machine (SVM) classifier performed on learned features, we obtained an efficient algorithm, which achieves higher classification performance on various datasets, such as BCI competition IIa and in-house BCI datasets. The proposed methods can also be used in other classification problems with sample data in the form of covariance matrices.

Emotion recognition from EEG signals offers significant advantages in affective computing, as EEG more accurately reflects internal emotional states than other modalities, such as facial expressions or peripheral physiological signals. Modeling and capturing subtle affective changes over time is crucial for real-world applications to achieve better human-computer interaction. However, training such models usually requires segment-level emotion labels, which are costly and may not be feasible. Assigning the overall label to all EEG segments within a trial can lead to inaccurate model training and degraded performance, as emotions evolve continuously. This highlights the need for models capable of learning from trial-wise emotion labels while capturing temporal dynamics of emotional responses within each segment because trial-wise post-stimulus labels are more accessible. To this end, we propose EmotionMIL, an end-to-end EEG-based emotion recognition framework that leverages recent advances in deep multiple instance learning (MIL). This framework enables robust emotion recognition from weakly labeled EEG signals and identifies the most prominent emotional responses. EmotionMIL captures the temporal dynamics of emotions using a retentive self-attention mechanism, which adaptively assigns weights to EEG segments based on their relevance in predicting the overall emotion label. A pseudo-bag augmentation strategy is also introduced to enhance the model's generalization ability by generating additional pseudo-bags from the original ones. Evaluated on three benchmark datasets—DEAP, DREAMER, and SEED—EmotionMIL outperforms state-of-the-art non-MIL and MIL models in both subject-dependent and subject-independent tasks, achieving superior accuracy and F1-score. Ablation study further validates the model design, while visualization results demonstrate that EmotionMIL effectively identifies both spatial EEG patterns and temporal emotional dynamics. These findings underscore EmotionMIL's potential for robust, interpretable emotion recognition, paving the way for real-world applications in emotion-aware systems. The code is available at https://github.com/yuty2009/emotionmil.

EEG (Electroencephalography)-based emotion recognition has emerged as a crucial area of research due to its potential applications in mental health, brain-computer interfaces (BCIs), and affective computing. However, the inherent variability in EEG signals across individuals, coupled with limited dataset sizes, significantly hinders the development of robust and generalizable emotion recognition models. To overcome these challenges, we propose the Sparse Bayesian Learning with Enhanced Covariance Alignment (SBLECA) algorithm. SBLECA formulates cross-subject emotion recognition as an end-to-end decoding problem, integrating spatiotemporal filtering and classification within a sparse Bayesian learning (SBL) framework. Crucially, SBLECA incorporates a novel covariance alignment technique to mitigate inter-subject variability in EEG patterns. Rigorous evaluations on two publicly available emotion datasets demonstrate that SBLECA consistently outperforms state-of-the-art methods. Furthermore, SBLECA offers valuable insights into the neural correlates of emotion through interpretable visualizations of learned spatial and temporal filters. SBLECA holds promise as a valuable EEG decoding tool to advance the development and translation of neurotechnologies and biomarkers for brain disorders. Code is available at https://github.com/EEGdecoding/Code-SBLECA .