Brain-computer interface (BCI) speech decoding has emerged as a promising tool for assisting individuals with speech impairments. In this context, the integration of electroencephalography (EEG) and electromyography (EMG) signals offers strong potential for enhancing decoding performance. Mandarin tone classification presents particular challenges, as tonal variations convey distinct meanings even when phonemes remain identical. In this study, we propose a novel cross-subject multimodal BCI decoding framework that fuses EEG and EMG signals to classify four Mandarin tones under both audible and silent speech conditions. Inspired by the cooperative mechanisms of neural and muscular systems in speech production, our neural decoding architecture combines spatial-temporal feature extraction branches with a cross-attention fusion mechanism, enabling informative interaction between modalities. We further incorporate domain-adversarial training to improve cross-subject generalization. We collected 4,800 EEG trials and 4,800 EMG trials from 10 participants using only twenty EEG and five EMG channels, demonstrating the feasibility of minimal-channel decoding. Despite employing lightweight modules, our model outperforms state-of-the-art baselines across all conditions, achieving average classification accuracies of 87.83\% for audible speech and 88.08\% for silent speech. In cross-subject evaluations, it still maintains strong performance with accuracies of 83.27\% and 85.10\% for audible and silent speech, respectively. We further conduct ablation studies to validate the effectiveness of each component. Our findings suggest that tone-level decoding with minimal EEG-EMG channels is feasible and potentially generalizable across subjects, contributing to the development of practical BCI applications.

Visual impairment is a common condition worldwide, and cortical electrical stimulation is one of the approaches to aid in visual restoration. However, existing methods suffer from limited precision, flexibility, and generalization in generating the desired visual perception. In this paper, we propose a novel deep learning-based algorithm for cortical electrical stimulation, named ``MindSight," aimed at enhancing the clarity and accuracy of induced visual perceptions. Our framework introduces three key innovations: (1) A differentiable biophysical model simulating cortical state transitions under electrical stimulation, enabling end-to-end training; (2) A dual-path training architecture combining neural decoding fidelity with phosphene simulation constraints; (3) An attention-guided background gated network for input filtration and, a multi-channel activation constraint to ensure the effectiveness of electrical stimulation. We validated our approach through novel experiments with macaque monkeys, demonstrating superior performance in visual perception tasks. These results highlight the potential of our approach in assisting individuals with visual impairments.

Multi-Tasks decoding from electroencephalogram (EEG) signals is of great value for brain-computer interaction (BCI) applications in natural scenes. Although most existing studies have concentrated on decoding significantly different multi-tasks, a few studies explored the various cognitive processes that individuals may exhibit when elicited by the same stimulus. However, in practice, the diversity and complexity of individuals' cognitive responses when faced with the same stimulus cannot be ignored. In this paper, we aimed to construct a paradigm of the various cognitive processes elicited by the same stimulus, explore the neural signatures, and decode the multiple cognitive processes from EEG signals. Experimental results show that the regularized linear discriminant analysis (RLDA) classifier with event-related spectral perturbation (ERSP) features yielded a decoding accuracy of 96.30%±3.40% for the multi-cognitions. In-Depth research on signatures and decoding of various cognitive processes elicited by the same stimulus is of great significance for improving the naturalness and intelligence of BCI.