通用脑机接口时代要来了？跨尺度脑基础模型CSBrain真正读懂脑信号

Core Insights - Brain-Computer Interface (BCI) is seen as the ultimate interface connecting human intelligence with artificial intelligence, with a focus on high-precision brain signal decoding to enable general AI models to understand complex brain activities [2] - Current BCI systems are limited to task-specific deep learning models, which lack generalizability and cross-task transfer capabilities, resulting in isolated "specialist" applications [2][3] - The introduction of a brain foundation model, CSBrain, aims to address these challenges by integrating cross-scale structural perception into the model design [5][6] Group 1: Challenges in Brain-Computer Interfaces - The BCI field has primarily relied on task-specific deep learning models, which perform well on specific datasets but struggle with adaptability to diverse brain signals [2] - The unique cross-scale spatiotemporal structure of brain signals presents challenges for traditional modeling paradigms, which fail to capture the inherent neural structure [3][5] Group 2: CSBrain Model Innovations - CSBrain introduces two core innovative modules: Cross-scale Spatiotemporal Tokenization (CST) and Structured Sparse Attention (SSA) [6][7] - CST extracts multi-scale temporal and spatial features from EEG signals, balancing neural representation capability and computational efficiency through a dimension allocation strategy [6] - SSA captures long-range temporal dependencies and models inter-region interactions while reducing computational complexity from O(N²) to O(N・k) [7] Group 3: Experimental Results and Performance - CSBrain was validated across 11 representative brain decoding tasks and 16 public datasets, achieving state-of-the-art performance with an average improvement of 3.35% over current models [12] - In high-challenge tasks, CSBrain showed a 5.2% accuracy improvement in motor imagery tasks and a 7.6% enhancement in epilepsy detection metrics [12] - The experimental results confirm the effectiveness of CSBrain's cross-scale modeling paradigm and pre-trained brain foundation model, supporting various BCI applications [12][14] Group 4: Future Prospects - As data scale and computational power increase, brain foundation models are expected to play a larger role in broader brain-AI integration scenarios, accelerating the application of next-generation brain-computer interfaces [14]