Decoding human brain activity from electroencephalography (EEG) signals is a central challenge at the intersection of neuroscience and artificial intelligence, enabling diverse applications in mental state assessment, clinical monitoring, and human-machine interaction. Recent efforts have extensively explored EEG-based brain foundation models for generalized brain decoding, employing large-scale training on multiple datasets. However, most of these attempts struggle with generalizability and fail to achieve satisfactory performance without task-specific tuning due to pronounced inherent heterogeneity among decoding tasks. To address these challenges, we present UniMind, a general-purpose EEG foundation model for unified multi-task brain decoding by uniquely unleashing the power of large language models to comprehend complex neural patterns. UniMind offers several advantages. First, we design a Neuro-Language Connector to bridge the modality gap between neural signals and large language models, distilling and transforming the spatiotemporal neural patterns of EEG data into representations understandable by language models. Second, a Task-aware Query Selection module is proposed to inject task-awareness into the cross-modal alignment by dynamically generating task-adaptive query tokens, enabling learning of task-relevant neural patterns across diverse tasks. Extensive experiments across ten datasets demonstrate that UniMind substantially outperforms state-of-the-art multi-task decoding models, with an average gain of 12 percent, while also offering valuable neuroscientific insights into neural functional correlations across tasks. The code is available at https://github.com/kaleidoyao/UniMind.

翻译：从脑电图（EEG）信号解码人类大脑活动是神经科学与人工智能交叉领域的核心挑战，可推动心理状态评估、临床监测和人机交互等多种应用。近期研究已广泛探索基于EEG的脑基础模型，通过多数据集大规模训练实现通用脑解码。然而，由于解码任务间存在显著的内在异质性，多数尝试泛化能力不足，且未经任务特定微调时难以达到满意性能。针对此问题，我们提出UniMind——一种通用型EEG基础模型，通过独特释放大语言模型理解复杂神经模式的能力实现统一多任务脑解码。UniMind具有以下优势：首先，我们设计神经-语言连接器以弥合神经信号与大语言模型间的模态鸿沟，将EEG数据的时空神经模式提炼并转化为语言模型可理解的表征；其次，提出任务感知查询选择模块，通过动态生成任务自适应查询标记将任务感知注入跨模态对齐过程，使模型能跨不同任务学习与任务相关的神经模式。在十个数据集上的广泛实验表明，UniMind显著优于最先进的多任务解码模型，平均性能提升12%，同时为跨任务神经功能相关性提供了重要的神经科学洞见。代码开源地址：https://github.com/kaleidoyao/UniMind。