做脑机接口或神经信息处理的团队，微状态分词器直接提升了多任务泛化能力，比调时频特征更省力，值得在自家数据集上试试。
该研究提出一种基于微状态的通用脑电图（EEG）表征学习方法。传统EEG分析依赖时域或频域特征，而微状态作为脑活动在微观时间尺度上的基本构建块，能更简洁地编码信号。研究者从大规模医疗EEG数据集中通过聚类构建了通用微状态分词器，将连续EEG信号转化为离散微状态序列。该分词器在睡眠分期、情绪识别和运动想象分类等多个下游任务中表现优于传统时频域特征，且在不同模型上均有一致提升。进一步分析表明，微状态方法具有更强的可解释性和可扩展性，为认知神经科学和临床研究开辟了新应用。
Atoms of Thought: Universal EEG Representation Learning with Microstates
Learning universal representations from electroencephalogram (EEG) signals is a cutting-edge approach in the field of neuroinformatics and brain-computer interfaces (BCIs). Conventionally, EEG is treated as a multivariate temporal signal, where time- or frequency-domain features are extracted for representation learning. This paper investigates a simple yet effective EEG representation, i.e., microstates. Microstates represent the building blocks of brain activity patterns at a microscopic time scale. We build a universal microstate tokenizer from a large medical EEG dataset by clustering continuous EEG signals into sequences of discrete microstates. The microstate tokenizer is then adopted universally across a series of downstream tasks, including sleep staging, emotion recognition, and motor imagery classification. Experimental results show that EEG representation learning with microstates outperforms traditional time-domain and frequency-domain features under different models and across different tasks. Further analysis shows that microstates offer greater interpretability and scalability, thereby opening up applications in both cognitive neuroscience and clinical research.