提出滑动窗口预筛选方法，提升异步脑机接口的运动想象识别准确率。
Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces
- 采用滑动窗口预筛选与分类双模块，先区分静息态与运动想象
- 在4个数据集上平均准确率最高，比最佳基线提升约2%
- 适合需要实时控制的异步脑机接口应用，如神经康复设备
基于运动想象（MI）的脑机接口（BCI）可通过想象身体不同部位的动作直接控制外部设备。与以往使用固定长度脑电（EEG）试次进行解码的系统不同，异步BCI旨在无显式触发下检测用户意图。其挑战在于算法需在无触发条件下，首先区分静息状态与运动想象试次，再将运动想象正确分类。本文提出滑动窗口预筛选与分类（SWPC）方法，包含两个模块：预筛选模块用于从静息态中识别运动想象试次，分类模块完成具体任务分类。两个模块均通过监督学习和自监督学习联合训练，以优化特征提取器。在四个不同EEG数据集上的受试者内与跨受试者异步运动想象分类实验验证了SWPC的有效性：其平均分类准确率始终最高，且在每个数据集上均比最优现有基线高出约2%。
原文摘要 · Abstract (English)
Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of external devices through the imagined movements of various body parts. Unlike previous systems that used fixed-length EEG trials for MI decoding, asynchronous BCIs aim to detect the user's MI without explicit triggers. They are challenging to implement, because the algorithm needs to first distinguish between resting-states and MI trials, and then classify the MI trials into the correct task, all without any triggers. This paper proposes a sliding window prescreening and classification (SWPC) approach for MI-based asynchronous BCIs, which consists of two modules: a prescreening module to screen MI trials out of the resting-state, and a classification module for MI classification. Both modules are trained with supervised learning followed by self-supervised learning, which refines the feature extractors. Within-subject and cross-subject asynchronous MI classifications on four different EEG datasets validated the effectiveness of SWPC, i.e., it always achieved the highest average classification accuracy, and outperformed the best state-of-the-art baseline on each dataset by about 2%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。