Department of Health Technology and Informatics, Hong Kong Polytechnic University, Kowloon, Hong Kong.
IEEE Trans Neural Syst Rehabil Eng. 2011 Dec;19(6):617-27. doi: 10.1109/TNSRE.2011.2168542. Epub 2011 Oct 6.
The brain-computer interface (BCI) system has been developed to assist people with motor disability. To make the system more user-friendly, it is a challenge to reduce the electrode preparation time and have a good reliability. This study aims to find a minimal set of electrodes for an individual stroke subject for motor imagery to control an assistive device using functional electrical stimulation for 20 sessions with accuracy higher than 90%. The characteristics of this minimal electrode set were evaluated with two popular algorithms: Fisher's criterion and support-vector machine recursive feature elimination (SVM-RFE). The number of calibration sessions for channel selection required for robust control of these 20 sessions was also investigated. Five chronic stroke patients were recruited for the study. Our results suggested that the number of calibration sessions for channel selection did not have a significant effect on the classification accuracy. A performance index devised in this study showed that one training day with 12 electrodes using the SVM-RFE method achieved the best balance between the number of electrodes and accuracy in the 20-session data. Generally, 8-36 channels were required to maintain accuracy higher than 90% in 20 BCI training sessions for chronic stroke patients.
脑机接口（BCI）系统旨在帮助运动障碍患者。为了使系统更具用户友好性，减少电极准备时间并具有良好的可靠性是一项挑战。本研究旨在为个体中风患者找到一组最小的电极，用于运动想象，以使用功能性电刺激控制辅助设备，在 20 次会话中的准确性高于 90%。使用两种流行的算法：Fisher 准则和支持向量机递归特征消除（SVM-RFE）评估此最小电极集的特征。还研究了用于稳健控制这 20 个会话所需的通道选择校准会话的数量。招募了五名慢性中风患者进行研究。我们的结果表明，通道选择的校准会话数量对分类准确性没有显著影响。本研究设计的性能指标表明，使用 SVM-RFE 方法，每天训练 12 个电极的一个训练日可在 20 次 BCI 训练会话中在电极数量和准确性之间达到最佳平衡。通常，在 20 次慢性中风患者的 BCI 训练会话中，需要 8-36 个通道才能保持准确性高于 90%。
