Department of Biomedical Engineering, Tianjin University, Tianjin, China.
PLoS One. 2013 Apr 11;8(4):e60608. doi: 10.1371/journal.pone.0060608. Print 2013.
Most EEG-based brain-computer interface (BCI) paradigms include specific electrode positions. As the structures and activities of the brain vary with each individual, contributing channels should be chosen based on original records of BCIs. Phase measurement is an important approach in EEG analyses, but seldom used for channel selections. In this paper, the phase locking and concentrating value-based recursive feature elimination approach (PLCV-RFE) is proposed to produce robust-EEG channel selections in a P300 speller. The PLCV-RFE, deriving from the phase resetting mechanism, measures the phase relation between EEGs and ranks channels by the recursive strategy. Data recorded from 32 electrodes on 9 subjects are used to evaluate the proposed method. The results show that the PLCV-RFE substantially reduces channel sets and improves recognition accuracies significantly. Moreover, compared with other state-of-the-art feature selection methods (SSNRSF and SVM-RFE), the PLCV-RFE achieves better performance. Thus the phase measurement is available in the channel selection of BCI and it may be an evidence to indirectly support that phase resetting is at least one reason for ERP generations.
大多数基于脑电图的脑机接口 (BCI) 范式都包括特定的电极位置。由于大脑的结构和活动因人而异，因此应该根据 BCI 的原始记录选择贡献通道。相位测量是 EEG 分析中的一种重要方法，但很少用于通道选择。在本文中，提出了基于相位锁定和集中值的递归特征消除方法 (PLCV-RFE)，用于在 P300 拼写器中进行稳健的 EEG 通道选择。PLCV-RFE 源自相位重设机制，通过递归策略测量 EEG 之间的相位关系并对通道进行排序。使用 9 名受试者的 32 个电极记录的数据来评估所提出的方法。结果表明，PLCV-RFE 大大减少了通道集并显著提高了识别精度。此外，与其他先进的特征选择方法 (SSNRSF 和 SVM-RFE) 相比，PLCV-RFE 具有更好的性能。因此，相位测量可用于 BCI 的通道选择，并且它可能是间接支持相位重设是 ERP 产生的至少一个原因的证据。
