Li Xing, Zhang Yikai, Peng Yong, Kong Wanzeng
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018 China.
Zhejiang Key Laboratory of Brain-Machine Collaborative Intelligence, Hangzhou, 310018 China.
Health Inf Sci Syst. 2024 Feb 17;12(1):9. doi: 10.1007/s13755-024-00271-0. eCollection 2024 Dec.
Electroencephalograph (EEG) has been a reliable data source for building brain-computer interface (BCI) systems; however, it is not reasonable to use the feature vector extracted from multiple EEG channels and frequency bands to perform recognition directly due to the two deficiencies. One is that EEG data is weak and non-stationary, which easily causes different EEG samples to have different quality. The other is that different feature dimensions corresponding to different brain regions and frequency bands have different correlations to a certain mental task, which is not sufficiently investigated. To this end, a Joint Sample and Feature importance Assessment (JSFA) model was proposed to simultaneously explore the different impacts of EEG samples and features in mental state recognition, in which the former is based on the self-paced learning technique while the latter is completed by the feature self-weighting technique. The efficacy of JSFA is extensively evaluated on two EEG data sets, i.e., SEED-IV and SEED-VIG. One is a classification task for emotion recognition and the other is a regression task for driving fatigue detection. Experimental results demonstrate that JSFA can effectively identify the importance of different EEG samples and features, leading to enhanced recognition performance of corresponding BCI systems.
脑电图（EEG）一直是构建脑机接口（BCI）系统的可靠数据源；然而，由于存在两个缺陷，直接使用从多个EEG通道和频段提取的特征向量进行识别是不合理的。一是EEG数据微弱且非平稳，这很容易导致不同的EEG样本具有不同的质量。另一个是对应于不同脑区和频段的不同特征维度与特定心理任务的相关性不同，对此尚未进行充分研究。为此，提出了一种联合样本与特征重要性评估（JSFA）模型，以同时探究EEG样本和特征在心理状态识别中的不同影响，其中前者基于自步学习技术，而后者通过特征自加权技术完成。在两个EEG数据集，即SEED-IV和SEED-VIG上广泛评估了JSFA的有效性。一个是用于情感识别的分类任务，另一个是用于驾驶疲劳检测的回归任务。实验结果表明，JSFA可以有效地识别不同EEG样本和特征的重要性，从而提高相应BCI系统的识别性能。
