衰退
计算机科学
运动表象
人工智能
机器学习
个性化
脑-机接口
模式识别(心理学)
支持向量机
数据挖掘
算法
脑电图
心理学
解码方法
精神科
万维网
作者
Zitong Wan,Rui Yang,Mengjie Huang,Fuad E. Alsaadi,Muntasir Sheikh,Zidong Wang
标识
DOI:10.1016/j.compbiomed.2022.106267
摘要
Motor imagery (MI) aims to use brain imagination without actual body activities to support motor learning, and machine learning algorithms such as common spatial patterns (CSP) are proven effective in the analysis of MI signals. In the conventional machine learning-based approaches, there are two main difficulties in feature extraction and recognition of MI signals: high personalization and data fading. The high personalization problem is due to the multi-subject nature when collecting MI signals, and the data fading problem as a recurring issue in MI signal quality is first raised by us but is not widely discussed at present. Aiming to solve the above two mentioned problems, a cross-subject fading data classification approach with segment alignment is proposed to classify the fading data of one single target with the model trained with the normal data of multiple sources in this paper. he effectiveness of proposed method is verified via two experiments: a dataset-based experiment with the dataset from BCI Competition and a lab-based experiment designed and conducted by us. The experimental results obtained from both experiments show that the proposed method can obtain optimal classification performance effectively under different fading levels with data from different subjects.
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