脑-机接口
运动表象
计算机科学
人工智能
康复
线性判别分析
支持向量机
决策树
机器学习
物理医学与康复
特征提取
特征选择
人工神经网络
脑电图
医学
物理疗法
精神科
作者
Cihan Uyanik,Muhammad Ahmed Khan,Iris Brunner,John Paulin Hansen,Sadasivan Puthusserypady
标识
DOI:10.1109/embc48229.2022.9871600
摘要
Stroke is a life-changing event that can affect the survivors' physical, cognitive and emotional state. Stroke care focuses on helping the survivors to regain their strength; recover as much functionality as possible and return to independent living through rehabilitation therapies. Automated training protocols have been reported to improve the efficiency of the rehabilitation process. These protocols also decrease the dependency of the process on a professional trainer. Brain-Computer Interface (BCI) based systems are examples of such systems where they make use of the motor imagery (MI) based electroencephalogram (EEG) signals to drive the rehabilitation protocols. In this paper, we have proposed the use of well-known machine learning (ML) algorithms, such as, the decision tree (DT), Naive Bayesian (NB), linear discriminant analysis (LDA), support vector machine (SVM), ensemble learning classifier (ELC), and artificial neural network (ANN) for MI wrist dorsiflexion prediction in a BCI assisted stroke rehabilitation study conducted on eleven stroke survivors with either the left or right paresis. The doubling sub-band selection filter bank common spatial pattern (DSBS-FBCSP) has been proposed as feature extractor and it is observed that the ANN based classifier produces the best results.
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