Comparison of Classification Accuracies Between Different Brain Areas During a Two-Class Motor Imagery in a fNIRS Based BCI

被引：1

作者：

Moslehi, Amir H. ^{[1
]}

Davies, T. Claire ^{[1
]}

机构：

[1] Queens Univ, Dept Mech & Mat Engn, Kingston, ON, Canada

来源：

2021 10TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER) | 2021年

基金：

加拿大自然科学与工程研究理事会;

关键词：

ACTIVATION;

D O I：

10.1109/NER49283.2021.9441376

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This study examined functional near infrared spectroscopy (fNIRS) data from 29 participants to determine whether the classification accuracy associated with a motor imagery task depended on different areas of the brain. The fNIRS was used to measure concentration changes of oxy- (HbO) and deoxyhemoglobin (HbR). The averages of HbO (mHbO) and HbR (mHbR) were used as features, and linear discriminant analysis (LDA) and support vector machine (SVM) were used as classifiers. The results showed significantly higher classification accuracies for the motor cortex than both frontal and occipital areas, but less accuracy compared to all channels. Furthermore, while SVM resulted in higher accuracy than LDA, mHbO and mHbR led to similar accuracies.

引用

页码：702 / 705

页数：4

共 45 条

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