Effect of alpha range activity on SSVEP decoding in brain-computer interfaces

被引:0
作者
Zehra, Syeda R. [1 ]
Mu, Jing [1 ]
Burkitt, Anthony N. [1 ]
Grayden, David B. [1 ,2 ]
机构
[1] Univ Melbourne, Dept Biomed Engn, Melbourne, Vic 3010, Australia
[2] Univ Melbourne, Graeme Clark Inst, Melbourne, Vic 3010, Australia
来源
2023 45TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY, EMBC | 2023年
基金
澳大利亚研究理事会;
关键词
D O I
10.1109/EMBC40787.2023.10340956
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Brain-computer interfaces (BCIs) facilitate direct communication between the brain and external devices. For BCI technology to be commercialized for wide scale applications, BCIs should be accurate, efficient, and exhibit consistency in performance for a wide variety of users. A core challenge is the physiological and anatomical differences amongst people, which causes a high variability amongst participants in BCI studies. Hence, it becomes necessary to analyze the mechanisms causing this variability and address them by improving the decoding algorithms. In this paper, a publicly available steady-state visual evoked potential (SSVEP) dataset is analyzed to study the effect of SSVEP flicker on the endogenous alpha power and the subsequent overall effect on the classification accuracy of the participants. It was observed that the participants with classification accuracy below 95% showed increased alpha power in their brain activities. Incorrect prediction in the decoding algorithm was observed a maximum number of times when the predicted frequency was in the range 9-12 Hz. We conclude that frequencies between 9-12 Hz may result in below par performance in some participants when canonical correlation analysis is used for classification.
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页数:4
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