The effects of layer-wise relevance propagation-based feature selection for EEG classification: a comparative study on multiple datasets

Frontiers in Human Neuroscience

Human Neuroscience

Soyeon Bak¹  Hyeonyeong Nam¹  Jun-Mo Kim¹  WooHyeok Choi¹  Tae-Eui Kam² 

[1] Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea;null;

关键词: brain-computer interface;    feature selection;    layer-wise relevance propagation;    motor imagery classification;    electroencephalography;    analysis;   

DOI  :  10.3389/fnhum.2023.1205881

 received in 2023-04-14, accepted in 2023-05-17,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionThe brain-computer interface (BCI) allows individuals to control external devices using their neural signals. One popular BCI paradigm is motor imagery (MI), which involves imagining movements to induce neural signals that can be decoded to control devices according to the user's intention. Electroencephalography (EEG) is frequently used for acquiring neural signals from the brain in the fields of MI-BCI due to its non-invasiveness and high temporal resolution. However, EEG signals can be affected by noise and artifacts, and patterns of EEG signals vary across different subjects. Therefore, selecting the most informative features is one of the essential processes to enhance classification performance in MI-BCI.MethodsIn this study, we design a layer-wise relevance propagation (LRP)-based feature selection method which can be easily integrated into deep learning (DL)-based models. We assess its effectiveness for reliable class-discriminative EEG feature selection on two different publicly available EEG datasets with various DL-based backbone models in the subject-dependent scenario.Results and discussionThe results show that LRP-based feature selection enhances the performance for MI classification on both datasets for all DL-based backbone models. Based on our analysis, we believe that it can broad its capability to different research domains.

【 授权许可】

Unknown   
Copyright © 2023 Nam, Kim, Choi, Bak and Kam.