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

The 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. Ele...

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Bibliographic Details
Published in:Frontiers in human neuroscience 2023-06, Vol.17, p.1205881-1205881
Main Authors: Nam, Hyeonyeong, Kim, Jun-Mo, Choi, WooHyeok, Bak, Soyeon, Kam, Tae-Eui
Format: Article
Language:English
Subjects:
analysis
Brain
Brain research
brain-computer interface
Classification
Computer applications
Datasets
Deep learning
EEG
Electrodes
Electroencephalography
Feature selection
Human Neuroscience
Implants
Invasiveness
layer-wise relevance propagation
Mental task performance
motor imagery classification
Neural networks
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Summary:The 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. In 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. The 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.
ISSN:1662-5161
1662-5161
DOI:10.3389/fnhum.2023.1205881