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

• Published in: Frontiers in Human Neuroscience Vol. 17; p. 1205881
• Main Authors: Nam, Hyeonyeong, Kim, Jun-Mo, Choi, WooHyeok, Bak, Soyeon, Kam, Tae-Eui
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media SA 05.06.2023; Frontiers Research Foundation; Frontiers Media S.A
• 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; Neurosciences. Biological psychiatry. Neuropsychiatry; RC321-571; electroencephalography; motor imagery classification; layer-wise relevance propagation; analysis; brain-computer interface; feature selection
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2023.1205881

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.