Feature Selection Based on Layer-Wise Relevance Propagation for EEG-based MI classification

Nam, Hyeonyeong; Kim, Junmo; Kam, Tae-Eui

doi:10.1109/bci57258.2023.10078676

Cited by 3 publications

(1 citation statement)

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“…Our recent work (Nam et al, 2023 ) has also conducted feature selection for EEG-based motor imagery classification based on Layer-wise relevance propagation (LRP) (Bach et al, 2015 ). LRP is a method designed to analyze and understand how a model processes information or makes decisions by providing the importance of input features through the decomposition of the prediction output backward (Bach et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

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

Nam

Kim

Choi

et al. 2023

Front. Hum. Neurosci.

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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.

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Section: Introductionmentioning

confidence: 99%