EEG-based Epilepsy Detection Using Robust Feature Learning Model with Manhattan Distance and L1 Regularization

The automatic detection of epilepsy based on electroencephalography (EEG) has been proven effective under feature learning models. However, the practical implementation often encounters challenges from noise contamination presented in EEG signals. To address issues related to noise interference, we proposed a robust feature learning model for EEG-based epilepsy detection using Manhattan distance and L1 regularization. Specifically, we introduced Manhattan distance to construct the weight of the L1 regularization term in LASSO and obtained epilepsy-related information in the spectrum components of the original EEG signals and the differentiated signals through the LASSO-based feature selection. We verified the performance of our proposed model using the public EEG dataset. The model achieved the best performance, outperforming competing models. In addition, we tested the performance under noise interference by simulating EEG signal noise, indicating that our model is robust in EEG-based epilepsy detection.