AUTOMATIC ELECTROENCEPHALOGRAPHIC SOURCE SEPARATION STRATEGIES FOR SEIZURE PREDICTION APPLICATION

Electroencephalography (EEG) is a common clinical method of recording the electrical activity of the brain. EEG can record High-Frequency Oscillations ( >80 HZ), which carry appropriate information regarding Epilepsy. High-Frequency Oscillations (HFO) serve as a potential biomarker for Epileptogenesis. EEG signals are often prone to artifact corruptions, which mislead the clinicians by the incorrect signal interpretations. Therefore, automatic artifact removal approach is a key phase in all the Brain-Computer Interface (BCI) applications. In this work, the automatic artifact identification and removal strategy without consuming any supplementary reference channel using two different approaches is developed and discussed. A proficient novel Modified Online Bi-Conjugate Gradient-based Independent Component Analysis (MOBICA) is developed. An efficient threshold-based peak detection and removal strategy, Sparsity-based Artifact Removal Technique (SART) is constructed, where Principle Component Analysis (PCA) is replaced with Singular Value Decomposition (SVD) of the K-SVD algorithm. Both the proposed models are evaluated on two different datasets like CHB-MIT and SRM scalp data recordings. Both the MOBICA and SART algorithms removed the artifactual component parting the intact EEG source component. Finally, the performance of the proposed agenda is compared with the conventional approaches. Our MOBICA and SART algorithms remove the artifactual component parting the intact EEG source component. Empirical results of SART on CHB-MIT and SRM databases of 52 EEG recordings outperform MOBICA maintaining least Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and high Signal to Artifact Ratio (SAR), Mutual Information (MI), and Correlation Coefficient (CC). The proposed strategy vows to be a promising solution for artifact removal in the clinical use of EEG signals and in BCI applications.

Automated summary

This paper discusses an automatic artifact identification and removal strategy in EEG signals. Two different approaches are developed and compared with conventional methods. The proposed strategy shows promising performance in artifact removal, as demonstrated through empirical results and comparison with traditional approaches.