Reworked Second Order Blind Identification and Support Vector Machine technique towards imagery movement identification from EEG signals

TL;DR

This paper introduces a novel approach combining a reworked SOBI algorithm for noise removal and SVM for classifying EEG signals to improve imagery movement detection in EEG-based brain-computer interfaces.

Contribution

It presents a new method integrating reworked SOBI and SVM techniques for enhanced noise removal and classification of EEG signals during imagery movements.

Findings

Improved noise reduction in EEG signals.

Enhanced accuracy in imagery movement classification.

Effective separation of ERS/ERD events from noise.

Abstract

During imagery motor movements tasks, the so called mu and beta event related desynchronization (ERD) and synchronization (ERS) are taking place, allowing us to determine human patient imagery movement. However, initial recordings of electroencephalography (EEG) signals contain system and environmental noise as well as interference that must be ejected in order to separate the ERS/ERD events from the rest of the signal. This paper presents a new technique based on a reworked Second Order Blind Identification (SOBI) algorithm for noise removal while imagery movement classification is implemented using Support Vector Machine (SVM) technique.