An EEG pre-processing technique for the fast recognition of motor imagery movements

TL;DR

This paper introduces a new EEG pre-processing method that accelerates the recognition of motor imagery movements, enabling real-time control of rehabilitation devices based on brain signals.

Contribution

The proposed technique improves EEG signal processing speed for motor imagery recognition, facilitating real-time brain-computer interface applications.

Findings

Effective noise removal from EEG signals.

Fast classification of motor imagery using SVM.

Potential for real-time rehabilitation device control.

Abstract

In this paper we propose a new pre-processing technique of Electroencephalography (EEG) signals produced by motor imagery movements. This technique results to an accelerated determination of the imagery movement and the command to carry it out, within the time limits imposed by the requirements of brain-based real-time control of rehabilitation devices, making thus feasible to drive these devices according to patient's will. Based on event related desynchronization and synchronization (ERD/ERS) of motor imagery, the received patient signal is first subjected to the removal of environmental, system and interference noise which correspond to normal human activities such as eye-blinking and cardiac motion. Next, power and energy features of the processed signal are compared with the same features of classified signals from an available database and the class to which the processed signal belongs, is identified. The database classification is done off-line by using the SVM algorithm.