Multivariate Fast Iterative Filtering Based Automated System for Grasp Motor Imagery Identification Using EEG Signals

Abstract

One of the most crucial use of hands in daily life is grasping. Sometimes people with neuromuscular disorders become incapable of moving their hands. This article proposes a grasp motor imagery identification approach based on multivariate fast iterative filtering (MFIF). The proposed methodology involves the selection of relevant electroencephalogram (EEG) channels based on the neurophysiology of the brain. The selected EEG channels have been decomposed into five components using MFIF. Information potential based features are extracted from the decomposed EEG components. The extracted features are smoothed using a moving average filter. The smoothed features are classified using the k-nearest neighbors classifier. The cross-subject classification accuracy, precision, and F1-score of 98.25%, 98.31%, and 98.24%, respectively, is obtained. While the average classification accuracy, precision and F1-score for multiple subjects is 98.43%, 98.62%, and 98.41%, respectively. The proposed methodology can be used for the development of a low cost EEG based grasp identification system.

Keywords:

• Grasp motor imagery
• electroencephalogram
• information potential
• iterative filtering
• motor imagery

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Shivam Sharma

Shivam Sharma is currently pursuing PhD degree from the DIAT, DRDO, Pune, India. He received his MTech degree from the DIAT, DRDO, Pune, India in Signal Processing and Communication. His area of research includes signal processing, machine learning, biomedical signals like EEG, ECoG, EMG, human computer interaction, etc.

Aakash Shedsale

Aakash Shedsale received MTech degree in Electronics and Communication Engineering from Defence Institute of Advanced Technology, Pune. Presently, he is working towards his PhD at the Department of Electrical Communication Engineering at Indian Institute of Science, Bangalore, India.

Rishi Raj Sharma

Rishi Raj Sharma completed MTech from ABV-IIITM, Gwalior, India and PhD from IIT-Indore, India, respectively. Currently, he is an Assistant professor at DIAT, (DRDO), India. His area of research cover signal processing, medical robotics, BCI, HCI, electronic-warfare, UAV, and automotive-radar. He received IET Premium Award-2019 and 2020 from IET-UK.