Features Selection and Pattern Classification of Electroencephalography Motor Imagery Tasks of Right Hand

• Published in: Research Journal of Applied Sciences, Engineering and Technology Vol. 14; no. 10; pp. 372 - 379
• Main Authors: Ahmed A. Ibrahim, Mohammed I. Awad, Abdulwahab A. Alnaqi, Ann A. Abdel Kader, Farid A. Tolbah
• Format: Journal Article
• Language: English
• Published: Maxwell Science Publishing 15.10.2017
• Subjects: Brain Computer Interface (BCI); ERD; feature selection; MRMR; OPENBCI
• ISSN: 2040-7467; 2040-7459; 2040-7467
• DOI: 10.19026/rjaset.14.5129

This study presentsa Brain Computer Interface (BCI) approach to detect the motor intents of the disabled people with right hand amputation. Electroencephalography (EEG) Motor Imagery (MI)-based Brain Computer Interface (BCI) systems have been recently used to improve the quality of life of disabled people. However, to naturally trigger particular applications (i.e., upper limb prostheses), independent BCIs appeal further paradigms to involve realistic motor imagery tasks. This study proposes an approach to classifying imagined hand gesture tasks, including the water glass gesture and the index pointer gesture of the right hand using OPENBCI as a consumer-grade EEG acquisition device. For three subjects, the data recorded by OPENBCI were sampled with a sampling rate of 250 Hz. The Minimum Redundancy Maximum Relevance (MRMR) technique was implemented as a feature selection method along with the Support Vector Machine (SVM) algorithm for classification. By obtaining a maximum classification accuracy of 91.7%, the results showed the feasibility of such Brain Computer Interface systems to detect different motor imagery tasks for the right hand. Consequently, upper limb prostheses could be manipulated using the intended motor imagery tasks.