Classification of Motor Imagery Based EEG Signals
Leena R1, Ashok Kumar R2

1Leena R, Department of Information Science and Engineering at BMS College of Engineering, Bangalore, India.
2Dr Ashok Kumar R, Department of Information Science and Engineering at BMS College of Engineering, Bangalore, India. 

Manuscript received on 29 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 1012-1020 | Volume-8 Issue-9, July 2019 | Retrieval Number: H7084068819/19©BEIESP | DOI: 10.35940/ijitee.H7084.078919

Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Brain Computer Interface (BCI) enable the user to interact with system only through brain activity, usually measured by Electroencephalography (EEG). BCI systems additionally offers analysis of Motor Imagery EEG, which may be appeared, is a novel way of communication for the patients who are physically disabled. Motor Imagery based EEG data (left hand, right hand, or foot) movements supplied by BCI Competition IV dataset1. The data signals were band-pass filtered between 0.05 and 200Hz and sampled at 100Hz. The features extracted from the raw data with respect to time and frequency domain of required channels. Motor Imagery based EEG (left hand, right hand or foot) data classified using machine learning algorithm namely Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) for four normal human subjects (a, b, f, g). Analysis of motor imagery-based EEG data was studied using EEGLAB toolbox. Selected data are presented from raw data in channel data (scroll), representation of channel location in 2D and 3D form, channel spectra and maps and channel properties.
Index Terms: Brain Computer Interface, Back Propagation Neural Network, EEGLAB, Motor Imagery, Support Vector Machine.

Scope of the Article: Classification