Motor Imagery EEG Classification based on Machine Learning Algorithm
Pradeep Rusiya1, N. S. Chaudhari2

1Pradeep Rusiya*, Research Scholar, SCSIT DAVV, Indore, MP, India.
2Dr. N. S. Chaudhari, Professor, IIT, Indore, India.
Manuscript received on February 28, 2020. | Revised Manuscript received on March 22, 2020. | Manuscript published on March 30, 2020. | PP: 5235-5246 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7754038620/2020©BEIESP | DOI: 10.35940/ijrte.F7754.038620

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

Abstract: The advancement of computer technology facilitates in the field of medical science for the analysis of complex Diseases related to the neurology. These technologies named as BCI (brain computer interface). The BCI is open area of research for physically challenge people such as paralyzed and amputees. The current technology of computer interface interest in EEG (electroencephalographic) for the analysis of signals for the predication of nervous system. The current trends of brain computer interface focus on process signal of EEG for the sense of human body behaviors and movement of nervous system, motor imagery and various senses. The gathered signal by the EEG is very noisy and predication and recognition of the motor imagery is typical. The minimization of noise upgrades the predication procedure and examination of signal behaviors. For the analysis of behaviors system utilized soft computing processing approach, for example, neural system, optimization techniques. The component extraction and feature selection are also major issue in motor imagery analysis for critical and complex disorder of human brain system.
Keywords: BCI, EEG, Classification, Soft Computing, Feature Extraction.
Scope of the Article: Classification.