By Topic

Optimized adaptive neuro-fuzzy inference system for motor imagery EEG signals classifications

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Kwang-Eun Ko ; Sch. of Electr. & Electron. Eng., Chung-Ang Univ., Seoul, South Korea ; Kwee-Bo Sim

A motor imagery related electroencephalogram (EEG) feature classification technique through the time-series prediction based on the adaptive neuro-fuzzy inference system (ANFIS) is presented for neural computation applications. We descries a method for classification of EEG using optimized ANFIS and the proposed method was focus on the validation of the Harmony Search algorithm based optimization procedure for ANFIS. The motor imagery related EEG signals recorded from subjects performing left, right hand and foots motor imagery. From this signal, features obtained from the difference of multiresolution fractal feature vectors between the predicted and actual signals by using time-series prediction technique. In order to optimize the ANFIS, Harmony Search algorithm is sufficiently adaptable to allow incorporation of other training techniques like feed-forward and gradient descents. In this paper, the proposed technique is employed to simulate the three types of motor imagery (left, right hand, right foots) EEG signals evaluation data which were used as input patterns of the optimized ANFIS classifier.

Published in:

Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on  (Volume:2 )

Date of Conference:

26-28 July 2011