Home // COGNITIVE 2018, The Tenth International Conference on Advanced Cognitive Technologies and Applications // View article

Feature Extraction Process with an Adaptive Filter on Brain Signals Motion Intention Classification

Authors:
Luis Felipe Marin-Urias
J. Alejandro Vásquez-Santacruz
Rogelio de J. Portillo-Vélez
Félix O. Rivera-Hernández
Mario Castelán

Keywords: BCI; EEG; Motion Intention Classification; Motor Imagery; KNN.

Abstract:
Abstract—Identifying motor imagery from an electroencephalogram (EEG) has been researched from different perspectives and methods of classification. Translating a brain signal into a language understandable for machines relies on feature extraction techniques, which vary from working on the frequency domain to dealing with raw data. Using statistical information to classify motor imagery has shown encouraging results. In this paper we benefit from statistical approaches and propose a different perspective to boost results obtained through brain signals provided by a low cost EEG. Our motivation is based on the natural separability of classes exhibited by statistical indicators such as the mean and standard deviation. A special emphasis in our method is made on filtering data to subject readings in an adaptive manner, leading to a successful classification rate of 97%, outperforming Hjorth's mobility and complexity measure, a state-of the art technique used in EEG signal classification

Pages: 7 to 13

Copyright: Copyright (c) IARIA, 2018

Publication date: February 18, 2018

Published in: conference

ISSN: 2308-4197

ISBN: 978-1-61208-609-5

Location: Barcelona, Spain

Dates: from February 18, 2018 to February 22, 2018