Online Adaptive Filters to Classify Left and Right Hand Motor Imagery

Kais Belwafi; Ridha Djemal; Fakhreddine Ghaffari; Olivier Romain; Bouraoui Ouni; Sofien Gannouni

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Paper

Online Adaptive Filters to Classify Left and Right Hand Motor Imagery

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4 BIOSIGNALS: Smart-BIODEV, 335-339, 2016 , Rome, Italy

Authors: Kais Belwafi ¹ ; Ridha Djemal ² ; Fakhreddine Ghaffari ³ ; Olivier Romain ³ ; Bouraoui Ouni ⁴ and Sofien Gannouni ²

Affiliations: ¹ University Cergy-Pontoise ENSEA and University of Sousse, France ; ² King Saud University, Saudi Arabia ; ³ University Cergy-Pontoise ENSEA, France ; ⁴ University of Sousse, Tunisia

Keyword(s): Brain Computer Interface (BCI), EEG Filters, Motor Imagery (MI).

Abstract: Sensorimotor rhythms (SMRs) caused by motor imagery are key issues for subject with severe disabilities when controlling home devices. However, the development of such EEG-based control system requires a great effort to reach a high accuracy in real-time. Furthermore, BCIs have to confront with inter-individual variability, imposing to the parameters of the methods to be adapted to each subjects. In this paper, we propose a novel EEG-based solution to classify right and left hands(RH and LH) thoughts. Our approach integrates adaptive filtering techniques customized for each subject during the training phase to increase the accuracy of the proposed system. The validation of the proposed architecture is conducted using existing data sets provided by BCI-competition and then using our own on-line validation platform experienced with four subjects. Common Spatial Pattern (CSP) is used for feature extraction to extract features vector from µ and β bands. These features are classified by t he Linear Discriminant Analysis (LDA) algorithm. Our prototype integrates the Open-BCI acquisition system with 8 channels connected to Matlab environment in which we integrated all EEG signal processing including the adaptive filtering. The proposed system achieves 80.5% of classification accuracy, which makes approach a promising method to control an external devices based on the thought of LH and RH movement. (More)

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Paper citation in several formats:

Belwafi, K.; Djemal, R.; Ghaffari, F.; Romain, O.; Ouni, B. and Gannouni, S. (2016). Online Adaptive Filters to Classify Left and Right Hand Motor Imagery. In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Smart-BIODEV; ISBN 978-989-758-170-0; ISSN 2184-4305, SciTePress, pages 335-339. DOI: 10.5220/0005846503350339

@conference{smart-biodev16,
author={Kais Belwafi. and Ridha Djemal. and Fakhreddine Ghaffari. and Olivier Romain. and Bouraoui Ouni. and Sofien Gannouni.},
title={Online Adaptive Filters to Classify Left and Right Hand Motor Imagery},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Smart-BIODEV},
year={2016},
pages={335-339},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005846503350339},
isbn={978-989-758-170-0},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Smart-BIODEV
TI - Online Adaptive Filters to Classify Left and Right Hand Motor Imagery
SN - 978-989-758-170-0
IS - 2184-4305
AU - Belwafi, K.
AU - Djemal, R.
AU - Ghaffari, F.
AU - Romain, O.
AU - Ouni, B.
AU - Gannouni, S.
PY - 2016
SP - 335
EP - 339
DO - 10.5220/0005846503350339
PB - SciTePress