A new method for accurate detection of movement intention from single channel EEG for online BCI

Mahmoodi, M, Makkiabadi, B, Mahmoudi, M and Sanei, S ORCID: https://orcid.org/0000-0002-3437-2801, 2021. A new method for accurate detection of movement intention from single channel EEG for online BCI. Computer Methods and Programs in Biomedicine Update, 1: 100027. ISSN 2666-9900

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Abstract

Low frequency readiness potential (RP) is elicited in electroencephalograms (EEGs) as one intends to perform an
imagery (IMI) or real movement (RMI). While in most brain-computer-interface (BCI) applications the challenge is to classify RPs of different limbs from the given EEG trials, the objective of this study is fast and automatic detection of RPs from the entire single channel EEG signal. The proposed algorithm has two threshold blocks based on the nonlinear Teager-Kaiser energy operator (TEO) in the first block and the morphological properties of the RP waveform as constraints in the second block. The performance is strongly influenced by the abrupt energy changes due to transients and artefacts. As the major contribution, the proposed nonlinear convex optimization algorithm enables separation of transients from low frequency components by providing a fast thresholding mechanism. Application of the proposed method to Physionet RMI dataset, BCI competitionIV-1 IMI dataset and our own left hand movement datasets of healthy subjects led to true positive rates (TPRs) of 76.5±8.27%, 83.85±11.4%, and 81.1±5.23%, number of FPs/min of 2.4±1.07, 1.4±0.7, and 1.6±0.69 and accuracy rates of 85.4±3.83%, 90±3.56%, and 91.2±2.04%. Movement onset detection latency from our automatic RP detector was -384.9±296.5 ms.

As a conclusion, the proposed method outperforms state-of-the-art techniques using as low as single channel EEG making it suitable for real-time neuro-rehabilitation of paralyzed subjects suffering from stroke.

Item Type:	Journal article
Publication Title:	Computer Methods and Programs in Biomedicine Update
Creators:	Mahmoodi, M., Makkiabadi, B., Mahmoudi, M. and Sanei, S.
Publisher:	Elsevier BV
Date:	2021
Volume:	1
ISSN:	2666-9900
Identifiers:	Number Type 10.1016/j.cmpbup.2021.100027 DOI 1464364 Other
Rights:	©2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	13 Sep 2021 13:25
Last Modified:	13 Sep 2021 13:25
URI:	https://irep.ntu.ac.uk/id/eprint/44090

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