Detection of movement related cortical potentials from EEG using constrained ICA for brain-computer interface applications
File(s)
Published version
Author(s)
Karimi, F
Kofman, J
Mrachacz-Kersting, N
Farina, D
Jiang, N
Type
Journal Article
Abstract
The movement related cortical potential (MRCP), a slow cortical potential from the scalp electroencephalogram (EEG), has been used in real-time brain-computer-interface (BCI) systems designed for neurorehabilitation. Detecting MPCPs in real time with high accuracy and low latency is essential in these applications. In this study, we propose a new MRCP detection method based on constrained independent component analysis (cICA). The method was tested for MRCP detection during executed and imagined ankle dorsiflexion of 24 healthy participants, and compared with four commonly used spatial filters for MRCP detection in an offline experiment. The effect of cICA and the compared spatial filters on the morphology of the extracted MRCP was evaluated by two indices quantifying the signal-to-noise ratio and variability of the extracted MRCP. The performance of the filters for detection was then directly compared for accuracy and latency. The latency obtained with cICA (-34 ± 29 ms motor execution (ME) and 28 ± 16 ms for motor imagery (MI) dataset) was significantly smaller than with all other spatial filters. Moreover, cICA resulted in greater true positive rates (87.11 ± 11.73 for ME and 86.66 ± 6.96 for MI dataset) and lower false positive rates (20.69 ± 13.68 for ME and 19.31 ± 12.60 for MI dataset) compared to the other methods. These results confirm the superiority of cICA in MRCP detection with respect to previously proposed EEG filtering approaches.
Date Issued
2017-06-30
Date Acceptance
2017-06-07
Citation
Frontiers in Neuroscience, 2017, 11
ISSN
1662-4548
Publisher
Frontiers
Journal / Book Title
Frontiers in Neuroscience
Volume
11
Copyright Statement
© 2017 Karimi, Kofman, Mrachacz-Kersting, Farina and Jiang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Subjects
1109 Neurosciences
1702 Cognitive Science
Publication Status
Published
Article Number
356