Classification of EEG signals to identify variations in attention during motor task execution
File(s)
Accepted version
Author(s)
Aliakbaryhosseinabadi, S
Kamavuako, EN
Jiang, N
Farina, D
Mrachacz-Kersting, N
Type
Journal Article
Abstract
Background
Brain-computer interface (BCI) systems in neuro-rehabilitation use brain signals to control external devices. User status such as attention affects BCI performance; thus detecting the user’s attention drift due to internal or external factors is essential for high detection accuracy.
New method
An auditory oddball task was applied to divert the users’ attention during a simple ankle dorsiflexion movement. Electroencephalogram signals were recorded from eighteen channels. Temporal and time-frequency features were projected to a lower dimension space and used to analyze the effect of two attention levels on motor tasks in each participant. Then, a global feature distribution was constructed with the projected time-frequency features of all participants from all channels and applied for attention classification during motor movement execution.
Results
Time-frequency features led to significantly better classification results with respect to the temporal features, particularly for electrodes located over the motor cortex. Motor cortex channels had a higher accuracy in comparison to other channels in the global discrimination of attention level.
Comparing with existing methods
Previous methods have used the attention to a task to drive external devices, such as the P300 speller. However, here we focus for the first time on the effect of attention drift while performing a motor task.
Conclusions
It is possible to explore user’s attention variation when performing motor tasks in synchronous BCI systems with time-frequency features. This is the first step towards an adaptive real-time BCI with an integrated function to reveal attention shifts from the motor task.
Brain-computer interface (BCI) systems in neuro-rehabilitation use brain signals to control external devices. User status such as attention affects BCI performance; thus detecting the user’s attention drift due to internal or external factors is essential for high detection accuracy.
New method
An auditory oddball task was applied to divert the users’ attention during a simple ankle dorsiflexion movement. Electroencephalogram signals were recorded from eighteen channels. Temporal and time-frequency features were projected to a lower dimension space and used to analyze the effect of two attention levels on motor tasks in each participant. Then, a global feature distribution was constructed with the projected time-frequency features of all participants from all channels and applied for attention classification during motor movement execution.
Results
Time-frequency features led to significantly better classification results with respect to the temporal features, particularly for electrodes located over the motor cortex. Motor cortex channels had a higher accuracy in comparison to other channels in the global discrimination of attention level.
Comparing with existing methods
Previous methods have used the attention to a task to drive external devices, such as the P300 speller. However, here we focus for the first time on the effect of attention drift while performing a motor task.
Conclusions
It is possible to explore user’s attention variation when performing motor tasks in synchronous BCI systems with time-frequency features. This is the first step towards an adaptive real-time BCI with an integrated function to reveal attention shifts from the motor task.
Date Issued
2017-04-18
Date Acceptance
2017-04-18
Citation
Journal of Neuroscience Methods, 2017, 284, pp.27-34
ISSN
1872-678X
Publisher
Elsevier
Start Page
27
End Page
34
Journal / Book Title
Journal of Neuroscience Methods
Volume
284
Copyright Statement
© 2017 Elsevier B.V. All rights reserved. . This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Subjects
Neurology & Neurosurgery
1109 Neurosciences
1702 Cognitive Science
Publication Status
Published