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Article

Neural Activities Classification of Human Inhibitory Control Using Hierarchical Model

by 1,2 and 1,2,3,4,*
1
Department of Biological Science and Technology, College of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan
2
Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Chiao Tung University, Hsinchu 300, Taiwan
3
Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan
4
Swartz Center for Computational Neuroscience, University of California San Diego, San Diego, CA 92093, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(17), 3791; https://doi.org/10.3390/s19173791
Received: 14 July 2019 / Revised: 18 August 2019 / Accepted: 29 August 2019 / Published: 1 September 2019
(This article belongs to the Special Issue Novel Approaches to EEG Signal Processing)
Human inhibitory control refers to the suppression of behavioral response in real environments, such as when driving a car or riding a motorcycle, playing a game and operating a machine. The P300 wave is a neural marker of human inhibitory control, and it can be used to recognize the symptoms of attention deficit hyperactivity disorder (ADHD) in human. In addition, the P300 neural marker can be considered as a stop command in the brain-computer interface (BCI) technologies. Therefore, the present study of electroencephalography (EEG) recognizes the mindset of human inhibition by observing the brain dynamics, like P300 wave in the frontal lobe, supplementary motor area, and in the right temporoparietal junction of the brain, all of them have been associated with response inhibition. Our work developed a hierarchical classification model to identify the neural activities of human inhibition. To accomplish this goal phase-locking value (PLV) method was used to select coupled brain regions related to inhibition because this method has demonstrated the best performance of the classification system. The PLVs were used with pattern recognition algorithms to classify a successful-stop versus a failed-stop in left-and right-hand inhibitions. The results demonstrate that quadratic discriminant analysis (QDA) yielded an average classification accuracy of 94.44%. These findings implicate the neural activities of human inhibition can be utilized as a stop command in BCI technologies, as well as to identify the symptoms of ADHD patients in clinical research. View Full-Text
Keywords: electroencephalography (EEG); ERP-P300; hierarchical classification model; phase locking value; brain-computer interface; human inhibitory control electroencephalography (EEG); ERP-P300; hierarchical classification model; phase locking value; brain-computer interface; human inhibitory control
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MDPI and ACS Style

Chikara, R.K.; Ko, L.-W. Neural Activities Classification of Human Inhibitory Control Using Hierarchical Model. Sensors 2019, 19, 3791. https://doi.org/10.3390/s19173791

AMA Style

Chikara RK, Ko L-W. Neural Activities Classification of Human Inhibitory Control Using Hierarchical Model. Sensors. 2019; 19(17):3791. https://doi.org/10.3390/s19173791

Chicago/Turabian Style

Chikara, Rupesh K., and Li-Wei Ko. 2019. "Neural Activities Classification of Human Inhibitory Control Using Hierarchical Model" Sensors 19, no. 17: 3791. https://doi.org/10.3390/s19173791

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