Cognitive capability identification in performing mental tasks using EEG-based coherence

Sandeep Kumar (), Shushobhan Shekhar () and Prabhakar Agarwal ()
Additional contact information
Sandeep Kumar: National Institute of Technology
Shushobhan Shekhar: National Institute of Technology
Prabhakar Agarwal: National Institute of Technology

International Journal of System Assurance Engineering and Management, 2023, vol. 14, issue 1, No 29, 334-342

Abstract: Abstract Previous research in the field of cognitive science clearly emphasizes the importance of coherence in language processing and the analysis of mental tasks. In this paper, electroencephalography (EEG)-based coherence between different pairs of electrodes has been used for the classification of mental arithmetic capability for different subjects. EEG signals were obtained using 19 electrodes when 36 subjects performed mental arithmetic operations. These EEG signals were denoised using wavelet-based techniques. Then the signals were decomposed into alpha, beta, gamma, delta, and theta frequency bands. The magnitude squared coherence in all the individual frequency bands for different pairs of electrodes was calculated. The high coherence was prevalent in the anterior frontal and frontal electrodes. It can also be seen from this work that the alpha band provides maximum coherence. The coherence features were classified in the alpha band using a non-linear support vector machine and 97.6% accuracy was achieved. To reinforce our findings, the work has been compared in a concurrent framework using statistical features such as mean, variance, and skewness. The classification accuracies were 72.3%, 61.4%, and 53.9% respectively using the above three features respectively. This study shows the effectiveness of coherence features by providing additional insights regarding the involvement of different brain areas in cognitive processes.

Keywords: Coherence; Brain-computer interface; Electroencephalogram (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-022-01799-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:14:y:2023:i:1:d:10.1007_s13198-022-01799-8

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-022-01799-8

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().