DocumentCode
260362
Title
Analysis of Cognitive Load -- Importance of EEG Channel Selection for Low Resolution Commercial EEG Devices
Author
Sinha, Aniruddha ; Chatterjee, Debatri ; Das, Diptesh ; Sinharay, Arijit
Author_Institution
Innovation Labs., Tata Consultancy Services Ltd., Kolkata, India
fYear
2014
fDate
10-12 Nov. 2014
Firstpage
341
Lastpage
348
Abstract
Measurement of cognitive load using brain signalsis an important area of research in human behavior and psychology. Recently, there have been attempts to use low cost, commercially available Electroencephalogram (EEG) devices for the analysis of the cognitive load. Due to the reduced number of leads, these low resolution devices pose major challenges in signal processing as well as in feature extraction. In this paper, we investigate the significant leads or channels that are useful for the analysis of the cognitive load. We use a standard matching test and n-back memory test imparting low and high cognitive loads respectively. The investigation is based on the analysis of variance (ANOVA) of Alpha and Theta frequency band signals for various combinations of leads. Comparisons have been done between the previously reported leads and those obtained using a few feature selection algorithms. Results indicate that for a given stimulus, though the significant leads are very much dependent on the subjects, the leads corresponding to the left frontal lobe and right parieto-occipital lobe are in general most significant across majority of subjects for analysis of the cognitive load.
Keywords
biomedical equipment; cognition; electroencephalography; feature selection; medical signal processing; psychology; ANOVA; Alpha frequency band signals; EEG channel selection; Theta frequency band signals; analysis of variance; brain signals; cognitive load analysis; cognitive load measurement; feature extraction; feature selection algorithms; human behavior; left frontal lobe; low resolution commercial EEG devices; n-back memory test; psychology; right parieto-occipital lobe; signal processing; standard matching test; Analysis of variance; Artificial neural networks; Electroencephalography; Lead; Microwave integrated circuits; Support vector machines; Vectors; channel selection; cognitive load; commercial EEG; electroencephalogarphy; feature selection;
fLanguage
English
Publisher
ieee
Conference_Titel
Bioinformatics and Bioengineering (BIBE), 2014 IEEE International Conference on
Conference_Location
Boca Raton, FL
Type
conf
DOI
10.1109/BIBE.2014.28
Filename
7033604
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