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
Link To Document :
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