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Calibration of off-the-shelf low-cost wearable EEG headset for application in field studies
Electroencephalography (EEG) is an integral tool in neurocognitive research worldwide. However, research grade EEG (32/64ch) systems are expensive and have cumbersome setup designed for clinical usage not suited for rugged environment of field-studies outside lab. Further, the long setup-time of EEG...
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Published in: | arXiv.org 2022-09 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Online Access: | Get full text |
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Summary: | Electroencephalography (EEG) is an integral tool in neurocognitive research worldwide. However, research grade EEG (32/64ch) systems are expensive and have cumbersome setup designed for clinical usage not suited for rugged environment of field-studies outside lab. Further, the long setup-time of EEG can be intimidating to those who are restless subjects e.g., children or elderly. Off-the-shelf, low-cost, dry EEG devices (LCDE) have been proposed as promising options. However, small number of electrodes in LCDE limit the detection scalp-area reducing the utility of an LCDE only to a specific set of cognitive tasks based on the brain lobe scanned. This paper proposes a novel methodology for calibration of an LCDE (e.g., DREEM Headband) to identify the specific class of cognitive tasks a LCDE is likely suited for. The methodology involves comparative analysis of the recorded data using LCDE with EEG-like signals simulated (using BESA Simulator software) by embedding dipole in a brain lobe. The simulated scalp activity and corresponding source analysis helps identify the approximate regions of brain scanned by the LCDE device. On further comparative analysis of brain lobes source localized using Brain Electrical Source Analysis (BESA software) helps characterize LCDE for cognitive tasks. The major findings conclude a list of psychological studies which can be performed using various LCDE, capable of replacing traditional and expensive wet EEG systems in both inside and outside-lab settings. |
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ISSN: | 2331-8422 |