TY - JOUR
T1 - Design and Development of Low-cost Wearable Electroencephalograms (EEG) Headset
AU - Muhammad, Riaz
AU - Ali, Ahmed
AU - Anwar, M. Abid
AU - Soomro, Toufique Ahmed
AU - Alshorman, Omar
AU - Alshahrani, Adel
AU - Masadeh, Mahmoud
AU - Ashraf, Ghulam Md
AU - Ali, Naif H.
AU - Irfan, Muhammad
AU - Alexiou, Athanasios
N1 - Publisher Copyright:
© 2023, Tech Science Press. All rights reserved.
PY - 2023
Y1 - 2023
N2 - Electroencephalogram (EEG) is a method of capturing the electrophysiological signal of the brain. An EEG headset is a wearable device that records electrophysiological data from the brain. This paper presents the design and fabrication of a customized low-cost Electroencephalogram (EEG) headset based on the open-source OpenBCI Ultracortex Mark IV system. The electrode placement locations are modified under a 10–20 standard system. The fabricated headset is then compared to commercially available headsets based on the following parameters: affordability, accessibility, noise, signal quality, and cost. First, the data is recorded from 20 subjects who used the EEG Headset, and signals were recorded. Secondly, the participants marked the accuracy, set up time, participant comfort, and participant perceived ease of set-up on a scale of 1 to 7 (7 being excellent). Thirdly, the self-designed EEG headband is used by 5 participants for slide changing. The raw EEG signal is decomposed into a series of band signals using discrete wavelet transform (DWT). Lastly, these findings have been compared to previously reported studies. We concluded that when used for slide-changing control, our self-designed EEG headband had an accuracy of 82.0 percent. We also concluded from the results that our headset performed well on the cost-effectiveness scale, had a reduced setup time of 2 ± 0.5 min (the shortest among all being compared), and demonstrated greater ease of use.
AB - Electroencephalogram (EEG) is a method of capturing the electrophysiological signal of the brain. An EEG headset is a wearable device that records electrophysiological data from the brain. This paper presents the design and fabrication of a customized low-cost Electroencephalogram (EEG) headset based on the open-source OpenBCI Ultracortex Mark IV system. The electrode placement locations are modified under a 10–20 standard system. The fabricated headset is then compared to commercially available headsets based on the following parameters: affordability, accessibility, noise, signal quality, and cost. First, the data is recorded from 20 subjects who used the EEG Headset, and signals were recorded. Secondly, the participants marked the accuracy, set up time, participant comfort, and participant perceived ease of set-up on a scale of 1 to 7 (7 being excellent). Thirdly, the self-designed EEG headband is used by 5 participants for slide changing. The raw EEG signal is decomposed into a series of band signals using discrete wavelet transform (DWT). Lastly, these findings have been compared to previously reported studies. We concluded that when used for slide-changing control, our self-designed EEG headband had an accuracy of 82.0 percent. We also concluded from the results that our headset performed well on the cost-effectiveness scale, had a reduced setup time of 2 ± 0.5 min (the shortest among all being compared), and demonstrated greater ease of use.
KW - accessibility
KW - Brain-computer interface
KW - consumer-grade
KW - EEG
KW - headset
UR - http://www.scopus.com/inward/record.url?scp=85137701686&partnerID=8YFLogxK
U2 - 10.32604/iasc.2023.026279
DO - 10.32604/iasc.2023.026279
M3 - Article
AN - SCOPUS:85137701686
SN - 1079-8587
VL - 35
SP - 2821
EP - 2835
JO - Intelligent Automation and Soft Computing
JF - Intelligent Automation and Soft Computing
IS - 3
ER -