Open Access

ARTICLE

Design and Development of Low-cost Wearable Electroencephalograms (EEG) Headset

Riaz Muhammad¹, Ahmed Ali¹, M. Abid Anwar¹, Toufique Ahmed Soomro^2,*, Omar AlShorman³, Adel Alshahrani⁴, Mahmoud Masadeh⁵, Ghulam Md Ashraf^6,7, Naif H. Ali⁸, Muhammad Irfan⁹, Athanasios Alexiou¹⁰

1 Department of Electrical Engineering, Sukkur IBA University, 65200, Sukkur, Pakistan
2 Department of Electronic Engineering, Quaid-e-Awam University of Engineering, Science and Technology, Larkana, 77150, Pakistan
3 Faculty of Engineering and AlShrouk Trading Company, Najran University, Najran, 61441, Saudi Arabia
4 Department of Medical Rehabilitation Sciences-Physiotherapy Program, College of Applied Medical Sciences, Najran University, Najran, 61441, Saudi Arabia
5 Computer Engineering Department, Yarmouk University, Irbid, 21163, Jordan
6 Pre-Clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
7 Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
8 Department of Internal Medicine, Medical College, Najran University, Najran, 61441, Saudi Arabia
9 Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran, 61441, Saudi Arabia
10 Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia, AFNP Med Austria, Wien, 2770, Austria

* Corresponding Author: Toufique Ahmed Soomro. Email:

Intelligent Automation & Soft Computing 2023, 35(3), 2821-2835. https://doi.org/10.32604/iasc.2023.026279

Received 21 December 2021; Accepted 20 March 2022; Issue published 17 August 2022

Abstract

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.

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Keywords

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