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

Riaz Muhammad, Ahmed Ali, M. Abid Anwar, Toufique Ahmed Soomro*, Omar Alshorman, Adel Alshahrani, Mahmoud Masadeh, Ghulam Md Ashraf, Naif H. Ali, Muhammad Irfan, Athanasios Alexiou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)2821-2835
Number of pages15
JournalIntelligent Automation and Soft Computing
Volume35
Issue number3
DOIs
Publication statusPublished - 2023
Externally publishedYes

Keywords

  • accessibility
  • Brain-computer interface
  • consumer-grade
  • EEG
  • headset

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