Version 1
: Received: 29 April 2024 / Approved: 30 April 2024 / Online: 1 May 2024 (03:00:28 CEST)
How to cite:
Delis, A.; Tsavdaridis, G.; Tsanakas, P. A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression. Preprints2024, 2024042023. https://doi.org/10.20944/preprints202404.2023.v1
Delis, A.; Tsavdaridis, G.; Tsanakas, P. A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression. Preprints 2024, 2024042023. https://doi.org/10.20944/preprints202404.2023.v1
Delis, A.; Tsavdaridis, G.; Tsanakas, P. A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression. Preprints2024, 2024042023. https://doi.org/10.20944/preprints202404.2023.v1
APA Style
Delis, A., Tsavdaridis, G., & Tsanakas, P. (2024). A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression. Preprints. https://doi.org/10.20944/preprints202404.2023.v1
Chicago/Turabian Style
Delis, A., George Tsavdaridis and Panayiotis Tsanakas. 2024 "A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression" Preprints. https://doi.org/10.20944/preprints202404.2023.v1
Abstract
In this study, the fundamentals of electroencephalography signals, their categorization into frequency sub-bands, the circuitry used for their acquisition, and the impact of noise interference on signal acquisition are examined. Additionally, design specifications for medical-grade and research-grade EEG circuits and a comprehensive analysis of various analog front-end architectures for electroencephalograph (EEG) circuit design are presented. Three distinct selected case studies are examined in terms of comparative evaluation with generic EEG circuit design templates. Moreover, a novel one-channel battery-supplied EEG analog front-end circuit designed to address the requirements of usage protocols containing strong com-pound muscle movements is introduced. Furthermore, a realistic input signal generator circuit is proposed that models the human body and the Electromagnetic Interference from its surroundings. Experimental simulations are conducted in 50 Hz and 60 Hz electrical grid environments to evaluate the performance of the novel design. The results demonstrate the efficacy of the proposed system, particularly in terms of bandwidth, portability, common mode rejection ratio , gain, suppression of muscle movement artifacts, electrostatic discharge and leakage current protection. Conclusively, the novel design is cost-effective and suitable for both commercial and research single-channel EEG applications. It can be easily incorporated in Brain Computer Interfaces and neurofeedback training systems.
Keywords
electroencephalography; EEG; muscle artifacts; notch filter; driving right leg; battery-based EEG signal acquisition system; biosignal filtering; low frequency signal; CMRR
Subject
Engineering, Bioengineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
