Nanocomposite-Based Electrode Structures for EEG Signal Acquisition

• Published in: Crystals (Basel) Vol. 12; no. 11; p. 1526
• Main Authors: Vajravelu, Ashok, Abdul Jamil, Muhammad Mahadi Bin, Abd Wahab, Mohd Helmy Bin, Wan Zaki, Wan Suhaimizan Bin, Vinod, Vibin Mammen, Ramasamy Palanisamy, Karthik, Nageswara Rao, Gousineyah
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-11-2022
• Subjects: Biofeedback; brain activities; Correlation coefficients; Design; EEG signals; electrode; Electrodes; Electroencephalography; fabrication; Graphene; Graphical user interface; Hematite; Human-computer interface; Hydrogels; Nanocomposites; Nanoparticles; Nonwoven fabrics; Polyvinyl alcohol; Polyvinylidene fluorides; Rehabilitation; Shape; Signal analysis; Signal quality; Signal to noise ratio; Strength to weight ratio; Substrates; Textiles; Wavelet transforms; Wearable technology; Yarn
• ISSN: 2073-4352; 2073-4352
• DOI: 10.3390/cryst12111526

Objective: To fabricate a lightweight, breathable, comfortable, and able to contour to the curvilinear body shape, electrodes built on a flexible substrate are a significant growth in wearable health monitoring. This research aims to create a GNP/FE electrode-based EEG signal acquisition system that is both efficient and inexpensive. Methodology: Three distinct electrode concentrations were developed for EEG signal acquisition, three distinct electrode concentrations (1.5:1.5, 2:1, and 3:0). The high strength-to-weight ratio to form the tribofilm in the fabrication of the electrode will provide good efficiency. The EEG signal is first subjected to a wavelet transform, which serves as a preliminary analysis. The use of biopotential signals in wearable systems as biofeedback or control commands is expected to substantially impact point-of-care health monitoring systems, rehabilitation devices, human–computer/machine interfaces (HCI/HMI), and brain–computer interfaces (BCIs). The graphene oxide (GO), glycerol (GL), and polyvinyl alcohol (PVA) GO/GL/PVA plastic electrodes were measured and compared to that of a commercially available electrode using the biopic equipment. The GO/GL/PVA plastic electrode was able to detect EEG signals satisfactorily after being used for two months, demonstrating good conductivity and lower noise than the commercial electrode. The GO/GL/PVA nanocomposite mixture was put into the electrode mold as soon as it was ready and then rapidly chilled. Results: The quality of an acquired EEG signal could be measured in several ways including by its error percentage, correlation coefficient, and signal-to-noise ratio (SNR). The fabricated electrode yield detection ranged from 0.81 kPa−1 % to 34.90 kPa−1%. The performance was estimated up to the response of 54 ms. Linear heating at the rate of 40 °C per minute was implemented on the sample ranges from 0 °C to 240 °C. During the sample electrode testing in EEG signal analysis, it obtained low impedance with a good quality of signal acquisition when compared to a conventional wet type of electrode. Conclusions: A large database was frequently built from all of the simulated signals in MATLAB code. Through the experiment, all of the required data were collected, checked against all other signals, and proven that they were accurate representations of the intended database. Evidence suggests that graphene nanoplatelets (GNP) hematite (FE2O3) polyvinylidene fluoride (PVDF) GNP/FE2O3@PVDF electrodes with a 3:0 concentration yielded the best outcomes.