Signal Quality Investigation of a New Wearable Frontal Lobe EEG Device

Abstract

The demand for non-laboratory and long-term EEG acquisition in scientific and clinical applications has put forward new requirements for wearable EEG devices. In this paper, a new wearable frontal EEG device called Mindeep was proposed. A signal quality study was then conducted, which included simulated signal tests and signal quality comparison experiments. Simulated signals with different frequencies and amplitudes were used to test the stability of Mindeep’s circuit, and the high correlation coefficients (>0.9) proved that Mindeep has a stable and reliable hardware circuit. The signal quality comparison experiment, between Mindeep and the gold standard device, Neuroscan, included three tasks: (1) resting; (2) auditory oddball; and (3) attention. In the resting state, the average normalized cross-correlation coefficients between EEG signals recorded by the two devices was around 0.72 ± 0.02, Berger effect was observed (p < 0.01), and the comparison results in the time and frequency domain illustrated the ability of Mindeep to record high-quality EEG signals. The significant differences between high tone and low tone in auditory event-related potential collected by Mindeep was observed in N2 and P2. The attention recognition accuracy of Mindeep achieved 71.12% and 74.76% based on EEG features and the XGBoost model in the two attention tasks, respectively, which were higher than that of Neuroscan (70.19% and 72.80%). The results validated the performance of Mindeep as a prefrontal EEG recording device, which has a wide range of potential applications in audiology, cognitive neuroscience, and daily requirements.

Advances in Biosignal Sensing and Signal Processing Methods with Wearable Devices

“…with permission from MDPI. (B) Frontal lobe EEG, reprinted from, [106] copyright(2022), Gao et al. with permission from MDPI.…”

“…EEG also has some localization ability, [104] though not to the degree of imaging techniques. Conventionally deployed in a cap-like device using Ag/AgCl electrodes with gel electrolyte, [105][106] EEG has since been adapted by wearable device researchers with alternative electrode materials and form factors to make the technology amenable to longterm or daily monitoring.…”

“…(A) EEG-utilized stress monitoring system while gaming, reprinted from, [107] copyright(2022), Affanni et al with permission from MDPI. (B) Frontal lobe EEG, reprinted from, [106] copyright(2022), Gao et al with permission from MDPI. (C) Stretchable dry EEG electrode, reprinted from, [105] copyright(2021), Harati et al with permission from Elsevier.…”

EEG Signals Based Internet Addiction Diagnosis Using Convolutional Neural Networks

“…By comparing the results of full spectral power and alpha-beta-gamma spectral power, the proposed algorithm can determine whether a person has IA through EEG signals in the absence of biomarkers. In addition, the results show that the proposed algorithm has obvious advantages in processing raw data, which can reduce the diagnosis time and have the potential to be used in some real-time health monitoring systems [28][29][30].…”

Assessment of a 16-Channel Ambulatory Dry Electrode EEG for Remote Monitoring

“…Today, many researchers have chosen to develop new devices rather than using commercially available devices. In 2022, Gao et al [ 23 ] investigated a new wearable EEG device’s signal quality for a brain computer interface (BCI). The device was constructed using an ADS1234 analog-to-digital converter (ADC) and had four dry electrode channels.…”