TY - JOUR
T1 - The Three-Lead EEG Sensor
T2 - Introducing an EEG-Assisted Depression Diagnosis System Based on Ant Lion Optimization
AU - Tian, Fuze
AU - Zhu, Lixian
AU - Shi, Qiuxia
AU - Wang, Rui
AU - Zhang, Lixin
AU - Dong, Qunxi
AU - Qian, Kun
AU - Zhao, Qinglin
AU - Hu, Bin
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/12/1
Y1 - 2023/12/1
N2 - For depression diagnosis, traditional methods such as interviews and clinical scales have been widely leveraged in the past few decades, but they are subjective, time-consuming, and labor-consuming. With the development of affective computing and Artificial Intelligence (AI) technologies, Electroencephalogram (EEG)-based depression detection methods have emerged. However, previous research has virtually neglected practical application scenarios, as most studies have focused on analyzing and modeling EEG data. Furthermore, EEG data is typically obtained from specialized devices that are large, complex to operate, and poorly ubiquitous. To address these challenges, a wearable three-lead EEG sensor with flexible electrodes was developed to obtain prefrontal-lobe EEG data. Experimental measurements show that the EEG sensor achieves promising performance (background noise of no more than 0.91 μ Vpp, Signal-to-Noise Ratio (SNR) of 26 - 48 dB, and electrode-skin contact impedance of less than 1 KΩ). In addition, EEG data from 70 depressed patients and 108 healthy controls were collected using the EEG sensor, and the linear and nonlinear features were extracted. The features were then weighted and selected using the Ant Lion Optimization (ALO) algorithm to improve classification performance. The experimental results show that the k-NN classifier achieves a classification accuracy of 90.70%, specificity of 96.53%, and sensitivity of 81.79%, indicating the promising potential of the three-lead EEG sensor combined with the ALO algorithm and the k-NN classifier for EEG-assisted depression diagnosis.
AB - For depression diagnosis, traditional methods such as interviews and clinical scales have been widely leveraged in the past few decades, but they are subjective, time-consuming, and labor-consuming. With the development of affective computing and Artificial Intelligence (AI) technologies, Electroencephalogram (EEG)-based depression detection methods have emerged. However, previous research has virtually neglected practical application scenarios, as most studies have focused on analyzing and modeling EEG data. Furthermore, EEG data is typically obtained from specialized devices that are large, complex to operate, and poorly ubiquitous. To address these challenges, a wearable three-lead EEG sensor with flexible electrodes was developed to obtain prefrontal-lobe EEG data. Experimental measurements show that the EEG sensor achieves promising performance (background noise of no more than 0.91 μ Vpp, Signal-to-Noise Ratio (SNR) of 26 - 48 dB, and electrode-skin contact impedance of less than 1 KΩ). In addition, EEG data from 70 depressed patients and 108 healthy controls were collected using the EEG sensor, and the linear and nonlinear features were extracted. The features were then weighted and selected using the Ant Lion Optimization (ALO) algorithm to improve classification performance. The experimental results show that the k-NN classifier achieves a classification accuracy of 90.70%, specificity of 96.53%, and sensitivity of 81.79%, indicating the promising potential of the three-lead EEG sensor combined with the ALO algorithm and the k-NN classifier for EEG-assisted depression diagnosis.
KW - ALO algorithm
KW - Depression diagnosis
KW - feature weighting and feature selection
KW - wearable EEG sensor
UR - http://www.scopus.com/inward/record.url?scp=85164406091&partnerID=8YFLogxK
U2 - 10.1109/TBCAS.2023.3292237
DO - 10.1109/TBCAS.2023.3292237
M3 - Article
C2 - 37402182
AN - SCOPUS:85164406091
SN - 1932-4545
VL - 17
SP - 1305
EP - 1318
JO - IEEE Transactions on Biomedical Circuits and Systems
JF - IEEE Transactions on Biomedical Circuits and Systems
IS - 6
ER -