Multi-channel Wireless EEG Recordings of Young Adults for Depression Screening based on PHQ-9
Description
The EEG was recorded using a wireless EMOTIV EPOC+ headset (with 14 channels and a sampling rate of 128 Hz) from 31 young adults (aged between 18 and 25 years, 15 male and 16 female). Before EEG acquisition, after taking participant consent, a self-reported survey following the PHQ-9 questionnaire was filled out by the participants to find the ground truth for screening depression. There were 18 participants found to have PHQ-9 scores more than or equal to 20 classified as Depressed subjects (labelled as DSub1-DSub18), while 13 participants had PHQ-9 scores less than or equal to 4 classified as Depression Control subjects (labelled as DCSub1-DSub13). Each recording was 5 minutes long for each participant. The 14 EEG channels are placed according to the International 10-20 electrode montage system: eight frontal electrodes (AF3, F3, F7, FC5, AF4, F4, F8 and FC6), two temporal electrodes (T7 and T8), two parietal electrodes (P7 and P8), two occipital electrodes (O1 and O2), and two reference channels (P3 and P4). The dataset has .mat file extension (can be opened by MATLAB software). Each file has a data size of 38,400 x 14, where each column denotes channel number and each row denotes sample number. Since each recording is 5 minutes long (300 seconds), each channel has 38,400 samples, which is equivalent to 300 seconds (sampling rate of 128 Hz). The ethical approval through the Institutional Review Board (IRB) of the Independent University, Bangladesh (IUB) was taken prior to the experiment. All the participants were students of IUB, whose EEG recordings were conducted at the Biomedical Instrumentation and Signal Processing Lab (BISPL) of the Department of Electrical and Electronic Engineering (EEE), IUB.
Files
Steps to reproduce
EEG Electrode Montage: International 10-20 System EEG Recording Headset: Emotiv EPOC+ Software to Analyse and Process Raw EEG: MATLAB Depression Screening Tool: PHQ-9 For details, please follow the methods section of the following articles to reproduce and cite them accordingly. 1. Sakib, Nazmus, Md Kafiul Islam, and Tasnuva Faruk. "Machine learning model for computer‐aided depression screening among young adults using wireless EEG headset." Computational Intelligence and Neuroscience 2023, no. 1 (2023): 1701429. 2. N. Sakib, M. K. Islam and T. Faruk, "Machine Learning Based Depression Screening Among Young Adults Using Wireless EEG," 2023 International Conference on Artificial Intelligence Innovation (ICAII), Wuhan, China, 2023, pp. 110-115, doi: 10.1109/ICAII59460.2023.10497265. 3. Sakib, Nazmus, Md Kafiul Islam, and Tasnuva Faruk. "Effect of Artifact Removal in Machine Learning Based Depression Screening using EEG." In Proceedings of the 2023 8th International Conference on Biomedical Imaging, Signal Processing, pp. 115-120. 2023.
Institutions
- Independent University