Single-Channel EEG-Based Machine Learning Method for Prescreening Major Depressive Disorder

Abstract

Many studies developed the machine learning method for discriminating Major Depressive Disorder (MDD) and normal control based on multi-channel electroencephalogram (EEG) data, less concerned about using single channel EEG collected from forehead scalp to discriminate the MDD. The EEG dataset is collected by the Fp1 and Fp2 electrode of a 32-channel EEG system. The result demonstrates that the classification performance based on the EEG of Fp1 location exceeds the performance based on the EEG of Fp2 location, and shows that single-channel EEG analysis can provide discrimination of MDD at the level of multi-channel EEG analysis. Furthermore, a portable EEG device collecting the signal from Fp1 location is used to collect the second dataset. The Classification and Regression Tree combining genetic algorithm (GA) achieves the highest accuracy of 86.67% based on leave-one-participant-out cross validation, which shows that the single-channel EEG-based machine learning method is promising to support MDD prescreening application.

Enabling #EEGManyLabs: Quality in Automatically Preprocessed EEG Data and Psychopathological Associations of Frontal Alpha Asymmetry

“…(2021) could prove to be a more a reliable and less controversial means to identify EEG patterns as biomarkers of mental illness. In the field of EEG-based classification, there have already been several successful demonstrations regarding classification of depression (Acharya et al, 2018;Hosseinifard et al, 2013;Wan et al, 2019), diagnosis of depression subtypes (Zelenina & Prata, 2019), depression severity (Mohammadi et al, 2019), and treatment response (Hasanzadeh et al, 2019;Jaworska et al, 2019;Khodayari-Rostamabad et al, 2013).…”

Exploration of EEG-Based Depression Biomarkers Identification Techniques and Their Applications: A Systematic Review

“…Moreover, many software and toolboxes (such as-MATLAB EEGLAB epoch rejection function; Net Station Waveform; BESA; QExG; TrimOutlier) manage built-in plugins that are used for automatic artefacts removal. Blackman window [55], [69] Used to remove high-band noise caused by EMG Z-score normalization [44], [80], [85], [50] Used to eliminate the amplitude scaling problem and remove offset effects.…”

“…ICA/ FastICA/ FASTER algorithm [24], [56], [62], [44], [36], [83], [32], [23], [37], [31], [58], [20], [44], [70], [86], [88] Used to reduce EOG or blinking and eye movement-related artefacts 50Hz Notch filter [55], [42], [43], [64], [61], [67], [47], [75], [20], [49], [50], [99], [88] Used to remove 50Hz power line noise Reduction of physical activities and Visual Expertise to avoid artifacts [48], [77], [53], [64], [78], [79] While recording the data, participants were instructed to close their eyes and reduce movement, swallowing saliva, and breathing normally to suppress the blinking artefacts as much as possible. [101], [111], [75] Kolmogorov entropy [68], [51], [113], [71] Shannon entropy [51], [53], [113], [71] Sample Entropy (SampEn) [99], <...>…”

“…T-test [68], [86] Genetic Algorithm [101], [56], [100], [120], [113], [50], [87], [72] RankSearch Algorithm [61], [113], [72] Others (MRMR, LFS) [71], [113] Uses of deep learning Method [99], [54], [116], [121], [122], [33] 7) CLINICAL QUESTIONNAIRE BASED ASSESSMENTS Many well-known questionnaire assessments have been used to detect the depression category. According to the score calculated through the patient's interview, psychiatrists can detect depression by the degree of severity (mild, moderate, or severe).…”

Single Channel EEG Classification: A Case Study on Prediction of Major Depressive Disorder Treatment Outcome

“…However, multichannel EEG has been widely used to study brain activity and brain networks [19][20][21] in depressive disorder. SCEEG is only applied to detect depression in only a few studies [22][23][24]. Bachmann et al extracted linear and nonlinear measures from single-channel EEG for classifying depressed and normal subjects.…”