| dc.description.abstract | Diagnosis of depression using electroencephalography (EEG) is an emerging field of study. When mental health facilities are unavailable, the use of EEG as an objective measure for depression management at an individual level becomes necessary. However, the limited availability of the openly accessible EEG datasets for depression and the non-standard task paradigm confine the scope of the research. This study contributes to the area by presenting a dataset that includes EEG data of subjects in the resting state and Patient Health Questionnaire (PHQ)-9 scores. These recordings incorporate EEG signals under both eyes open (EO) and eyes closed (EC) conditions. Moreover, this work documents high performance on various benchmark depression classification tasks with the help of traditional supervised machine learning algorithms, namely Decision Tree, Random Forest, k-Nearest Neighbours, Naive Bayes, Support Vector Machine, Multi-Layer Perceptron, and extreme gradient boosted trees (XGBoost) using the newly created dataset, where the class label of each patient is determined by the PHQ-9 score of the person. Then, feature selection is performed on twenty-three linear, nonlinear, time domain, and frequency domain features using ANOVA test and correlation analysis to identify statistically significant features, which are further fed into algorithms mentioned above separately for distinguishing healthy subjects from depressed. Among these classifiers, the performance of the XGBoost is found to be the best, with an accuracy of 87% for the EO state. The obtained results demonstrate that the proposed method outperforms fourteen existing approaches. The dataset presented in this work can be downloaded via https://drive.google.com/drive/folders/1ANUC-6hq02QG728ZWv2a1UWTLUbRrq y?usp=sharing. | en_US |
