A Pervasive Approach to EEG‐Based Depression Detection

Abstract

Nowadays, depression is the world's major health concern and economic burden worldwide. However, due to the limitations of current methods for depression diagnosis, a pervasive and objective approach is essential. In the present study, a psychophysiological database, containing 213 (92 depressed patients and 121 normal controls) subjects, was constructed. The electroencephalogram (EEG) signals of all participants under resting state and sound stimulation were collected using a pervasive prefrontal-lobe threeelectrode EEG system at Fp1, Fp2, and Fpz electrode sites. After denoising using the Finite Impulse Response filter combining the Kalman derivation formula, Discrete Wavelet Transformation, and an Adaptive Predictor Filter, a total of 270 linear and nonlinear features were extracted. Then, the minimal-redundancy-maximal-relevance feature selection technique reduced the dimensionality of the feature space. Four classification methods (Support Vector Machine, K-Nearest Neighbor, Classification Trees, and Artificial Neural Network) distinguished the depressed participants from normal controls. The classifiers' performances were evaluated using 10-fold cross-validation. The results showed that K-Nearest Neighbor (KNN) had the highest accuracy of 79.27%. The result also suggested that the absolute power of the theta wave might be a valid characteristic for discriminating depression. This study proves the feasibility of a pervasive three-electrode EEG acquisition system for depression diagnosis.

GO Loss: A Gaussian Distribution‐Based Orthogonal Decomposition Loss for Classification

“…For various classi cation tasks, the loss function directly a ects the classi cation e ect [29][30][31]. In the existing methods, metric learning is widely used in the loss function to improve the classi cation e ect [32][33][34].…”

Artificial Neural Network Classification of Motor‐Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

“…The most common technology to detect various kinds of brain activity, both normal and pathological, is based on EEG recordings [25,26], although recently Wu et al [27] introduced a new approach for MEG data classification using a support vector machine (SVM) with a radial basis kernel function, which was shown to be an effective method for right and left temporal lobe epilepsy recognition. In the present paper, we analyze different types of ANNs in order to reveal most convenient configurations.…”

“…Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73 ± 15%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time-frequency structure of EEG signals and find that motor-related features associated with left and right leg motor imagery are more pronounced in the mu (8-13 Hz) and delta (1-5 Hz) brainwaves than in the high-frequency beta brainwave (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low-pass filter with different cutoffs.…”

Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN

“…This has been made possible with the advancements in the Internet of Things (IoT) . Brain diseases and disorders, including epilepsy, Parkinson's disease, autism, and Major Depression Disorder (MDD), constitute a significant proportion of healthcare and its applications. For example, accurate evaluation and prompt notification of brain states can minimize the risk of falling in public places and suicide in people with MDD .…”