The growing number of depressive people and the overload in primary care services make it necessary to identify depressive states with easily accessible biomarkers such as mobile electroencephalography (EEG). Some studies have addressed this issue by collecting and analyzing EEG resting state in a search of appropriate features and classification methods. Traditionally, EEG resting state classification methods for depression were mainly based on linear or a combination of linear and non-linear features. We hypothesize that participants with ongoing depressive states differ from controls in complex patterns of brain dynamics that can be captured in EEG resting state data, using only nonlinear measures on a few electrodes, making it possible to develop cheap and wearable devices that could be even monitored through smartphones. To validate such a perspective, a resting-state EEG study was conducted with 50 participants, half with depressive state (DEP) and half controls (CTL). A data-driven approach was applied to select the most appropriate time window and electrodes for the EEG analyses, as suggested by Giacometti⁠, as well as the most efficient nonlinear features and classifiers, to distinguish between CTL and DEP participants. Nonlinear features showing temporo-spatial and spectral complexity were selected. The results confirmed that computing nonlinear features from a few selected electrodes in a 15 s time window are sufficient to classify DEP and CTL participants accurately. Finally, after training and testing internally the classifier, the trained machine was applied to EEG resting state data (CTL and DEP) from a publicly available database, validating the capacity of generalization of the classifier with data from different equipment, population, and environment obtaining an accuracy near 100%.

In this study we trained and tested several deep learning algorithms to classify depressive individuals and controls based on their electroencephalography data. Traditionally, classification methods based on electroencephalography resting state are based primarily on linear features or a combination of linear and non-linear features. Based on different theoretical grounds, some authors claim that the more electrodes, the more accurate the classifiers, while others consider that working on a selection of electrodes is a better approach⁠. In this study, a data-driven approach was initially applied on a selection of electrodes to classify 25 depressive and 24 control participants. Using a classifier with just four electrodes, based on non-linear features with high temporo-spatial complexity, proved accurate enough to classify depressive and control participants. After the classifier was internally trained and tested, it was applied to electroencephalography resting-state data of control and depressive individuals available from a public database, obtaining a classifier accuracy of 93% in depressive and 100% in control. This validates the generalizability of the classifier to untrained data from different teams, populations, and settings. We conclude that time-window span analysis is a promising approach to understand the neural dynamics of depression and to develop an independent biomarker.

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In this study we trained and tested several deep learning algorithms to classify depressive individuals and controls based on their electroencephalography data. Traditionally, classification methods based on electroencephalography resting-state are based primarily on linear features or a combination of linear and non-linear features. Based on different theoretical grounds, some authors claim that the more electrodes, the more accurate the classifiers, while others consider that working on a selection of electrodes is a better approach□. In this study, a data-driven approach was initially applied on a selection of electrodes to classify 25 depressive and 24 control participants. Using a classifier with just four electrodes, based on non-linear features with high temporo-spatial complexity, proved accurate enough to classify depressive and control participants. After the classifier was internally trained and tested, it was applied to electroencephalography resting-state data of control and depressive individuals available from a public database, obtaining a classifier accuracy of 93% in the depressive and 100% in the control group. This validates the generalizability of the classifier to untrained data from different teams, populations and settings. We conclude that time-window span analysis is a promising approach to understand the neural dynamics of depression and to develop an independent biomarker.