Email this article Automatic Depression Screening Through CNN And EEG Data
Abstract
Depression remains a challenge worldwide owing to subjective assessment techniques. In this particular project, we experiment with an automated depression detection system based on EEG signals using Convolutional Neural Networks (CNN). The required EEG data is pulled from a public Kaggle data set, where it is preprocessed and entered into a CNN model that classifies individuals as either depressed or non-depressed. The CNN model demonstrated a 93% accuracy rate, significantly outperforming traditional Support Vector Machines (SVM) which offered a 63% accuracy rate. The focus of the proposed solution is on early identification, which minimizes the subjectivity associated with diagnosis and enables the efficient scale of mental health screening and timely intervention within clinical environments.
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References
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