End-to-End Machine Learning for Depressive State Classification via EEG and fNIRS
For mental health diagnostics, this work provides a foundational step toward automated, objective depression detection using biological signals, though it is a small pilot study with no reported performance metrics.
This pilot study with 11 healthy students establishes a framework for classifying depressive states using EEG and fNIRS signals, aiming to develop objective diagnostic tools to overcome subjective bias in traditional methods.
The escalating demand for mental healthcare, driven by rising societal stress, highlights the limitations of traditional psychiatric diagnostics. Conventional methods - relying primarily on clinical interviews and patient self-reports - are inherently vulnerable to subjective bias and the varying empirical judgment of practitioners. To address the need for quantitative evaluation, biological signal-based detection, including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), has emerged as a promising objective alternative. Such technology is particularly vital for identifying latent depressive states that may be unrecognized by the subjects themselves. Furthermore, in aging populations, the high comorbidity between depression and dementia necessitates early differentiation to prevent mutual symptom exacerbation and maintain Quality of Life (QoL). This pilot study of eleven healthy students establishes a framework for biological signal-based depression detection, serving as a foundational step toward automated, objective diagnostic tools for clinical use.