aiqbench analysis
This pilot study proposes an end-to-end machine learning framework utilizing EEG and fNIRS signals to objectively detect latent depressive states, aiming to overcome the subjectivity inherent in traditional clinical interviews. By establishing a foundational model on a small cohort of healthy students, the research targets the critical need for quantitative diagnostics in aging populations where depression and dementia comorbidities complicate assessment. While the current scope is limited, the work serves as a necessary proof-of-concept for transitioning mental healthcare toward automated, data-driven diagnostic tools.
End-to-End Machine Learning for Depressive State Classification via EEG and fNIRS
arXiv:2606.11555v1 Announce Type: cross Abstract: 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.
Abstract: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.
4 pages, 4 figures, Accepted for publication in the Proc. 48th Annu. Int. Conf. IEEE EMBS (EMBC 2026), Toronto, Canada, July 20-24, 2026
Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2606.11555 [q-bio.NC]
(or arXiv:2606.11555v1 [q-bio.NC] for this version)
https://doi.org/10.48550/arXiv.2606.11555 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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