End-to-End Machine Learning for Depressive State Classification via EEG and fNIRS 事件

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 quantita