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While Parkinson's disease (PD) is typically characterized by motor disorder,there is evidence of diminished emotion perception in PD patients. This studyexamines the utility of affective Electroencephalography (EEG) signals tounderstand emotional differences between PD vs Healthy Controls (HC), and forautomated PD detection. Employing traditional machine learning and deeplearning methods, we explore (a) dimensional and categorical emotionrecognition, and (b) PD vs HC classification from emotional EEG signals. Ourresults reveal that PD patients comprehend arousal better than valence, andamongst emotion categories, \textit{fear}, \textit{disgust} and\textit{surprise} less accurately, and \textit{sadness} most accurately.Mislabeling analyses confirm confounds among opposite-valence emotions with PDdata. Emotional EEG responses also achieve near-perfect PD vs HC recognition.{Cumulatively, our study demonstrates that (a) examining \textit{implicit}responses alone enables (i) discovery of valence-related impairments in PDpatients, and (ii) differentiation of PD from HC, and (b) emotional EEGanalysis is an ecologically-valid, effective, facile and sustainable tool forPD diagnosis vis-\'a-vis self reports, expert assessments and resting-stateanalysis.}

Automated Parkinson's Disease Detection and Affective Analysis from Emotional EEG Signals