  While Parkinson's disease (PD) is typically characterized by motor disorder,
there is evidence of diminished emotion perception in PD patients. This study
examines the utility of affective Electroencephalography (EEG) signals to
understand emotional differences between PD vs Healthy Controls (HC), and for
automated PD detection. Employing traditional machine learning and deep
learning methods, we explore (a) dimensional and categorical emotion
recognition, and (b) PD vs HC classification from emotional EEG signals. Our
results reveal that PD patients comprehend arousal better than valence, and
amongst 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 PD
data. 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 PD
patients, and (ii) differentiation of PD from HC, and (b) emotional EEG
analysis is an ecologically-valid, effective, facile and sustainable tool for
PD diagnosis vis-\'a-vis self reports, expert assessments and resting-state
analysis.}
