Recurrence Quantification Analysis based Emotion Detection in Parkinson’s disease using EEG Signals

Abstract— Emotional disturbances are Parkinson’s disease
(PD) patients is typical, and this work aims to identify the
emotional disturbances in PD using Electroencephalogram
(EEG) signals. Clinicians assess the emotional impairment in
PD using International standard questionnaires, and most of
the time, this assessment becomes inaccurate since the verbalresponses of PDs are not precise to express their internalfeelings. EEG based emotional impairment detection in PDgained significant attention due to its robustness, flexibility,and non-invasiveness. In this work, we utilized the EEGdataset consists of 20 subjects each in PD and 20 NormalControl (NC), and EEG signals are collected using 14 channelwireless EEG device over six types of emotions (happiness,sadness, anger, fear, disgust, and surprise) at a sampling rateof 128 Hz. The 6th order IIR Butterworth filter with a cut-offfrequency of 0.5 Hz – 49 Hz is used to filter the noises andother external interferences. Two features from Recurrent