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

arXiv: https://arxiv.org/abs/2202.12936 · HTML: https://mlsanity.com/paper/2202.12936/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 78 references that are in the map.
- First posted on arXiv 21 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Beyond Short Snippets: Deep Networks for Video Classification](https://mlsanity.com/paper/1503.08909/) (2015)
- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization](https://mlsanity.com/paper/2005.14405/) (2020)
- [Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks](https://mlsanity.com/paper/1511.06448/) (2015)
- [Deep learning for neuroimaging: a validation study](https://mlsanity.com/paper/1312.5847/) (2013)
- [Recognition of Advertisement Emotions with Application to Computational Advertising](https://mlsanity.com/paper/1904.01778/) (2019)

## Built on by (most cited first, 1 of 1)

- [Novel techniques for improving NNetEn entropy calculation for short and noisy time series](https://mlsanity.com/paper/2202.12703/) (2022)

## Abstract (arXiv)

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, textitfear, textitdisgust and textitsurprise less accurately, and textitsadness 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 textitimplicit 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.