| dc.description.abstract | In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional impairments. Electroencephalogram (EEG) signals, being an activity of the central nervous system, reflect the underlying true emotional
state of a person. This research focuses on analyzing different non-linear algorithms to
recognize emotional states in Parkinson’s disease (PD) patients compared to healthy
controls (HC) participants using EEG signals. Twenty non-demented PD patients and 20
healthy age-, gender-, and education level-matched controls viewed happiness, sadness,
fear, anger, surprise, and disgust using multimodal stimulus (combination of audio and
visual) while 14-channel wireless EEG was being recorded. In addition, participants
were asked to report their subjective affect. The acquired EEG signals were
preprocessed using thresholding method to remove eye blinks/movement artifacts. A
Butterworth 6th order bandpass filter was used to extract the following EEG frequency
bands: delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma
(30–49 Hz). To classify the emotional states and visualize the changes of emotional
states over time at single-electrode level, four kinds of feature extraction methods
(namely higher order spectra (HOS), non-linear analysis, fast Fourier transform and
wavelet packet transform) were compared, and proposed an approach to visualize the
trajectory of emotion changes with manifold learning. Three connectivity indices,
including correlation, coherence, and phase synchronization index (PSI) were extracted
by focusing on electrode pairs to estimate brain functional connectivity in EEG signals.
New feature, namely, bispectrum based phase synchronization index (bPSI) was
proposed for computing EEG functional connectivity patterns with the traditional
methods. The statistical significance of all the computed features was studied using
Analysis of Variance (ANOVA) test. Four different classifiers namely Fuzzy K- Nearest
Neighbor (FKNN), K-Nearest Neighbor (KNN), Regression Tree (RT), and Support
Vector Machine (SVM) were used to investigate the performance of the extracted
features. Ten-fold cross-validation method was used for testing the reliability of the
classifier results. The features extracted in all the methods were found to be statically
significant (p < 0.05). The HOS based feature across ALL frequency bands
(combination of five bands) performed well in recognizing emotional states of PD
patients and HC participants with an averaged recognition rate of 77.43% ± 1.59% and
83.04% ± 1.87% respectively. The PD patients showed emotional impairments as
demonstrated by a lower classification performance, particularly for negative emotions
(sadness, fear, anger and disgust). The emotion-specific feature was mainly related to
high frequency band (alpha, beta and gamma) than low frequency band (delta and
theta). The trajectory of emotion changes was drawn by a manifold learning model.
Also, bPSI functional connectivity index performed better with an averaged recognition
rate of 51.66% ± 1.02% and 71.79% ± 1.01% for PD patients and HC respectively. | en_US |
