title: Brain functional connectivity patterns for emotional state classification in Parkinson's disease patients without dementia.
abstract: Successful emotional communication is crucial for social interactions and social relationships. Parkinson's Disease (PD) patients have shown deficits in emotional recognition abilities although the research findings are inconclusive. This paper presents an investigation of six emotions (happiness, sadness, fear, anger, surprise, and disgust) of twenty non-demented (Mini-Mental State Examination score >24) PD patients and twenty Healthy Controls (HCs) using Electroencephalogram (EEG)-based Brain Functional Connectivity (BFC) patterns. The functional connectivity index feature in EEG signals is computed using three different methods: Correlation (COR), Coherence (COH), and Phase Synchronization Index (PSI). Further, a new functional connectivity index feature is proposed using bispectral analysis. The experimental results indicate that the BFC change is significantly different among emotional states of PD patients compared with HC. Also, the emotional connectivity pattern classified using Support Vector Machine (SVM) classifier yielded the highest accuracy for the new bispectral functional connectivity index. The PD patients showed emotional impairments as demonstrated by a poor classification performance. This finding suggests that decrease in the functional connectivity indices during emotional stimulation in PD, indicating functional disconnections between cortical areas.
authors.affiliation: Department of Biomedical Engineering, Sri Sivasubramaniya Nadar (SSN) College of Engineering, Old Mahabalipuram Road, Kalavakkam, Chennai, 603110 Tamilnadu, India.
authors.forename: R
authors.initials: R
authors.lastname: Yuvaraj
authors.affiliation: School of Mechatronic Engineering, University Malaysia Perlis (UniMAP), Campus Ulupauh, Arau, 02600 Perlis, Malaysia. Electronic address: murugappan@unimap.edu.my.
authors.forename: M
authors.initials: M
authors.lastname: Murugappan
authors.affiliation: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
authors.forename: U Rajendra
authors.initials: UR
authors.lastname: Acharya
authors.affiliation: Departments of Biomedical Engineering, Biomedical Informatics, Civil, Environmental and Geodetic Engineering, Electrical and Computer Engineering, Neurology and Neuroscience Ohio State University, Columbus, OH 43210, USA.
authors.forename: Hojjat
authors.initials: H
authors.lastname: Adeli
authors.affiliation: Neurology Unit, Department of Medicine, UKM Medical Center, Jalan Yaacob Latiff, 56000 Bandar Tun Razak, Kuala Lumpur, Malaysia.
authors.forename: Norlinah Mohamed
authors.initials: NM
authors.lastname: Ibrahim
authors.affiliation: Department of Education, University of Minho, Braga, Portugal.
authors.forename: Edgar
authors.initials: E
authors.lastname: Mesquita
mesh_terms: D001921:Brain / Q000503:physiopathology*; D004569:Electroencephalography / Q000379:methods*; D004644:Emotions / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009434:Neural Pathways / Q000503:physiopathology; D010300:Parkinson Disease / Q000145:classification / Q000503:physiopathology* / Q000523:psychology*; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine
keywords: Coherence; Correlation; EEG; Emotion; PSI; Parkinson’s disease; SVM