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Significance of gender, brain region and EEG band complexity analysis for Parkinson's disease classification using recurrence plots and machine learning algorithms.
Phys Eng Sci Med
January 2025
Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Bangalore, India.
Parkinson Disease (PD) is a complex neurological disorder attributed by loss of neurons generating dopamine in the SN per compacta. Electroencephalogram (EEG) plays an important role in diagnosing PD as it offers a non-invasive continuous assessment of the disease progression and reflects these complex patterns. This study focuses on the non-linear analysis of resting state EEG signals in PD, with a gender-specific, brain region-specific, and EEG band-specific approach, utilizing recurrence plots (RPs) and machine learning (ML) algorithms for classification.
View Article and Find Full Text PDFElectroencephalographic Resting-State Functional Connectivity of Benign Epilepsy with Centrotemporal Spikes.
J Clin Neurol
April 2019
Pediatric Clinical Neuroscience Center, Seoul National University Children's Hospital, Seoul, Korea.
Background And Purpose: We aimed to reveal resting-state functional connectivity characteristics based on the spike-free waking electroencephalogram (EEG) of benign epilepsy with centrotemporal spikes (BECTS) patients, which usually appears normal in routine visual inspection.
Methods: Thirty BECTS patients and 30 disease-free and age- and sex-matched controls were included. Eight-second EEG epochs without artifacts were sampled and then bandpass filtered into the delta, theta, lower alpha, upper alpha, and beta bands to construct the association matrix.
Selective impairment of hippocampus and posterior hub areas in Alzheimer's disease: an MEG-based multiplex network study.
Brain
May 2017
Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
Although frequency-specific network analyses have shown that functional brain networks are altered in patients with Alzheimer's disease, the relationships between these frequency-specific network alterations remain largely unknown. Multiplex network analysis is a novel network approach to study complex systems consisting of subsystems with different types of connectivity patterns. In this study, we used magnetoencephalography to integrate five frequency-band specific brain networks in a multiplex framework.
View Article and Find Full Text PDFChoosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction.
PLoS One
July 2017
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America.
Wavelet methods are widely used to decompose fMRI, EEG, or MEG signals into time series representing neurophysiological activity in fixed frequency bands. Using these time series, one can estimate frequency-band specific functional connectivity between sensors or regions of interest, and thereby construct functional brain networks that can be examined from a graph theoretic perspective. Despite their common use, however, practical guidelines for the choice of wavelet method, filter, and length have remained largely undelineated.
View Article and Find Full Text PDFWorking memory performance inversely predicts spontaneous delta and theta-band scaling relations.
Brain Res
April 2016
Department of Psychology, University of Utah, 380 S. 1530 E. BEHS 502, Salt Lake City, UT 84112, USA. Electronic address:
Electrophysiological studies have strongly implicated theta-band activity in human working memory processes. Concurrently, work on spontaneous, non-task-related oscillations has revealed the presence of long-range temporal correlations (LRTCs) within sub-bands of the ongoing EEG, and has begun to demonstrate their functional significance. However, few studies have yet assessed the relation of LRTCs (also called scaling relations) to individual differences in cognitive abilities.
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