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Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression.
Sensors (Basel)
November 2020
Department of Computer Engineering, Gachon University, Sungnam-si 13306, Korea.
To have an objective depression diagnosis, numerous studies based on machine learning and deep learning using electroencephalogram (EEG) have been conducted. Most studies depend on one-dimensional raw data and required fine feature extraction. To solve this problem, in the EEG visualization research field, short-time Fourier transform (STFT), wavelet, and coherence commonly used as method s for transferring EEG data to 2D images.
View Article and Find Full Text PDFAge-Related Distinctions in EEG Signals during Execution of Motor Tasks Characterized in Terms of Long-Range Correlations.
Sensors (Basel)
October 2020
Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Universitetskaya Str. 1, 420500 Innopolis, Russia.
The problem of revealing age-related distinctions in multichannel electroencephalograms (EEGs) during the execution of motor tasks in young and elderly adults is addressed herein. Based on the detrended fluctuation analysis (DFA), differences in long-range correlations are considered, emphasizing changes in the scaling exponent α. Stronger responses in elderly subjects are confirmed, including the range and rate of increase in α.
View Article and Find Full Text PDFLittle effort with big effect - implementing the new IFCN 2017 recommendations on standard EEGs.
Clin Neurophysiol
November 2018
Department of Neurology, Cantonal Hospital Aarau, Aarau, Switzerland; Department of Neurology, University Hospital Basel, Basel, Switzerland.
Robust Averaging of Covariances for EEG Recordings Classification in Motor Imagery Brain-Computer Interfaces.
Neural Comput
June 2017
School of Information and Automation Engineering, Università Politecnica delle Marche, Ancona 1-60131, Italy, and Dipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche, 1-60131, Italy
The estimation of covariance matrices is of prime importance to analyze the distribution of multivariate signals. In motor imagery-based brain-computer interfaces (MI-BCI), covariance matrices play a central role in the extraction of features from recorded electroencephalograms (EEGs); therefore, correctly estimating covariance is crucial for EEG classification. This letter discusses algorithms to average sample covariance matrices (SCMs) for the selection of the reference matrix in tangent space mapping (TSM)-based MI-BCI.
View Article and Find Full Text PDFA proposed role for routine EEGs in patients with consciousness disorders.
Ann Neurol
January 2015
The Rockefeller University, New York, NY; Feil Family Brain and Mind Research Institute, Department of Neurology, Weill Cornell Medical College, New York, NY.
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