Standard

GPU-based high-performance computing of multichannel EEG phase wavelet synchronization. / Efitorov, Alexander; Knyazeva, Irina; Yulia, Boytsova; Danko, Sergey.

In: Procedia Computer Science, Vol. 123, 01.01.2018, p. 128-133.

Research output: Contribution to journal › Conference article › peer-review

Harvard

Efitorov, A, Knyazeva, I, Yulia, B & Danko, S 2018, 'GPU-based high-performance computing of multichannel EEG phase wavelet synchronization', Procedia Computer Science, vol. 123, pp. 128-133. https://doi.org/10.1016/j.procs.2018.01.021

APA

Efitorov, A., Knyazeva, I., Yulia, B., & Danko, S. (2018). GPU-based high-performance computing of multichannel EEG phase wavelet synchronization. Procedia Computer Science, 123, 128-133. https://doi.org/10.1016/j.procs.2018.01.021

Vancouver

Efitorov A, Knyazeva I, Yulia B, Danko S. GPU-based high-performance computing of multichannel EEG phase wavelet synchronization. Procedia Computer Science. 2018 Jan 1;123:128-133. https://doi.org/10.1016/j.procs.2018.01.021

Author

Efitorov, Alexander ; Knyazeva, Irina ; Yulia, Boytsova ; Danko, Sergey. / GPU-based high-performance computing of multichannel EEG phase wavelet synchronization. In: Procedia Computer Science. 2018 ; Vol. 123. pp. 128-133.

BibTeX

@article{6571b39d151e4b6d9676b8d28bbbf437,
title = "GPU-based high-performance computing of multichannel EEG phase wavelet synchronization",
abstract = "The work is devoted to GPU-based high performance realization of algorithm for wavelet phase synchronization. Wavelet phase coherence was applied for analyzing brain activity in states with different degrees of mental and sensory attention. In the analysis of electroencephalographic correlates of mental states, as a rule the focus is on the analysis of the spectral power of a quasi-stationary EEG or task-related power of time-frequency EEG spectra. The analysis of the wavelet phase coherence provides additional information on the organization of brain activity, but requires more computing time. Fast implementation can simplify the use of this method in practice.",
keywords = "Analysis of non-stationary signals, EEG analysis, Graphics processing unit(GPU), Wavelet phase coherence",
author = "Alexander Efitorov and Irina Knyazeva and Boytsova Yulia and Sergey Danko",
year = "2018",
month = jan,
day = "1",
doi = "10.1016/j.procs.2018.01.021",
language = "English",
volume = "123",
pages = "128--133",
journal = "Procedia Computer Science",
issn = "1877-0509",
publisher = "Elsevier",
note = "8th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2017 ; Conference date: 01-08-2017 Through 06-08-2017",

}

RIS

TY - JOUR

T1 - GPU-based high-performance computing of multichannel EEG phase wavelet synchronization

AU - Efitorov, Alexander

AU - Knyazeva, Irina

AU - Yulia, Boytsova

AU - Danko, Sergey

PY - 2018/1/1

Y1 - 2018/1/1

N2 - The work is devoted to GPU-based high performance realization of algorithm for wavelet phase synchronization. Wavelet phase coherence was applied for analyzing brain activity in states with different degrees of mental and sensory attention. In the analysis of electroencephalographic correlates of mental states, as a rule the focus is on the analysis of the spectral power of a quasi-stationary EEG or task-related power of time-frequency EEG spectra. The analysis of the wavelet phase coherence provides additional information on the organization of brain activity, but requires more computing time. Fast implementation can simplify the use of this method in practice.

AB - The work is devoted to GPU-based high performance realization of algorithm for wavelet phase synchronization. Wavelet phase coherence was applied for analyzing brain activity in states with different degrees of mental and sensory attention. In the analysis of electroencephalographic correlates of mental states, as a rule the focus is on the analysis of the spectral power of a quasi-stationary EEG or task-related power of time-frequency EEG spectra. The analysis of the wavelet phase coherence provides additional information on the organization of brain activity, but requires more computing time. Fast implementation can simplify the use of this method in practice.

KW - Analysis of non-stationary signals

KW - EEG analysis

KW - Graphics processing unit(GPU)

KW - Wavelet phase coherence

UR - http://www.scopus.com/inward/record.url?scp=85045670058&partnerID=8YFLogxK

U2 - 10.1016/j.procs.2018.01.021

DO - 10.1016/j.procs.2018.01.021

M3 - Conference article

AN - SCOPUS:85045670058

VL - 123

SP - 128

EP - 133

JO - Procedia Computer Science

JF - Procedia Computer Science

SN - 1877-0509

T2 - 8th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2017

Y2 - 1 August 2017 through 6 August 2017

ER -

ID: 36122212