Spectral analysis versus signal complexity methods for assessing attention related activity in human EEG

We aimed to find the most effective analytical method for assessment of attention related activity to be used in neurofeedback training. We compared commonly used spectral EEG methods with those measuring signal complexity - based on calculation of entropy and fractal dimension. The 14 subjects were...

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Bibliographic Details
Published inConference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.) Vol. 2019; pp. 4517 - 4520
Main Authors Malinowska, Urszula, Wojciechowski, Jakub, Waligora, Marek, Wrobel, Andrzej, Niedbalski, Pawel, Rogala, Jacek
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.07.2019
Subjects
Attention
Complexity theory
Electrodes
Electroencephalography
Entropy
Fractals
Humans
Microsoft Windows
Neurofeedback
Spectrum Analysis
Task analysis
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Summary:We aimed to find the most effective analytical method for assessment of attention related activity to be used in neurofeedback training. We compared commonly used spectral EEG methods with those measuring signal complexity - based on calculation of entropy and fractal dimension. The 14 subjects were examined with a modified delayed matching-to-sample task. All investigated methods revealed significant differences of EEG signals recorded in control and attentional trials, however the selection of signals with such differences varied between subjects and applied methods. The results indicated: (i) the importance of the individual analysis of signals from each subject and session, (ii) benefits of applying signal complexity methods to support spectral analysis in a further application and (iii) an advantage of the signal complexity method, carrying information of assembles of spectral components, over common spectral methods.
ISSN:1557-170X
1558-4615
DOI:10.1109/EMBC.2019.8856798