Moment to moment variability in functional brain networks during cognitive activity in EEG data
| Autor: | Nabaraj Dahal, Paul Gaertner, Bernadine Cocks, Naga Dasari, Nanda Nandagopal, Bruce H. Thomas, Vijayalaxmi Ramasamy |
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| Přispěvatelé: | Dasari, Naga M, Nandagopal, Nanda (D), Vijayalaxmi, Ramasamy, Cocks, Bernadine, Thomas, Bruce H, Dahal, Nabaraj, Gaertner, Paul |
| Rok vydání: | 2015 |
| Předmět: |
cognition
Adult Time Factors Information Theory Neuropsychological Tests Stimulus (physiology) Electroencephalography Machine learning computer.software_genre moment-to-moment graph metrics Young Adult Cognition Neural Pathways medicine Humans mutual information Brain Mapping Signal processing Computational neuroscience medicine.diagnostic_test business.industry General Neuroscience Functional brain network Brain General Medicine Mutual information Middle Aged Visualization Nonlinear Dynamics brain network variability Artificial intelligence Psychology business Neuroscience computer Cognitive load |
| Zdroj: | Journal of Integrative Neuroscience. 14:383-402 |
| ISSN: | 1757-448X 0219-6352 |
| DOI: | 10.1142/s0219635215500211 |
| Popis: | Functional brain networks (FBNs) are gaining increasing attention in computational neuroscience due to their ability to reveal dynamic interdependencies between brain regions. The dynamics of such networks during cognitive activity between stimulus and response using multi-channel electroencephalogram (EEG), recorded from 16 healthy human participants are explored in this research. Successive EEG segments of 500[Formula: see text]ms duration starting from the onset of cognitive stimulation have been used to analyze and understand the cognitive dynamics. The approach employs a combination of signal processing techniques, nonlinear statistical measures and graph-theoretical analysis. The efficacy of this approach in detecting and tracking cognitive load induced changes in EEG data is clearly demonstrated using graph metrics. It is revealed that most cognitive activity occurs within approximately 500[Formula: see text]ms of the stimulus presentation in addition to temporal variability in the FBNs. It is shown that mutual information (MI), a nonlinear measure, produces good correlations between the EEG channels thus enabling the construction of FBNs which are sensitive to cognitive load induced changes in EEG. Analyses of the dynamics of FBNs and the visualization approach reveal hard to detect subtle changes in cognitive function and hence may lead to a better understanding of cognitive processing in the brain. The techniques exploited have the potential to detect human cognitive dysfunction (impairments). |
| Databáze: | OpenAIRE |
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