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Across-subjects classification of stimulus modality from human MEG high frequency activity

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Τίτλος: Across-subjects classification of stimulus modality from human MEG high frequency activity
Συγγραφείς: Westner, Britta U, Dalal, Sarang S, Hanslmayr, Simon, Staudigl, Tobias
Πηγή: Westner, B U, Dalal, S S, Hanslmayr, S & Staudigl, T 2018, 'Across-subjects classification of stimulus modality from human MEG high frequency activity', PLoS Computational Biology, vol. 14, no. 3, e1005938. https://doi.org/10.1371/journal.pcbi.1005938
Έτος έκδοσης: 2018
Συλλογή: Aarhus University: Research
Θεματικοί όροι: Acoustic Stimulation, Adult, Auditory Cortex/diagnostic imaging, Brain/diagnostic imaging, Female, Humans, Magnetoencephalography/methods, Male, Photic Stimulation, Signal Processing, Computer-Assisted, Signal-To-Noise Ratio, Support Vector Machine, Visual Cortex/diagnostic imaging, Young Adult, ATTENTION, MOVEMENT DIRECTION, TIME, RESPONSES, CAT VISUAL-CORTEX, AUDITORY-CORTEX, GAMMA-BAND OSCILLATIONS, INTRACRANIAL EEG, BRAIN, WORKING-MEMORY
Περιγραφή: Single-trial analyses have the potential to uncover meaningful brain dynamics that are obscured when averaging across trials. However, low signal-to-noise ratio (SNR) can impede the use of single-trial analyses and decoding methods. In this study, we investigate the applicability of a single-trial approach to decode stimulus modality from magnetoencephalographic (MEG) high frequency activity. In order to classify the auditory versus visual presentation of words, we combine beamformer source reconstruction with the random forest classification method. To enable group level inference, the classification is embedded in an across-subjects framework. We show that single-trial gamma SNR allows for good classification performance (accuracy across subjects: 66.44%). This implies that the characteristics of high frequency activity have a high consistency across trials and subjects. The random forest classifier assigned informational value to activity in both auditory and visual cortex with high spatial specificity. Across time, gamma power was most informative during stimulus presentation. Among all frequency bands, the 75 Hz to 95 Hz band was the most informative frequency band in visual as well as in auditory areas. Especially in visual areas, a broad range of gamma frequencies (55 Hz to 125 Hz) contributed to the successful classification. Thus, we demonstrate the feasibility of single-trial approaches for decoding the stimulus modality across subjects from high frequency activity and describe the discriminative gamma activity in time, frequency, and space.
Τύπος εγγράφου: article in journal/newspaper
Γλώσσα: English
ISSN: 1553-7358
Relation: info:eu-repo/semantics/altIdentifier/pmid/29529062; info:eu-repo/semantics/altIdentifier/pissn/1553-7358; info:eu-repo/semantics/altIdentifier/eissn/1553-7358
DOI: 10.1371/journal.pcbi.1005938
Διαθεσιμότητα: https://pure.au.dk/portal/en/publications/ee258de8-fd83-4e2e-9166-ce09f137676d
https://doi.org/10.1371/journal.pcbi.1005938
Rights: info:eu-repo/semantics/restrictedAccess
Αριθμός Καταχώρησης: edsbas.2F0C1894
Βάση Δεδομένων: BASE
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  Data: Across-subjects classification of stimulus modality from human MEG high frequency activity
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  Data: Westner, B U, Dalal, S S, Hanslmayr, S & Staudigl, T 2018, 'Across-subjects classification of stimulus modality from human MEG high frequency activity', PLoS Computational Biology, vol. 14, no. 3, e1005938. https://doi.org/10.1371/journal.pcbi.1005938
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  Data: Single-trial analyses have the potential to uncover meaningful brain dynamics that are obscured when averaging across trials. However, low signal-to-noise ratio (SNR) can impede the use of single-trial analyses and decoding methods. In this study, we investigate the applicability of a single-trial approach to decode stimulus modality from magnetoencephalographic (MEG) high frequency activity. In order to classify the auditory versus visual presentation of words, we combine beamformer source reconstruction with the random forest classification method. To enable group level inference, the classification is embedded in an across-subjects framework. We show that single-trial gamma SNR allows for good classification performance (accuracy across subjects: 66.44%). This implies that the characteristics of high frequency activity have a high consistency across trials and subjects. The random forest classifier assigned informational value to activity in both auditory and visual cortex with high spatial specificity. Across time, gamma power was most informative during stimulus presentation. Among all frequency bands, the 75 Hz to 95 Hz band was the most informative frequency band in visual as well as in auditory areas. Especially in visual areas, a broad range of gamma frequencies (55 Hz to 125 Hz) contributed to the successful classification. Thus, we demonstrate the feasibility of single-trial approaches for decoding the stimulus modality across subjects from high frequency activity and describe the discriminative gamma activity in time, frequency, and space.
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        Value: 10.1371/journal.pcbi.1005938
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