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

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Across-subjects classification of stimulus modality from human MEG high frequency activity. / Westner, Britta U.; Dalal, Sarang S.; Hanslmayr, Simon; Staudigl, Tobias.

In: PLoS Computational Biology, Vol. 14, No. 3, e1005938, 12.03.2018.

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Westner, Britta U. ; Dalal, Sarang S. ; Hanslmayr, Simon ; Staudigl, Tobias. / Across-subjects classification of stimulus modality from human MEG high frequency activity. In: PLoS Computational Biology. 2018 ; Vol. 14, No. 3.

Bibtex

@article{352d3f94f7334215951c586c196eec61,
title = "Across-subjects classification of stimulus modality from human MEG high frequency activity",
abstract = "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.",
author = "Westner, {Britta U.} and Dalal, {Sarang S.} and Simon Hanslmayr and Tobias Staudigl",
year = "2018",
month = mar,
day = "12",
doi = "10.1371/journal.pcbi.1005938",
language = "English",
volume = "14",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science (PLOS)",
number = "3",

}

RIS

TY - JOUR

T1 - Across-subjects classification of stimulus modality from human MEG high frequency activity

AU - Westner, Britta U.

AU - Dalal, Sarang S.

AU - Hanslmayr, Simon

AU - Staudigl, Tobias

PY - 2018/3/12

Y1 - 2018/3/12

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85044757062&partnerID=8YFLogxK

U2 - 10.1371/journal.pcbi.1005938

DO - 10.1371/journal.pcbi.1005938

M3 - Article

C2 - 29529062

AN - SCOPUS:85044757062

VL - 14

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

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