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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, 03.2018.

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Author

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{ee258de8fd834e2e9166ce09f137676d,
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.",
keywords = "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",
author = "Westner, {Britta U} and Dalal, {Sarang S} and Simon Hanslmayr and Tobias Staudigl",
year = "2018",
month = "3",
doi = "10.1371/journal.pcbi.1005938",
language = "English",
volume = "14",
journal = "P L o S Computational Biology (Online)",
issn = "1553-7358",
publisher = "public library of science",
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

Y1 - 2018/3

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.

KW - Acoustic Stimulation

KW - Adult

KW - Auditory Cortex/diagnostic imaging

KW - Brain/diagnostic imaging

KW - Female

KW - Humans

KW - Magnetoencephalography/methods

KW - Male

KW - Photic Stimulation

KW - Signal Processing, Computer-Assisted

KW - Signal-To-Noise Ratio

KW - Support Vector Machine

KW - Visual Cortex/diagnostic imaging

KW - Young Adult

KW - ATTENTION

KW - MOVEMENT DIRECTION

KW - TIME

KW - RESPONSES

KW - CAT VISUAL-CORTEX

KW - AUDITORY-CORTEX

KW - GAMMA-BAND OSCILLATIONS

KW - INTRACRANIAL EEG

KW - BRAIN

KW - WORKING-MEMORY

U2 - 10.1371/journal.pcbi.1005938

DO - 10.1371/journal.pcbi.1005938

M3 - Journal article

C2 - 29529062

VL - 14

JO - P L o S Computational Biology (Online)

JF - P L o S Computational Biology (Online)

SN - 1553-7358

IS - 3

M1 - e1005938

ER -