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Statistical non-parametric mapping in sensor space

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Abstract

Establishing the significance of observed effects is a preliminary requirement for any meaningful interpretation of clinical and experimental Electroencephalography or Magnetoencephalography (MEG) data. We propose a method to evaluate significance on the level of sensors whilst retaining full temporal or spectral resolution. Input data are multiple realizations of sensor data. In this context, multiple realizations may be the individual epochs obtained in an evoked-response experiment, or group study data, possibly averaged within subject and event type, or spontaneous events such as spikes of different types. In this contribution, we apply Statistical non-Parametric Mapping (SnPM) to MEG sensor data. SnPM is a non-parametric permutation or randomization test that is assumption-free regarding distributional properties of the underlying data. The method, referred to as Maps SnPM, is demonstrated using MEG data from an auditory mismatch negativity paradigm with one frequent and two rare stimuli and validated by comparison with Topographic Analysis of Variance (TANOVA). The result is a time- or frequency-resolved breakdown of sensors that show consistent activity within and/or differ significantly between event or spike types. TANOVA and Maps SnPM were applied to the individual epochs obtained in an evoked-response experiment. The TANOVA analysis established data plausibility and identified latencies-of-interest for further analysis. Maps SnPM, in addition to the above, identified sensors of significantly different activity between stimulus types.

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Acknowledgements

The authors wish to thank Chia-Ying Lee (Institute of Linguistics, Academia Sinica, Taipei, Taiwan) for kindly providing the MMN data, acquisition of which was supported by research grants from Academia Sinica, Taipei, Taiwan (AS-99-TP-AC1 and AS-102-TP-C06).

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Authors and Affiliations

  1. Compumedics Europe GmbH, Heußweg 25, 20255, Hamburg, Germany

    Michael Wagner, Reyko Tech, Manfred Fuchs, Jörn Kastner & Fernando Gasca

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  1. Michael Wagner
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  2. Reyko Tech
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  3. Manfred Fuchs
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  4. Jörn Kastner
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  5. Fernando Gasca
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Correspondence to Michael Wagner.

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Conflict of interest

The CURRY software used in this submission is a commercial product of Compumedics USA, Charlotte, NC, USA. The authors of this paper are employees of Compumedics Europe GmbH, Hamburg, Germany. Both Compumedics Europe GmbH and Compumedics USA are subsidiaries of Compumedics Ltd., Melbourne, Australia.

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Wagner, M., Tech, R., Fuchs, M. et al. Statistical non-parametric mapping in sensor space. Biomed. Eng. Lett. 7, 193–203 (2017). https://doi.org/10.1007/s13534-017-0015-6

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