Reducing the Number of MEG/EEG Trials Needed for the Estimation of Brain Evoked Responses: A Bootstrap Approach
| Autor: | Artur Matysiak, Cezary Sielużycki, Reinhard König, D. Robert Iskander |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
0206 medical engineering Biomedical Engineering 02 engineering and technology Electroencephalography Signal-to-noise ratio medicine Humans Reliability (statistics) Estimation Brain Mapping medicine.diagnostic_test signal averaging Artifacts Brain MEG Magnetoencephalography Bootstrap EEG Electroencephalograpy parameter estimation business.industry Pattern recognition 020601 biomedical engineering Matching pursuit Range (mathematics) Bootstrapping (electronics) Artificial intelligence Signal averaging business |
| Zdroj: | IEEE transactions on biomedical engineering, 68(7):2301-2312 IEEE Transactions on Biomedical Engineering |
| ISSN: | 1558-2531 |
| Popis: | Objective: A common problem in magnetoencephalographic (MEG) and electroencephalographic (EEG) experimental paradigms relying on the estimation of brain evoked responses is the lengthy time of the experiment, which stems from the need to acquire a large number of repeated recordings. Using a bootstrap approach, we aim at reliably reducing the number of these repeated trials. Methods: To this end, we assessed five variants of non-parametric bootstrapping based on the classical signal-plus-noise model constituting the foundation of signal averaging in MEG/EEG. We explain which of these approaches should and which should not be used for the aforementioned purpose, and why. Results: We present results for two advocated bootstrap variants applied to auditory MEG data. The ensuing trial-averaged magnetic fields served as input to the estimation of cortical source generators, with spatio-temporal matching pursuit as an example of an inverse solution technique. We propose, for a wide range of trial numbers, a general framework to evaluate the statistical properties of the parameter estimates for source locations and related time courses. Conclusion: The proposed bootstrap framework offers a systematic approach to reduce the number of trials required to estimate the evoked response. The general validity of our findings is neither bound to any particular type of MEG/EEG data nor to any specific source localization method. Significance: Practical implications of this work relate to the optimization of acquisition time of MEG/EEG experiments, thus reducing stress for the subjects (especially for patients) and minimizing related artifacts. |
| Databáze: | OpenAIRE |
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