Space–Time–Frequency Multi-Sensor Analysis for Motor Cortex Localization Using Magnetoencephalography
| Autor: | Victor Rohu, Nana Arizumi, Alim-Louis Benabid, Christelle Larzabal, Lilia Langar, Etienne Labyt, Vincent Auboiroux, Tetiana Aksenova, Ales Mishchenko |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
magnetoencephalography
Computer science Electroencephalography lcsh:Chemical technology Biochemistry Signal Article 050105 experimental psychology localization Analytical Chemistry 03 medical and health sciences symbols.namesake source imaging Spatio-Temporal Analysis 0302 clinical medicine medicine Humans 0501 psychology and cognitive sciences lcsh:TP1-1185 multi-sensor Electrical and Electronic Engineering Instrumentation Brain Mapping medicine.diagnostic_test business.industry 05 social sciences Motor Cortex time–frequency Pattern recognition Magnetoencephalography Inverse problem coefficient of determination Atomic and Molecular Physics and Optics Pearson product-moment correlation coefficient Time–frequency analysis cortex Electro encephalography symbols linear regression Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | Sensors, Vol 20, Iss 2706, p 2706 (2020) Sensors Volume 20 Issue 9 Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| Popis: | Brain source imaging and time frequency mapping (TFM) are commonly used in magneto/electro encephalography (M/EEG) imaging. However, these methods suffer from important limitations. Source imaging is based on an ill-posed inverse problem leading to instability of source localization solutions, has a limited capacity to localize high frequency oscillations and loses its robustness for induced responses (ill-defined trigger). The drawback of TFM is that it involves independent analysis of signals from a number of frequency bands, and from co-localized sensors. In the present article, a regression-based multi-sensor space&ndash time&ndash frequency analysis (MSA) approach, which integrates co-localized sensors and/or multi-frequency information, is proposed. To estimate task-specific brain activations, MSA uses cross-validated, shifted, multiple Pearson correlation, calculated from the time&ndash frequency transformed brain signal and the binary signal of stimuli. The results are projected from the sensor space onto the cortical surface. To assess MSA performance, the proposed method was compared to the weighted minimum norm estimate (wMNE) source imaging method, in terms of spatial selectivity and robustness against an ill-defined trigger. Magnetoencephalography (MEG) recordings were performed in fourteen subjects during two motor tasks: finger tapping and elbow flexion/extension. In particular, our results show that the MSA approach provides good localization performance when compared to wMNE and statistically significant improvement of robustness against ill-defined trigger. |
| Databáze: | OpenAIRE |
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