Real-Time Clustered Multiple Signal Classification (RTC-MUSIC)
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Magnetoencephalography (MEG) and electroencephalography provide a high temporal resolution, which allows estimation of the detailed time courses of neuronal activity. However, in real-time analysis of these data two major challenges must be handled: the low signal-to-noise ratio (SNR) and the limited time available for computations. In this work, we present real-time clustered multiple signal classification (RTC-MUSIC) a real-time source localization algorithm, which can handle low SNRs and can reduce the computational effort. It provides correlation information together with sparse source estimation results, which can, e.g., be used to identify evoked responses with high sensitivity. RTC-MUSIC clusters the forward solution based on an anatomical brain atlas and optimizes the scanning process inherent to MUSIC approaches. We evaluated RTC-MUSIC by analyzing MEG auditory and somatosensory data. The results demonstrate that the proposed method localizes sources reliably. For the auditory experiment the most dominant correlated source pair was located bilaterally in the superior temporal gyri. The highest activation in the somatosensory experiment was found in the contra-lateral primary somatosensory cortex.
KeywordsReal-time Source estimation RAP-MUSIC RTC-MUSIC Powell’s conjugate direction method K-means clustering
This work was supported by the National Institutes of Health (NIH, Grants 4R01EB009048 and 5P41EB015896), European Union’s Horizon 2020 research and innovation program under grant agreement No 686865 the German Research Foundation (DFG, Grant Ba 4858/1-1), the Thuringian Ministry of Science under Grant Number 2015 FGR 0085 and the German Academic Exchange Service (DAAD).
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