Magnetoencephalography (MEG) and Electroencephalography (EEG) provides high temporal resolution which allows a detailed investigation of time related neuronal activation properties. In real-time analysis two major challenges have to be handled: the low signal-to-noise ratio (SNR) and the limited time available for computations.
We recently proposed Real-Time Clustered Multiple Signal Classification (RTC-MUSIC) as a real-time source localization algorithm, which can handle low SNRs and can cope with the high computational effort. It provides correlation information together with sparse source localization results, which can, e.g., be used to identify evoked responses with an increased sensitivity.
RTC-MUSIC source localizations are achieved by clustering the forward solution based on an anatomical brain atlas and the optimization of the localization’s scanning process. We evaluated RTC-MUSIC analyzing MEG auditory and somatosensory data. The results demonstrate that the proposed real-time method localizes sources reliable. For the auditory experiment the most dominant correlated source pair was located in the superior temporal gyrus ipsi- and contra-lateral. The highest activation in the somatosensory experiment was reliable located in the contra-lateral primary somatosensory cortex (SI). Window sizes of , i.e., post auditory and post median-nerve stimulation interval, turned out to be best to achieve these results. In addition to the localization precision was the real-time delay evaluated. Window sizes of 100 ms together with sampling rates of consume a localization time of , which allows us to follow the real-time data stream continuously.