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Simultaneous EEG-fMRI has proven to be useful in localizing interictal epileptic activity. However, the applicability of traditional GLM-based analysis is limited as interictal spikes are often not seen on the EEG inside the scanner. Therefore, we aim at extracting epileptic activity purely from the fMRI time series using independent component analysis(More)
The extraction of task-related single trial ERP features has recently gained much interest, in particular in simultaneous EEG-fMRI applications. In this study, a specific decomposition known as parallel factor analysis (PARAFAC) was used, in order to retrieve the task-related activity from the raw signals. Using visual detection task data, acquired in(More)
A new, automated way to obtain signatures of active motor units (MUs) from high density surface EMG recordings during voluntary contractions is presented. It relies on clustering of repetitive shapes corresponding to different MU action potentials (MUAPs) present. The number of clusters and the mean shapes of the MUAPs as observed on the electrode grid, are(More)
The decomposition of high-density surface EMG (HD-sEMG) interference patterns into the contribution of motor units is still a challenging task. We introduce a new, fast solution to this problem. The method uses a data-driven approach for selecting a set of electrodes to enable discrimination of present motor unit action potentials (MUAPs). Then, using(More)
INTRODUCTION This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Ad-vanced Methods for Neural Signals and Images". OBJECTIVES The study discusses a technique to automatically correct for effects of electrode grid displacement across serial surface EMG measurements with high-density electrode arrays(More)
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