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In this work we test a technique based on independent component analysis (ICA), applied to single channel brain signals recorded through the electroencephalogram. Standard (or ensemble) ICA (enICA) requires multiple channel recordings to work, however when single of few channels are required enICA cannot be readily applied. Single channel ICA (scICA) can be performed by using the method of delays...
We present a method for decomposing MEG or EEG data (channel times time times trials) into a set of atoms with fixed spatial and time-frequency signatures. The spatial part (i.e., topography) is obtained by independent component analysis (ICA). We propose a frequency prewhitening procedure as a pre-processing step before ICA, which gives access to high frequency activity. The time-frequency part is...
In this work we propose a technique based on independent component analysis (ICA), applied to single or two channel(s) recordings of electroencephalogram (EEG) brain signals. Standard (ensemble) ICA requires multiple channel recordings to work, however when single of few channels are required ensemble ICA cannot be readily applied. Single channel ICA (temporal ICA) can be performed by preprocessed...
Signal averaging method is usually utilized for extracting the characteristics of event related potentials (ERPs). However, the amplitude and duration of ERPs are not constant for each stimulus due to the fluctuation of the subject's state, accordingly the appropriate selection of available data is crucial for realizing the accurate averaging. Independent component analysis (ICA) is one of powerful...
Blind source separation (BSS) methods such as independent component analysis (ICA) are increasingly being used in biomedical signal processing for decomposition of multivariate time-series, such as the multichannel electroencephalogram (EEG), into a set of underlying sources, some of which may reflect clinically relevant neurophysiological activity such as epileptic seizures or spikes. Tracking and...
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