Artifact Removing From Eeg Recordings Using Independent Component Analysis with High-order Statistics
نویسندگان
چکیده
منابع مشابه
Improved rejection of artifacts from EEG data using high-order statistics and independent component analysis
While it is now generally accepted that independent component analysis (ICA) is a good tool for isolating both artifacts and cognition-related processes in EEG data, there is little definite proof that data preprocessed using ICA is more effective than artifact rejection on raw channel data, especially when more subtle signal processing methods are used to detect artifacts. Here we applied five...
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Detecting artifacts produced in EEG data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG research. It is now widely accepted that independent component analysis (ICA) may be a useful tool for isolating artifacts and/or cortical processes from electroencephalographic (EEG) data. We present results of simulations demonstrating that ICA decomposition, h...
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Independent Component Analysis (ICA) has been widely used for separating artifacts from Electroencephalographic (EEG) signals. Still, a few challenging problems remain. First, in real-time applications, visual inspection of components should be replaced with an automatic identification method or a heuristic for artifacts detection. Second, as we will explain more in the paper, we expect to have...
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Electrical impulses generated by nerve firings in the brain diffuse through the head and can be measured by electrodes placed on the scalp, is known as electroencephalogram (EEG) and was first measured in humans by Hans Berger in 1929. The EEG gives a coarse view of neural activity and has been used to noninvasively study cognitive processes and the physiology of the brain. The analysis of EEG ...
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ژورنال
عنوان ژورنال: International Journal of Mathematical Models and Methods in Applied Sciences
سال: 2021
ISSN: 1998-0140
DOI: 10.46300/9101.2021.15.11