Artifact Removal in Magnetoencephalogram Background Activity With Independent Component Analysis
نویسندگان
چکیده
منابع مشابه
Applying Independent Component Analysis to the Artifact Detection Problem in Magnetoencephalogram Background Recordings
IntroductIon The analysis of the electromagnetic brain activity can provide important information to help in the diagnosis of several mental diseases. Both electroencephalogram (EEG) and magnetoencephalogram (MEG) record the neural activity with high temporal resolution (Hämäläinen, Hari, Ilmoniemi, Knuutila, & Lounasmaa, 1993). Nevertheless, MEG offers some advantages over EEG. For example, in...
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Electroencephalography (EEG) is a fundamental diagnostic instrument for many neurological disorders, and it is the main tool for the investigation of the cognitive or pathological activity of the brain through the bioelectromagnetic fields that it generates. The correct interpretation of the EEG is misleading, both for clinicians’ visual evaluation and for automated procedures, because of artif...
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Independent Component Analysis (ICA) has emerged as a necessary preprocessing step when analyzing Electroencephalographic (EEG) data. While many studies reported on the use of ICA for EEG, most of these studies rely on visual inspection of the signal to detect those components that need to be removed from the signal. Little has been done on how to process EEG data in real-time, autonomously, an...
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Recent works have shown that artifact removal in biomedical signals can be performed by using Discrete Wavelet Transform (DWT) or Independent Component Analysis (ICA). It results often very difficult to remove some artifacts because they could be superimposed on the recordings and they could corrupt the signals in the frequency domain. The two conditions could compromise the performance of both...
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ژورنال
عنوان ژورنال: IEEE Transactions on Biomedical Engineering
سال: 2007
ISSN: 0018-9294
DOI: 10.1109/tbme.2007.894968