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- W2186164097 abstract "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 non- invasively study cognitive processes and the physiology of the brain. The analysis of EEG data and the extraction of information from this data is a difficult problem. This problem is exacerbated by the introduction of extraneous biologically generated and externally generated signals into the EEG. EEG data is used for development of brain-computer interfaces (BCIs). A brain-computer interface has been defined as a communication system that does not depend on the brain's normal output pathways of peripheral nerves and muscles (1). A BCI system allows users, typically people with motor disabilities, to communicate, via a computer, through their EEG signals. To increase the effectiveness of BCI systems it is necessary to find methods of increasing the signal-to-noise ratio (SNR) of the observed EEG signals. In the context of EEG driven BCIs, the signal is endogenous brain activity measured as voltage changes at the scalp while noise is any voltage change generated by other sources. These noise, or artifact, sources include: line noise from the power grid, eye blinks, eye movements, heart beat, breathing, and other muscle activity. Some artifacts, such as eye blinks, produce voltage changes of much higher amplitude than the endogenous brain activity. In this situation the data must be discarded unless the artifact can be removed from the data. The present paper gives overview of different techniques for artifact removal." @default.
- W2186164097 created "2016-06-24" @default.
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- W2186164097 date "2010-01-01" @default.
- W2186164097 modified "2023-09-24" @default.
- W2186164097 title "ARTIFACT REMOVAL FROM EEG RECORDINGS - AN OVERVIEW" @default.
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