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Whether "movement" or "rest" in Electroencephalogram (EEG) plays an important role in the domain of brain computer interface (BCI). Hidden Markov model (HMM)-AR might be a useful tool in EEG pattern classification since EEG data is a multivariate time series data which contains noise and artifacts. The methods are presented for EEG pattern classification which jointly employ Laplacian...
Extracting reach information from brain signals is of great interest to the fields of brain-computer interfaces (BCIs) and human motor control. To date, most work in this area has focused on invasive intracranial recordings; however, successful decoding of reach targets from noninvasive electroencephalogram (EEG) signals would be of great interest. In this article, we show that EEG signals contain...
Non invasive brain-computer interfaces (BCI) allow people to communicate by modulating features of their electroencephalogram (EEG). Spatiotemporal filtering has a vital role in multi-class, EEG based BCI. In this study, we used a novel combination of principle component analysis, independent component analysis and dipole source localization to design a spatiotemporal multiple source tuning (SPAMSORT)...
Non invasive brain-computer interfaces (BCI) allow people to communicate by modulating features of their electroencephalogram (EEG). Spatiotemporal filtering has a vital role in multi-class, EEG based BCI. In this study, we used a novel combination of principle component analysis, independent component analysis and dipole source localization to design a spatiotemporal multiple source tuning (SPAMSORT)...
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