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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...
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...
Electroencephalogram (EEG) related to fast eye movement (saccade), has been the subject of application oriented research by our group toward developing a brain-computer interface (BCI). Our goal is to develop novel BCI based on eye movements system employing EEG signals online. Most of the analysis of the saccade-related EEG data has been performed using ensemble averaging approaches. However, ensemble...
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)...
Electroencephalogram (EEG) related to fast eye movement (saccade), has been the subject of application oriented research by our group toward developing a brain-computer interface (BCI). Our goal is to develop novel BCI based on eye movements system employing EEG signals online. Most of the analysis of the saccade-related EEG data has been performed using ensemble averaging approaches. However, ensemble...
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)...
Electroencephalogram (EEG) related to fast eye movement (saccade), has been the subject of application oriented research by our group toward developing a brain-computer interface (BCI). Our goal is to develop novel BCI based on eye movements system employing EEG signals online. Most of the analysis of the saccade-related EEG data has been performed using ensemble averaging approaches. However, ensemble...
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