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A Brain-Computer Interface (BCI) allows the direct communication between humans and computers by analyzing brain activity. The oddball paradigm allows detecting event-related potentials (ERPs), like the P300 wave, on targets selected by the user. While this paradigm provides the location of the P300 wave in the signal, its exact location remains a hypothesis and depends on the subject. This paper...
Independent component analysis (ICA) is one statistical method closely related to the method called blind signal separation. In this paper, three typical ICA algorithms FastICA, Infomax and SOBI are utilized for spatial filtering and feature extraction in a four-task brain-computer interface (BCI) by decomposing EEG signals into independent sources. These algorithms are applied to five data sets recorded...
One of the preprocessors can be used to improve the performance of brain-computer interface (BCI) systems is independent component analysis (ICA). ICA is a signal processing technique in which observed random data are transformed into components that are statistically independent from each other. This suggests the possibility of using ICA to separate different independent brain activities during motor...
This study presents a comparison of two methods to extract features for the classification of wrist movements (flexion, extension, pronation, supination). For the first method, a set of 160 features was extracted from the filtered time and frequency domain EEG data and its alpha, beta, and theta bands. For the second method, a set of 40 features per movement type was extracted from the ICA-calculated...
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