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Brain Computer Interface (BCI) provides a communication channel via computer between mind and environment. Extracting suitable and discriminant features is one of the most important stages in BCI Applications. Common spatial patterns (CSP) is a well-known feature extraction method; however, due to the non-stationary nature of EEG signals CSP should be updated through time. This paper proposes a novel...
In this paper, a general framework is proposed for simultaneous design of spatial and spectral filters, which are used to extract discriminant features from EEG signals in Brain Computer Interfacing (BCI) systems. This paper introduces Common Spatial Patterns (CSP) as a step-by-step filter optimization algorithm, and then proposes a generalized type of the CSP which is not limited in a specific optimization...
Common spatial patterns (CSP) has proved to be very successful in EEG feature extraction. To relax the presumption of strictly linear patterns in the CSP, nonlinear variants of the approach are proposed using the kernel method. However, they typically suffer from two main drawbacks: the problem of complexity and low generalization ability dealing with different subjects. To overcome these drawbacks,...
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