Machine Learning
Optimization: algorithms and consistent approximations
Optimization: algorithms and consistent approximations
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Blind source separation via generalized eigenvalue decomposition
The Journal of Machine Learning Research
Second-Order Bilinear Discriminant Analysis
The Journal of Machine Learning Research
Optimum Spatio-Spectral Filtering Network for Brain–Computer Interface
IEEE Transactions on Neural Networks
| Hi-index | 0.01 |
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 constraint. Moreover, it is shown that how this generalization can be extended to a spatio-spectral filter estimation scheme. Then, two specific versions of the generalized CSP are proposed, where a specific target function and optimization constraint are used for estimating the spatial and spectral filters. Unlike the traditional CSP which is not very closely linked to the classification accuracy, the proposed algorithms are able to be more directly aimed at achieving better accuracy and stability. Experimental results obtained from applying the introduced methods on the recorded imagery signals from two datasets, demonstrate considerable improvement in the classification accuracy and stability compared to the standard CSP and other similar methods.