Diffusion-Inspired Shrinkage Functions and Stability Results for Wavelet Denoising
International Journal of Computer Vision
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Two-Class Pattern Discrimination via Recursive Optimization of Patrick-Fisher Distance
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Subband-adaptive shrinkage for denoising of ECG signals
EURASIP Journal on Applied Signal Processing
Hyper-trim shrinkage for denoising of ECG signal
Digital Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
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Brain-computer interface (BCI) systems use electro-encephalogram (EEG) data to control external electronic devices. The main task of BCI systems is to differentiate the classes of mental tasks from the EEG data. The EEG data is inherently complex and difficult to analyze due to interference by eye and muscle movements as well as electrical grid noise. In this paper we analyze shrinkage functions for signal filtering and propose a class-adaptive method for EEG data denoising. The results are evaluated using a Support Vector Machine.