Adaptive signal processing
Adaptive filter theory
Extended ICA removes artifacts from electroencephalographic recordings
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A novel adaptive nonlinear filter-based pipelined feed-forward second-order Volterra architecture
IEEE Transactions on Signal Processing
A sparse-interpolated scheme for implementing adaptive volterra filters
IEEE Transactions on Signal Processing
Baseband Volterra filters for implementing carrier basednonlinearities
IEEE Transactions on Signal Processing
Block adaptive filters with deterministic reference inputs forevent-related signals: BLMS and BRLS
IEEE Transactions on Signal Processing
Adaptive Combination of Volterra Kernels and Its Application to Nonlinear Acoustic Echo Cancellation
IEEE Transactions on Audio, Speech, and Language Processing
| Hi-index | 0.00 |
The electroencephalogram (EEG) is the most widely used method for diagnosis of brain diseases, where a good quality of recordings allows the proper interpretation and identification of physiological and pathological phenomena. However, EEG recordings are often contaminated by different kinds of noise. These annoying signals limit severely brain recording utility and, hence, have to be removed. To deal with this problem, in this paper an adaptive filtering framework is proposed for the enhancing of brain signal recordings. This new method is capable of reducing muscle and baseline noise in EEG signals with low EEG distortion and high noise cancellation. The advantages of the proposed method are demonstrated on real and synthetic brain signals with comparisons made to several benchmark methods. Results show that the proposed approach is preferable to the other systems by achieving a better trade-off between deleting noises and preserving inherent brain activities.