Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizur...

In recent years, many studies have shown that music can reflect quantifiable physiological effects on human. However, in the part of EEG, not many research projects have made use of EEG to verify the music influence on human brain activity. This research has emphasized on the development of analytical tools and methods for bio-signals, especially focused on the part of EEG with musical signal s...

BACKGROUND Noise reduction techniques play an essential role in EEG signal processing applications. A variety of methods are currently in use, including those based on linear filtering and adaptive noise cancellation, as well as subspace-based methods using singular value decomposition (SVD). SVD offers a robust method to decompose the data matrix into signal and noise subspaces. However, the S...

BACKGROUND Evoked and induced activities are two typical components in the EEG and MEG time series after a stimulation. While evoked activity is phase-locked to the stimulus, induced activity is not. Present analysis methods are able to detect non-phase-locked parts of the signal, however, they do not improve the signal-to-noise ratio (SNR) of these signal components. RESULTS We present a new...