Denoising of multiscale/multiresolution structural feature dictionaries for rapid training of a brain computer interface

Multichannel neural activities such as EEG or ECoG in a brain computer interface can be classified with subset selection algorithms running on large feature dictionaries describing subject specific features in spectral, temporal and spatial domain. While providing high accuracies in classification, the subset selection techniques are associated with long training times due to the large feature set constructed from multichannel neural recordings. In this paper we study a novel denoising technique for reducing the dimensionality of the feature space which decreases the computational complexity of the subset selection step radically without causing any degradation in the final classification accuracy. The denoising procedure was based on the comparison of the energy in a particular time segment and in a given scale/level to the energy of the raw data. By setting denoising threshold a priori the algorithm removes those nodes which fail to capture the energy in the raw data in a given scale. We provide experimental studies towards the classification of motor imagery related multichannel ECoG recordings for a brain computer interface. The denoising procedure was able to reach the same classification accuracy without denoising and a computational complexity around 5 times smaller. We also note that in some cases the denoised procedure performed better classification.