چكيده به لاتين
Abstract:
Field potentials signal processing is a crutial step in brain computer interface (BCI). Artifact removal is a signal processing scheme which improved signal quality and decoding performance. In this tesis a novel automated and unsupervised method for removing artifacts from multichannel field potential signals is introduced which can be used in BCI applications. The method, which is called minimum noise estimate (MNE) filter is based on an iterative thresholding followed by Rayleigh quotient which tries to find an estimate of the noise and to minimize it over the original signal. MNE filter is capable to operate without any prior information about field potential signals. Thus it could be performed on electroencephalogram (EEG), electrocorticogram (ECoG) and local field potentials (LFP) signals. The performance of the proposed method is evaluated by its application on two different type of signals, i.e., ECoG and EEG datasets, through a decoding procedure. The results indicate that the proposed method significantly outperforms over well-known artifacts removal techniques such as common average referencing (CAR), Laplacian method, independent component analysis (ICA) and wavelet denoising approach. In addition, MNE filter has less time complexity compare to ICA and wavelet denosing. Thus it could be an interesting method for online implementation.
Keywords: Brain computer interface (BCI), Field potentials, Artifact Removal, Electrocorticogram (ECoG), Electroencephalogram (EEG).
