A Robust Algorithm for Multichannel Eeg Compressed Sensing with Mixed Noise Compressed Sensing (CS) has been widely used for telemonitoring of multichannel electroencephalogram (EEG) signals through wireless bodayarea networks. However, most of existing multichannel EEG CS algorithms have not taken the noise into consideation or only considered the Gaussian noise. In this paper, we propose a robust multichannel EEG CS algorithm based on sparse and low rank representation in the presence of mixed noise (SLRMN). Our proposed algorithm involves an optimization model that takes both the Gaussian noise and the implusive noise into consideration, and the alternative direction method of multipliers (ADMM) is also developed to solve the proposed SLRMN. Moreover, we apply our method to EEG database to demonstrate the dramatic improvements in signal recovery compared to the stateoftheart multichannel EEG CS methods, especially in the presence of mixed noise.

Motor Imagery Classification via ClusteredGroup Sparse Representation A significant limitation on the wide use of a Motor Imagery (MI) BrainComputer Interfaces is the acquisition of electroenchephalogram (EEG) data over a significant amount of EEG trials to achieve accurate classification. In this work, we propose a new sparse representation classification scheme to overcome the above limitation by training the proposed model using only a limited amount of EEG trials. Our algorithm extends current sparse representation classification schemes by exploiting the group sparsity of EEG features. We have evaluated the proposed algorithm on two MI EEG datasets, showing stateoftheart performance against well known classification methods of MI BCI literature.