A comparative study of motor imagery based BCI classifiers on EEG and iEEG data There are many stateoftheart Brain Computer Interface (BCI) classification algorithms designed to perform well when applied to signals acquired using electroencephalography (EEG). EEG has the advantage of being noninvasive in nature, easy to use, and effective in capturing signals in the mu (713 Hz) and beta (1330 Hz) bands during motor imagery tasks. However, EEG recordings are more susceptible to movement artifacts and capture a lower frequency of neural activity when compared with invasive techniques such as electrocorticography (ECoG) or intracranial EEG (iEEG). In this paper, we analyze the performance of four different EEG motor imagery classification algorithms (both classical machine learning methods and deep learningbased methods) on a twohand motor imagery task using both EEG and iEEG data sets. Using various feature visualization techniques, we provide insight into why deep learningbased classifiers designed to learn features endtoend may perform better than the classical machine learningbased models. We also showed on average iEEGbased motor imagery BCIs, using our iEEG data set, do not perform as well as EEGbased BCIs. This work provides a starting point for the implementation of BCI applications using iEEG data.

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.