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A relatively new neuroimaging modality is simultaneous EEG and fMRI. Though such a multi-modal acquisition is attractive given that it can exploit the temporal resolution of EEG and spatial resolution of fMRI, it comes with unique signal processing and pattern classification challenges. In this paper I will review some our work at developing signal processing and pattern recognition for analysis of simultaneous EEG and fMRI, with a focus on those algorithms enabling a single-trial analysis of the neural signal. In general, these algorithms exploit the multivariate nature of the EEG, removing MR induced artifacts and classifying event-related signals that then can be correlated with the BOLD signal to yield specific fMRI activations.