Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy.

Simultaneous electroencephalogram and functional magnetic resonance imaging (EEG-fMRI) is a unique combined technique that provides synergy in the understanding and localization of seizure onset in epilepsy. However, reported experimental protocols for EEG-fMRI recordings fail to address details about conducting such procedures on epilepsy patients. In addition, these protocols are limited solely to research settings. To fill the gap between patient monitoring in an epilepsy monitoring unit (EMU) and conducting research with an epilepsy patient, we introduce a unique EEG-fMRI recording protocol of epilepsy during the interictal period. The use of an MR conditional electrode set, which can also be used in the EMU for a simultaneous scalp EEG and video recording, allows an easy transition of EEG recordings from the EMU to the scanning room for concurrent EEG-fMRI recordings. Details on the recording procedures using this specific MR conditional electrode set are provided. In addition, the study explains step-by-step EEG processing procedures to remove the imaging artifacts, which can then be used for clinical review. This experimental protocol promotes an amendment to the conventional EEG-fMRI recording for enhanced applicability in both clinical (i.e., EMU) and research settings. Furthermore, this protocol provides the potential to expand this modality to postictal EEG-fMRI recordings in the clinical setting.

Kernel Temporal Differences for EEG-based Reinforcement Learning Brain Machine Interfaces.

Kernel temporal differences (KTD) (λ) algorithm integrated in Q-learning (Q-KTD) has shown its applicability and feasibility for reinforcement learning brain machine interfaces (RLBMIs). RLBMI with its unique learning strategy based on trial-error allows continuous learning and adaptation in BMIs. Q-KTD has shown good performance in both open and closed-loop experiments for finding a proper mapping from neural intention to control commands of an external device. However, previous studies have been limited to intracortical BMIs where monkey's firing rates from primary motor cortex were used as inputs to the neural decoder. This study provides the first attempt to investigate Q-KTD algorithm's applicability in EEG-based RLBMIs. Two different publicly available EEG data sets are considered, we refer to them as Data set A and Data set B. EEG motor imagery tasks are integrated in a single step center-out reaching task, and we observe the open-loop RLBMI experiments reach 100% average success rates after sufficient learning experience. Data set A converges after approximately 20 epochs for raw features and Data set B shows convergence after approximately 40 epochs for both raw and Fourier transform features. Although there still exist challenges to overcome in EEG-based RLBMI using Q-KTD, including increasing the learning speed, and optimization of a continuously growing number of kernel units, the results encourage further investigation of Q-KTD in closed-loop RLBMIs using EEG. Clinical Relevance- This study supports feasibility of noninvasive EEG-based RLBMI implementations and addresses benefits and challenges of RLBMI using EEG.