Background: Repetitive transcranial magnetic stimulation (rTMS) of the left dorsolateral prefrontal cortex (DLPFC) is an effective treatment for major depressive disorder (MDD), but response rates are low and effect sizes small. Synchronizing TMS pulses with instantaneous brain oscillations can reduce variability and increase efficacy of TMS-induced plasticity. Objective: To study whether brain oscillation-synchronized rTMS is feasible, safe and has neuromodulatory effects when targeting the DLPFC of patients with MDD. Methods: Using real-time EEG-triggered TMS we conducted a pseudo-randomized controlled singlesession crossover trial of brain oscillation-synchronized rTMS of left DLPFC in 17 adult patients with antidepressant-resistant MDD. Stimulation conditions in separate sessions were: (1) rTMS triggered at the negative EEG peak of instantaneous alpha oscillations (alpha-synchronized rTMS), (2) a variation of intermittent theta-burst stimulation (modified iTBS), and (3) a random alpha phase control condition. Results: Triggering TMS at the negative peak of instantaneous alpha oscillations by real-time analysis of the electrode F5 EEG signal was successful in 15 subjects. Two subjects reported mild transient discomfort at the site of stimulation during stimulation; no serious adverse events were reported. Alphasynchronized rTMS, but not modified iTBS or the random alpha phase control condition, reduced restingstate alpha activity in left DLPFC and increased TMS-induced beta oscillations over frontocentral channels. Conclusions: Alpha-synchronized rTMS of left DLPFC is feasible, safe and has specific single-session neuromodulatory effects in patients with antidepressant-resistant MDD. Future studies need to further elucidate the mechanisms, optimize the parameters and investigate the therapeutic potential and efficacy of brain oscillation-synchronized rTMS in MDD.

Virtual reality (VR)-based motor therapy is an emerging approach in neurorehabilitation. The combination of VR with electroencephalography (EEG) presents further opportunities to improve therapeutic efficacy by personalizing the paradigm. Specifically, the idea is to synchronize the choice and timing of stimuli in the perceived virtual world with fluctuating brain states relevant to motor behavior. Here, we present an open source EEG single-trial based classification pipeline that is designed to identify ongoing brain states predictive of the planning and execution of movements. 9 healthy volunteers each performed 1080 trials of a repetitive reaching task with an implicit two-alternative forced choice, i.e., use of the right or left hand, in response to the appearance of a visual target. The performance of the EEG decoding pipeline was assessed with respect to classification accuracy of right vs. left arm use, based on the EEG signal at the time of the stimulus. Different features, feature extraction methods, and classifiers were compared at different time windows; the number and location of informative EEG channels and the number of calibration trials needed were also quantified, as well as any benefits from individual-level optimization of pipeline parameters. This resulted in a set of recommended parameters that achieved an average 83.3% correct prediction on never-before-seen testing data, and a state-of-the-art 77.1% in a real-time simulation. Neurophysiological plausibility of the resulting classifiers was assessed by time–frequency and event-related potential analyses, as well as by Independent Component Analysis topographies and cortical source localization. We expect that this pipeline will facilitate the identification of relevant brain states as prospective therapeutic targets in closed-loop EEG-VR motor neurorehabilitation.