Seminal studies in motor neuroscience involving healthy subjects have revealed time-locked changes in induced power within specific frequency bands. Brain recordings were shown to exhibit a gradual reduction in signal power, relative to baseline, in the mu and beta frequency bands during an action or during motor imagery: the event-related desynchronization (ERD). This is considered to reflect processes related to movement preparation and execution and is particularly pronounced in the contralateral sensorimotor cortex. Shortly following the completion of the task, a relative increase in power, the event-related synchronization (ERS), could be observed in the beta band. ERS is thought to reflect the re-establishment of inhibition in the same area. Ever since the characterization of the ERD and ERS phenomena, there has been little to no discussion in the field of non-invasive Brain Computer Interfaces (BCI) as to whether these features accurately capture the task-related modulations of brain activity. Recent studies in neurophysiology have demonstrated that the ERD and ERS patterns only emerge as a result of averaging signal power over multiple trials. On a single trial level, beta band activity occurs in short, transient events, bursts, rather than as sustained oscillations. This indicates that the ERD and ERS patterns reflect accumulated, time-varying changes in the burst probability during each trial. Thus, beta bursts may carry more behaviourally relevant information than averaged beta band power. Studies in humans involving arm movements have established a link between the timing of sensorimotor beta bursts and response times before movement, as well as behavioural errors post-movement. Beta burst activity in frontal areas has also been shown to correlate with movement cancellation and recent studies show that activity at the motor unit level also occurs in a transient manner, which is time-locked to sensorimotor beta bursts. Although beta burst rate has been shown to carry significant information, it still comprises a rather simplistic representation of the underlying activity. Indeed, complex burst waveforms are embedded in the raw signals, and can be characterized by a stereotypical average shape with large variability around it. The waveform features are neglected in standard BCI approaches, because conventional signal processing methods generally presuppose sustained, oscillatory and stationary signals, and are thus inherently unsuitable for analysing transient activity. In contrast to beta, activity in the mu frequency band is oscillatory even in single trials. This activity is typically analysed using time-frequency decomposition techniques, which assume that the underlying signal is sinusoidal. However, there is now growing consensus that oscillatory neural activity is often non-sinusoidal and that the raw waveform shape can be informative of movement. In this project, the design of a subject-specific neurophysiological model to guide motor BCI training will be optimized using Magnetic Resonance Imaging (MRI) and Magnetoencephalography (MEG) for high spatial and biophysical specificity in the experimental group. Anatomical MR volumes will be used to design and 3D-print an individual head cast that will be used in the MEG scanner to stabilize the head position and minimize movements. This high-precision approach (hpMEG) has been proven to significantly improve source localization up to the level of distinguishing laminar activity, which makes it superior to EEG recording technique. An individualized hpMEG approach, as well as the widely adopted EEG, will be used to study bursts of oscillatory activity in the beta and mu frequency bands related to motor imagery and motor execution. hpMEG will yield subject-specific models of motor imagery that will be used to constrain online decoding of EEG data. This approach will be applied and validated on a group of healthy adult subjects and will then be compared against another feasibility group of patients and age-matched healthy participants. The proposed approach will be compared with a classic EEG-based BCI approach. The information will be used to optimally guide subsequent EEG-based BCI training in the control group. After a thorough investigation in healthy subjects in this project, the feasibility of the approach will be evaluated in a few stroke patients with upper-limb motor deficits. Tasks 1.1 and 1.2 aim to develop subject-specific generative models decoding movement onset and offset, the type of movement, as well as finely discretized movement amplitude during both real and imagined wrist extensions/flexions. Task 1.2 investigates how lesions of patients alter our ability to decode attempted wrist movements.