Decoding Self-paced Upper Limb Reaching Movements from Brain Signals

Future emerging technologies in upper limb neuroprosthetic devices will require decoding and executing the user's intended movement. Previous studies, using invasive and non-invasive brain signals, have shown promising results in decoding movement directions during movement execution. Intracortical recordings also allow for decoding of target directions before the actual movement in a reaction task. This thesis contributes to the exciting endeavor of designing practical upper limb neuroprosthetics by investigating the potential of using brain signals recorded non-invasively for detecting the intention to move and decoding directions of a self-paced reaching task before movement initiation. This work has the potential towards a more intuitive and coupled neuro-motor rehabilitation tools. The thesis provides three major contributions: (i) it proposes the use of data-driven and machine learning methods for localizing the regions of interests from scalp surface signals, (ii) it reports on successful detection of movement intention before a self-paced reaching task using amplitudes of on-going slow cortical potentials (SCPs) which is consistent across healthy subjects and chronic stroke patients and (ii) it proposes the use of amplitudes and phase of on-going SCPs for decoding movement directions before the reaching task. First, we reported a method for single trial detection of movement intention before a self-paced reaching task using signal processing and machine learning techniques. We used the movement-related potentials (MRPs) in a narrow frequency range between 0.1 to 1Hz for early detection of movement intention in the first study with 8 healthy subjects using scalp EEG. Movement intention can be detected on average 460ms before the actual onset. The average maximum true positive rates across subjects was 76% peaking at time 167ms before onset. These findings are coherent with the next study on stroke patients and control subjects. In the second study, movement intention can be detected as early as 600ms before the actual onset. More interestingly, the true positive rate reached above 80% for one of the stroke patients (both the paretic and healthy arm) and healthy control subjects. The low false positive rate before the detection of movement intention suggested that the method is promising for future online implementation using appropriate tasks (i.e. goal-oriented reaching). Second, we proposed a method for decoding reaching movement directions before onset using feature selection method called canonical variants analysis (CVA). Across all the subjects, the best selected features are mostly from the frontoparietal regions, which is consistent with previous neurophysiological studies on arm reaching tasks. The results of single trial decoding of movement directions are also promising, with a decoding accuracy before the onset for one of the control healthy subjects was of 71% using the amplitude of on-going SCPs. In addition, at 250ms before