Eileen Lew Yi Lee
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 onset, the decoding accuracy peaks at 65% with the use of instantaneous phase features. The decoding accuracy for the stroke patients was 40-50% with their paretic arms. Thus, we observe that using phase features has two advantages: (i) the possibility to decode directions earlier and (ii) higher decoding accuracy. The thesis also validates the findings from scalp EEG with the invasive intra-cranial local field potentials recorded from an epileptic patient. We report that movement intention can be detected as early as 2000ms before the onset of movement with accuracy above 90%. The use of electrodes implanted in the supplementary motor area (SMA) for detecting intention with near perfection single trial classification has strengthen the theory that movement preparation starts as early as 1500ms before execution. As for movement directions, the decoding capability is rather low. It should be noticed that there are no electrode implanted in the parietal region that, as we have found in the two EEG studies and has been previously reported, are key for decoding reaching directions. In summary, the work in this thesis suggests that it is possible to recognize above chance level both the intention and direction of a self-paced reaching movement. In particular, movement intention detection can be used for the purpose of early activation of neuro-motor prosthetic devices that could enhance the recovery of stroke patients. In addition, future work derived from this thesis can harness the full potential of early intention detection from human voluntary movement for BCI and neuroprosthetics.
EPFL2012
