Human voluntary movement is associated with at least two distinct types of scalp electroencephalographic (EEG) changes. Event-related potentials are slow, with DC signals developing in the bifrontal region as early as 1.5 seconds prior to movement. They are detected by averaging multiple events in the time domain and generally require at least 40-50 events to allow detection of the signal within the noise. Frequency changes however, are more robust and may be seen reliably on individual traces. The frequency changes occur in the alpha (8-13 Hz) range as well as beta (13-30 Hz) and may occur up to 2 seconds before movement. This leads to the notion that real-time analysis of the EEG may allow one to predict individual movement. If this could be done reliably, it may provide further insight about how the brain prepares for movement, as well as potential therapeutic options such as control of cortically based prosthetic device.
Our initial study, henceforth Phase 1, is an exploratory study using real-time EEG to identify the factors that allow one to reliably predict normal human voluntary movement. Subjects will be normal volunteers, studied in the EEG lab in the Human Motor Control Section. Subjects will be asked to perform a simple motor task involving a sequence of finger movements while undergoing a routine EEG recording with surface electromyography. The EEG will then be processed using standard techniques to identify the location and time course of EEG signals in response to movement. Once this has occurred, subjects will return for a real-time study that will use their individually identified factors to predict their movement. The effects of training on the accuracy of prediction will also be explored by scheduling multiple real-time prediction sessions per subject over the course of several weeks. The rate of successful movement prediction will be the primary outcome measure.
After we are able to accurately predict movement intention with healthy volunteers, i.e., the false positive rate is under 20% with the false negative rate under 50%, we will study whether we can achieve the same prediction accuracy with stroke patients and patients with primary lateral sclerosis (PLS) or amyotrophic lateral sclerosis (ALS). The stroke patients and ALS/PLS patients will perform the same procedure as the subjects in the Phase 1 part of the trial.
Phase 2 of the investigation will extend to a different type of movement, reaching, and to an additional parameter, the spatial field of the intended target of the movement. In addition, Phase 2 will also include magnetoencephalography (MEG) as well as EEG methods to classify the spatiotemporal features of these movement parameters. Successful prediction of the intended goals of reaches to either ipsilateral or contralateral fields, prior to the onset of movement will be the main outcome measure of phase 2 of the study.
In Phase 3 of the investigation, healthy volunteers will perform a simple finger movement task which will be analyzed with special attention given to the timing of the intention to move and to how the intention affects the EEG signal. In order to assess whether spontaneous movements without prior instruction are associated with different physiological markers from typical self-paced paradigms, a recording session will be performed after the EEG cap is placed without instructing the subject.
Results from this study will then be used to design further protocols studying human voluntary movement and clinical applications as appropriate.
