Spatio-temporal variations in hand movement trajectory based brain activation patterns

The neuro engineering research over the past decades has established Electroencephalography based Brain Computer Interface (EEG-BCI) systems as an efficient means of decoding brain activity. Motor control BCI is a category of BCI that analyzes neural activity recorded over sensory motor area to classify or decode intended motor tasks. For a BCI system, it is desired to have defined and independent output control commands. Decoding movement trajectory parameters such as instantaneous position, speed coordinates from non-invasive brain recordings can thus be a key contribution in motor control BCI applications. In this study, we use Multiple Linear Regressor to estimate the hand movement trajectory from spectrally localized multi-sensor EEG. The algorithm is validated using data collected from subjects as they perform 2-dimensional center-out hand movement towards pre-defined targets at varying speeds. The spatio-temporal variations in motor activity based neural activation patterns using metrics derived from MLR estimator is investigated. The contribution of the predictors to the regression equation and decoding performance at various stages of movement are also studied. An average correlation of 0.63 (p<;0.005) between recorded and estimated trajectory is obtained using the method. The temporally varying involvement of motor, pre-motor and parietal areas; movement task dependent activations and time-varying sensor contribution in reconstruction are further demonstrated.