Reconstruction of hand, elbow and shoulder actual and imagined trajectories in 3D space using EEG current source dipoles

Abstract

Objective. Growing evidence suggests that electroencephalography (EEG) electrode (sensor) potential time series (PTS) of slow cortical potentials (SCPs) hold motor neural correlates that can be used for motion trajectory prediction, commonly by multiple linear regression (mLR). It is not yet known whether arm-joint trajectories can be reliably decoded from current sources, computed from sensor data, from which brain areas they can be decoded and using which neural features. Approach. In this study, the PTS of 44 sensors were fed into sLORETA source localization software to compute current source activity in 30 regions of interest (ROIs) found in a recent meta-analysis to be engaged in action execution, motor imagery and motor preparation. The current sources PTS and band-power time series (BTS) in several frequency bands and time lags were used to predict actual and imagined trajectories in 3D space of the three velocity components of the hand, elbow and shoulder of nine subjects using an mLR model. Main results. For all arm joints and movement types, current source SCPs PTS contributed most to trajectory reconstruction with time lags 150, 116 and 84 ms providing the highest contribution, and current source BTS in any of the tested frequency bands was not informative. Person’s correlation coefficient (r) averaged across movement types, arm joints and velocity components using source data was slightly lower than using sensor data (r = 0.25 and r = 0.28, respectively). For each ROI, the three current source dipoles had different contribution to the reconstruction of each of the three velocity components. Significance. Overall, our results demonstrate the feasibility of predicting of actual and imagined 3D trajectories of all arm joints from current sources, computed from scalp EEG. These findings may be used by developers of a future BCI as a validated set of contributing ROIs.