Passive robot assistance in arm movement decoding from EEG signals

The past decade has seen the rapid development of upper limb kinematic decoding techniques by performing intracortical recordings of brain signals. However, the use of non-invasive approaches to perform similar decoding procedures is still in its early stages. Previous works show that there is a correlation between electroencephalographic signals and position/velocity hand movement parameters. In these studies, it was observed that uniform and linear movements were better decoded. In this paper, a passive robot assistance has been used during non-invasive hand kinematics decoding. To study the influence of movement variability in this decoding, we propose to use this passive robot arm to track a circle that moves on the screen. Brain signals are recorded while performing the movements. The decoding method is based on a linear regression model applied to low frequency signal components. The decoding accuracy has been analyzed by studying the correlation between the reconstructed and original movements. These results have been compared to previous works in which a computer mouse was used to perform the movements. The results show more stable and realistic decoding accuracies when using the planar robot. Also, we show that decoding accuracy is directly correlated to tracking success.