Finger movement and coactivation predicted from intracranial brain activity using extended block-term tensor regression.

Objective. We introduce extended Block-Term Tensor Regression (eBTTR), a novel regression method designed to account for the multilinear nature of human intracranial finger movement recordings. Approach. The proposed method relies on recursive Tucker decomposition combined with automatic component extraction. Main results. eBTTR outperforms state-of-the-art regression approaches, including multilinear and deep learning ones, in accurately predicting finger trajectories as well as unintentional finger coactivations. Significance. eBTTR rivals state-of-the-art approaches while being less computationally expensive which is an advantage when intracranial electrodes are implanted acutely, as part of the patient's presurgical workup, limiting time for decoder development and testing.