Decoding two-dimensional movement trajectories using electrocorticographic signals in humans

TLDR

Brain signals are considered a basis for brain–computer interfaces to aid severely paralyzed individuals, yet it has been assumed that accurate decoding of kinematic parameters requires intracortical microelectrodes, whose long‑term stability is uncertain. The study demonstrates that kinematic parameters can be decoded from subdural ECoG signals with accuracy comparable to intracortical microelectrodes, with the local motor potential providing the most informative feature and exhibiting cosine tuning, indicating that ECoG offers a stable, less invasive alternative for BCI and motor studies.

Abstract

Signals from the brain could provide a non-muscular communication and control system, a brain–computer interface (BCI), for people who are severely paralyzed. A common BCI research strategy begins by decoding kinematic parameters from brain signals recorded during actual arm movement. It has been assumed that these parameters can be derived accurately only from signals recorded by intracortical microelectrodes, but the long-term stability of such electrodes is uncertain. The present study disproves this widespread assumption by showing in humans that kinematic parameters can also be decoded from signals recorded by subdural electrodes on the cortical surface (ECoG) with an accuracy comparable to that achieved in monkey studies using intracortical microelectrodes. A new ECoG feature labeled the local motor potential (LMP) provided the most information about movement. Furthermore, features displayed cosine tuning that has previously been described only for signals recorded within the brain. These results suggest that ECoG could be a more stable and less invasive alternative to intracortical electrodes for BCI systems, and could also prove useful in studies of motor function.