A brain–computer interface using electrocorticographic signals in humans

TLDR

BCIs allow device control via scalp EEG or single‑neuron recordings, but EEG suffers from limited resolution and training demands, while single‑neuron approaches carry clinical risks and limited stability. The study demonstrates that surface ECoG signals can enable rapid, accurate one‑dimensional cursor control. The authors identified motor and speech‑imagery ECoG signals, trained four patients in 3–24 min to achieve 74–100 % success in closed‑loop one‑dimensional control, and showed that frequencies up to 180 Hz encoded two‑dimensional joystick direction in open‑loop tests. The ECoG‑based BCI achieved 74–100 % success in one‑dimensional tasks and encoded two‑dimensional joystick direction, indicating it could offer a more powerful, stable, and less traumatic communication option for individuals with severe motor disabilities than EEG or invasive BCIs.

Abstract

Brain–computer interfaces (BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the brain can enable users to control a one-dimensional computer cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3–24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74–100% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the brain.