Sub-Scalp EEG for Sensorimotor Brain-Computer Interface

TL;DR

This study demonstrates that sub-scalp EEG can effectively record and classify sensorimotor neural activity in sheep, showing potential as a minimally invasive option for chronic brain-computer interface applications.

Contribution

The paper provides evidence that sub-scalp EEG offers high spatial resolution and comparable signal quality to invasive methods like ECoG, supporting its use in chronic BCI systems.

Findings

Successfully recorded sensorimotor rhythms in sheep

Achieved above-chance motor classification performance

Signal quality approaches that of invasive neural recording methods

Abstract

Objective: To establish sub-scalp electroencephalography (EEG) as a viable option for brain-computer interface (BCI) applications, particularly for chronic use, by demonstrating its effectiveness in recording and classifying sensorimotor neural activity. Approach: Two experiments were conducted in this study. The first aim was to demonstrate the high spatial resolution of sub-scalp EEG through analysis of somatosensory evoked potentials in sheep models. The second focused on the practical application of sub-scalp EEG, classifying motor execution using data collected during a sheep behavioural experiment. Main Results: We successfully demonstrated the recording of sensorimotor rhythms using sub-scalp EEG in sheep models. Important spatial, temporal, and spectral features of these signals were identified, and we were able to classify motor execution with above-chance performance. These results are comparable to previous work that investigated signal quality and motor execution classification using ECoG and endovascular arrays in sheep models. Significance: These results suggest that sub-scalp EEG may provide signal quality that approaches that of more invasive neural recording methods such as ECoG and endovascular arrays, and support the use of sub-scalp EEG for chronic BCI applications.