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Principal Investigator: NATHAN E CRONE
Organization: JOHNS HOPKINS UNIVERSITY
Fiscal Year: 2024
Award: $2,019,539
Funding agency: National Institute of Neurological Disorders and Stroke
For many years brain-computer interfaces (BCI's) have been explored as a means of restoring communication
to patients with Locked-In Syndrome (LIS), a devastating and often irreversible neurological condition in which
cognition is intact but nearly all motor output from the brain is interrupted, effectively cutting off communication
with the outside world. To date non-invasive BCI's (e.g. EEG) have had inadequate signal fidelity and spatial
resolution, while invasive BCI's using microelectrode arrays in hand motor cortex have delivered cursor and
multi-joint robotic control in controlled settings, but have been difficult to learn and have required frequent
retraining of decoding models, due to instabilities in the microelectrode-tissue interface.
High-density electrocorticographic (ECoG) recordings have been recently used by our team (JHU and
University of Utrecht) and by others for real-time detection and classification of a variety of different upper limb
movements and speech components. ECoG has sufficient spatial-temporal resolution and signal quality to
decode the broadband high-gamma (~60-200 Hz) responses of native cortical representations for upper limb
movements and speech. Speech representations are spatially distributed over several square centimeters,
ideally suited for electrocorticography (ECoG), but impractical for MEA's.
In a recent landmark paper in NEJM (Vansteensel et al. 2016) Dr. Ramsey's team in Utrecht demonstrated
home use of a fully implantable wireless ECoG BCI by a patient with LIS, without supervision by researchers.
To expand on the capabilities of this 4-channel system, our team proposes a first-in-human clinical trial to
establish the safety and efficacy of an ECoG BCI with far more channels, implanted for 6 months. Based on
the long-term safety and signal quality of ECoG demonstrated in neuromodulation for epilepsy (Neuropace
RNS), we have an IDE for the proposed “CortiCom System”, which uniquely combines a 128-channel HD-
ECoG array (PMT Corp) with a transcutaneous pedestal connector and neural signal processor (Blackrock
Microsystems). In this early feasibility trial, our team will pursue the following Aims/Milestones:
1. Demonstrate efficient and stable control of essential BCI functions (initiate BCI, call caregiver, and BCI
menu navigation). CortiCom will use real-time decoding of attempted movements of different fingers, arm
joints, and mouth and face muscles to control the critical BCI functions, e.g. caregiver calling (by 3 months),
and menu navigation--Up/Down/Left/Right, Enter, and Back/Escape (6 commands).
2. Demonstrate efficient and stable operation of a keyword-based speech BCI. CortiCom will use low-
latency detection and classification of attempted speech (keywords) to expand communication. Keyword
decoding will use a hierarchical hybrid model to detect and classify keywords based on their unique spatial-
temporal signatures of population activity. Keyword command vocabulary, based on communication value and
ease of classification, will expand from 6 (by 3 months) to 20 or more keywords during the trial.
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