A brain-computer typing interface using finger movements

Article Multidisciplinary Sciences

Real-time brain-machine interface in non-human primates achieves high-velocity prosthetic finger movements using a shallow feedforward neural network decoder

Matthew S. Willsey et al.

Summary: Despite the rapid progress and interest in brain-machine interfaces that restore motor function, the study demonstrates that shallow-layer neural network decoders outperform and enable higher velocity finger movements than the current linear decoding standard.

NATURE COMMUNICATIONS (2022)

Add to Collection

Review Neurosciences

A systematic review of research on augmentative and alternative communication brain-computer interface systems for individuals with disabilities

Betts Peters et al.

Summary: Augmentative and alternative communication brain-computer interface (AAC-BCI) systems aim to provide communication access to individuals with severe speech and physical impairment (SSPI). This systematic review examines the characteristics of studies, systems, and participants in AAC-BCI research, summarizes the communication task performance of participants with disabilities, and explores performance differences between participants with and without disabilities. The review identifies variability in AAC-BCI system performance and the reporting of participant characteristics, highlighting the need for further research and standardized reporting measures.

FRONTIERS IN HUMAN NEUROSCIENCE (2022)

Add to Collection

Article Multidisciplinary Sciences

High-performance brain-to-text communication via handwriting

Francis R. Willett et al.

Summary: BCIs can restore communication for individuals with paralysis by decoding attempted handwriting movements, achieving typing speeds of 90 characters per minute with high accuracy. This approach opens new possibilities for BCIs and demonstrates the feasibility of decoding rapid, dexterous movements even years after paralysis.

NATURE (2021)

Add to Collection

Article Neurosciences

Real-time linear prediction of simultaneous and independent movements of two finger groups using an intracortical brain-machine interface

R. Samuel Nason et al.

Summary: The study introduces a linear brain-machine interface that achieves individual finger control in nonhuman primates by systematically distinguishing between two finger groups. Results suggest that training ridge regression decoders can effectively predict untrained finger group movements.

NEURON (2021)

Add to Collection

Article Medicine, General & Internal

Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria

David A. Moses et al.

Summary: This study successfully decoded words and sentences directly from cortical activity during attempted speech in a patient with anarthria and spastic quadriparesis using deep-learning models and a natural-language model, showing potential for improving communication in paralyzed individuals.

NEW ENGLAND JOURNAL OF MEDICINE (2021)

Add to Collection

Article Biochemistry & Molecular Biology