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Updated: Jul 23, 2025

An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones

An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones

Published on: October 5, 2020

A brain-computer typing interface using finger movements.

Nishal P Shah¹, Matthew S Willsey¹, Nick Hahn¹

¹Stanford University.

International IEEE/EMBS Conference on Neural Engineering : [Proceedings]. International IEEE EMBS Conference on Neural Engineering

|July 19, 2023

Summary

This summary is machine-generated.

This study introduces a novel intracortical brain-computer interface (iBCI) keyboard for individuals with paralysis. The 3D iBCI keyboard achieved 90% accuracy for typing, significantly improving communication prosthesis capabilities.

Keywords:

Communication Fingers Intracortical Brain Computer Interface Motor Cortex Motor Decoding Typing

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Area of Science:

Neuroscience
Biomedical Engineering
Rehabilitation Technology

Background:

Intracortical brain-computer interfaces (iBCIs) are crucial for developing assistive technologies for individuals with paralysis.
Existing iBCIs enable functions like cursor control, handwriting, and speech, but advanced communication methods are needed.
Decoding neural activity from the motor cortex allows for the control of external devices.

Purpose of the Study:

To introduce and evaluate a novel 3D intracortical brain-computer interface (iBCI) keyboard for enhanced typing capabilities.
To compare the decoding accuracy of individual finger movements versus grouped finger movements for iBCI control.
To optimize the virtual keyboard layout using natural language statistics for efficient symbol acquisition.

Main Methods:

Developed a participant-specific iBCI keyboard prototype for a BrainGate2 clinical trial participant (T5).
Recorded neural activity from the left premotor cortex to decode finger movements (5 individual DOF vs. 3 grouped DOF).
Designed a virtual keyboard with optimized symbol placement based on natural language statistics and tested with a real-time closed-loop decoder.

Main Results:

Neural decoding using 3 independent degrees of freedom (DOF) for finger groups achieved higher accuracy (95%) compared to 5 individual DOF (76%).
The optimized virtual keyboard layout reduced average finger travel distance by approximately 2x compared to a random layout.
The participant successfully typed 31 symbols with 90% accuracy at an average speed of 2.3 seconds per symbol.

Conclusions:

A 3D iBCI keyboard utilizing grouped finger movements offers a more accurate and efficient communication prosthesis for individuals with paralysis.
Optimizing keyboard layout with natural language statistics significantly enhances typing performance in iBCI systems.
This novel iBCI approach demonstrates a promising advancement in restoring communication for severely disabled individuals.