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Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Self-recalibrating classifiers for intracortical brain-computer interfaces.

William Bishop¹, Cynthia C Chestek, Vikash Gilja

¹Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213, USA. Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Journal of Neural Engineering

|February 8, 2014

Summary

This summary is machine-generated.

This study introduces self-recalibrating classifiers for brain-computer interfaces (BCIs), reducing the need for daily retraining. Novel classifiers nearly match daily retraining performance, accelerating clinical translation.

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An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

Area of Science:

Neuroscience
Biomedical Engineering
Machine Learning

Background:

Intracortical brain-computer interfaces (BCIs) require daily decoder retraining for stable performance.
Current self-recalibrating decoders are limited to continuous control, not discrete classification.
Developing autonomous retraining for discrete decoding is crucial for clinical BCI applications.

Purpose of the Study:

To develop and evaluate self-recalibrating classifiers for discrete decoding in intracortical BCIs.
To investigate the stability of decoder parameters over time in motor cortex recordings.
To improve the efficiency and reduce the clinical burden of BCI systems.

Main Methods:

Recorded neural data (96-electrode arrays) from two rhesus macaques performing a center-out reach task.
Analyzed threshold crossings from motor cortex over multiple days (41 and 36 sessions).
Developed and tested two novel self-recalibrating classifiers against a standard, non-retrained classifier.

Main Results:

Decoder tuning parameters remain stable within days and show correlated drift across days.
A non-retrained classifier shows a performance decrease of over 10% compared to daily retraining.
Novel self-recalibrating classifiers achieved a ~15% increase in accuracy, nearing daily retraining performance.

Conclusions:

Self-recalibrating classifiers eliminate the need for daily retraining in discrete BCI decoding.
These advancements are expected to accelerate the clinical adoption of BCI technology.
Future research should focus on closed-loop BCI system validation with these novel classifiers.