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A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

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Increasing Robustness of Brain-Computer Interfaces Through Automatic Detection and Removal of Corrupted Input

Jordan L Vasko¹, Laura Aume¹, Sanjay Tamrakar¹

¹Battelle Memorial Institute, Columbus, OH, United States.

Frontiers in Neuroscience

|May 16, 2022

Summary

This study presents an automated system for brain-computer interfaces (BCIs) to detect and adapt to signal disruptions, enhancing usability without user intervention. The method ensures robust BCI performance by seamlessly adjusting neural decoders to faulty channels.

Keywords:

brain–machine (computer interface)deep learning – artificial neural network intracortical array neuroprosthetic statistical process control

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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:

Brain-computer interfaces (BCIs) require robust signal processing for long-term daily use.
Signal disruptions from chronic use, like channel failures, impede BCI performance and usability.
Existing methods often require user intervention or significant computational resources.

Purpose of the Study:

To develop an automated, computationally tractable framework for BCIs to adapt to signal disruptions.
To enhance BCI robustness and usability during chronic use without user intervention.
To maintain high decoding performance despite channel failures.

Main Methods:

Adapted statistical process control (SPC) for automated detection of disrupted channels.
Automated channel removal from neural network decoders using a masking approach.
Transfer and unsupervised learning to update decoder weights without new calibration data.

Main Results:

The proposed method automatically identifies and adapts to disrupted recording channels.
Channel removal and weight updates are performed rapidly without altering the decoder architecture.
High decoding performance is maintained with minimized computation and storage requirements.
The framework is invisible to the user, increasing BCI robustness and usability.

Conclusions:

This automated framework significantly enhances BCI robustness and usability for long-term applications.
The approach is computationally efficient and suitable for low-power BCI hardware.
Seamless adaptation to signal disruptions is crucial for practical BCI deployment.