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Toward an Open-Ended BCI: A User-Centered Coadaptive Design.

Kiret Dhindsa¹, Dean Carcone², Suzanna Becker³

¹Neurotechnology and Neuroplasticity Lab, Department of Psychology, Neuroscience, and Behaviour, McMaster University, Hamilton, Ontario L8S 4L8, Canada dhindsj@mcmaster.ca.

Neural Computation

|August 5, 2017

Summary

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This study introduces an open-ended brain-computer interface (BCI) that allows users to discover their own mental strategies, improving BCI performance by accommodating individual differences in neurophysiological signal modulation.

Area of Science:

Neuroscience
Human-Computer Interaction
Biomedical Engineering

Background:

Brain-computer interfaces (BCIs) traditionally require users to modulate specific brain signals, leading to variable performance.
Individual differences in neurophysiological signal modulation ability impact BCI effectiveness.
Current BCI designs lack adaptability to diverse user capabilities.

Purpose of the Study:

To develop and evaluate a novel open-ended BCI design.
To enable users to discover and utilize their own mental strategies for BCI control.
To investigate the impact of individual differences and expertise on BCI performance.

Main Methods:

Implemented an open-ended BCI allowing users to freely explore mental strategies.
Conducted coadaptive training sessions between users and the BCI.

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Analyzed performance variations based on individual strategies and background expertise.

Main Results:

The open-ended BCI demonstrated comparable performance to traditional BCIs.
It accommodated a wider range of user mental strategies without prior signal knowledge.
Individual expertise, such as musical training, significantly influenced BCI performance.

Conclusions:

Open-ended BCIs offer a more individualized and user-focused approach to BCI control.
Leveraging individual expertise and diverse mental strategies enhances BCI usability.
Future research should focus on generalized training and decoding methods for open-ended BCIs.