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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

Faster self-organizing fuzzy neural network training and a hyperparameter analysis for a brain-computer interface.

Damien Coyle¹, Girijesh Prasad, Thomas Martin McGinnity

¹Intelligent Systems Research Center, School ofComputing and Intelligent Systems, Faculty of Computing and Engineering,University of Ulster, Londonderry, U.K. dh.coyle@ulster.ac.uk

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|June 5, 2009

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This study enhances the self-organizing fuzzy neural network (SOFNN) for efficient brain-computer interfaces (BCI). Optimized parameters enable a fully parameterless BCI system for autonomous adaptation.

Area of Science:

Computational Neuroscience
Machine Learning
Signal Processing

Background:

Self-organizing fuzzy neural networks (SOFNN) are utilized in brain-computer interfaces (BCI).
Existing SOFNN models require subject-specific hyperparameter tuning, limiting autonomous application.
Computational efficiency and accuracy are key challenges in evolving fuzzy systems for BCI.

Purpose of the Study:

To improve the computational efficiency of SOFNN learning algorithms.
To evaluate the modified SOFNN's performance against other evolving fuzzy systems.
To develop a parameterless SOFNN framework for electroencephalogram (EEG)-based BCI preprocessing.

Main Methods:

Modifications were made to the SOFNN learning algorithm to enhance computational efficiency.

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A sensitivity analysis (SA) of SOFNN hyperparameters was conducted using EEG data from motor imagery BCI experiments.

The SOFNN was integrated into the neural-time-series-prediction-preprocessing (NTSPP) framework.

Main Results:

The modified SOFNN demonstrated superior accuracy and reduced structural complexity compared to other evolving fuzzy systems.
A general set of hyperparameters, identified through SA, optimized SOFNN performance in the NTSPP framework.
The study successfully established a parameterless SOFNN for EEG preprocessing.

Conclusions:

The modified SOFNN offers improved computational efficiency and performance for BCI applications.
A parameterless SOFNN, combined with parameterless feature extraction and classification, enables a fully autonomous BCI.
This approach facilitates autonomous adaptation in BCI systems, reducing the need for manual parameter selection.