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Bayesian inference for an adaptive Ordered Probit model: an application to Brain Computer Interfacing.

Ji Won Yoon¹, Stephen J Roberts, Mathew Dyson

¹Information Engineering, Department of Engineering Science, University of Oxford, Parks Road, Oxford, UK. yoonj@tcd.ie

Neural Networks : the Official Journal of the International Neural Network Society

|April 16, 2011

Summary

This summary is machine-generated.

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This study introduces a novel algorithm for adaptive, sequential classification in Brain Computer Interfacing (BCI) systems, effectively handling unknown labeling errors and noise. The method demonstrates robustness for accurate label prediction in complex, real-world biomedical applications.

Area of Science:

Biomedical Engineering
Machine Learning
Signal Processing

Background:

Brain Computer Interfacing (BCI) systems require robust classification algorithms.
Labeling errors and sensor noise are significant challenges in sequential data analysis.
Accurate inference and prediction of target labels are crucial for BCI performance.

Purpose of the Study:

To propose a novel algorithm for adaptive, sequential classification in systems with unknown labeling errors.
To enhance the robustness of classification in the presence of label and sensor noise.
To apply and evaluate the algorithm in Brain Computer Interfacing (BCI) systems.

Main Methods:

Developed a dynamic classification algorithm combining an Ordered Probit model and an Extended Kalman Filter (EKF).

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Modeled observed labels as noisy observations of latent labels with multiple classes (≥ 2).

Utilized the EKF for sequential estimation of Ordered Probit model parameters over time.

Main Results:

The proposed algorithm demonstrated robustness against label and sensor noise.
Successfully performed inference and prediction of target labels under nonlinear and non-Gaussian models.
Validated performance using synthetic datasets and real experimental EEG signals for BCI.

Conclusions:

The developed algorithm offers a robust solution for adaptive, sequential classification in BCI.
The method is effective in handling missing or erroneous labeling in dynamic systems.
This approach has broad applicability to domains requiring sequential missing label imputation under uncertainty.