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Adaptive Semi-Supervised Training of P300 ERP-BCI Speller System with Minimum Calibration Effort

Shumeng Chen, Jane E. Huggins, Tianwen Ma

TL;DR

The proposed semi-supervised learning framework provides a practical and efficient alternative to improve the overall spelling efficiency in the real-time BCI speller system, particularly in contexts with limited labeled data.

Abstract

A P300 ERP-based Brain-Computer Interface (BCI) speller is an assistive communication tool. It searches for the P300 event-related potential (ERP) elicited by target stimuli, distinguishing it from the neural responses to non-target stimuli embedded in electroencephalogram (EEG) signals. Conventional methods require a lengthy calibration procedure to construct the binary classifier, which reduced overall efficiency. Thus, we proposed a unified framework with minimum calibration effort such that, given a small amount of labeled calibration data, we employed an adaptive semi-supervised EM-GMM algorithm to update the binary classifier. We evaluated our method based on character-level prediction accuracy, information transfer rate (ITR), and BCI utility. We applied calibration on training data and reported results on testing data. Our results indicate that, out of 15 participants, 9 participants exceed the minimum character-level accuracy of 0.7 using either on our adaptive method or the benchmark, and 7 out of these 9 participants showed that our adaptive method performed better than the benchmark. The proposed semi-supervised learning framework provides a practical and efficient alternative to improve the overall spelling efficiency in the real-time BCI speller system, particularly in contexts with limited labeled data.

Adaptive Semi-Supervised Training of P300 ERP-BCI Speller System with Minimum Calibration Effort

TL;DR

The proposed semi-supervised learning framework provides a practical and efficient alternative to improve the overall spelling efficiency in the real-time BCI speller system, particularly in contexts with limited labeled data.

Abstract

A P300 ERP-based Brain-Computer Interface (BCI) speller is an assistive communication tool. It searches for the P300 event-related potential (ERP) elicited by target stimuli, distinguishing it from the neural responses to non-target stimuli embedded in electroencephalogram (EEG) signals. Conventional methods require a lengthy calibration procedure to construct the binary classifier, which reduced overall efficiency. Thus, we proposed a unified framework with minimum calibration effort such that, given a small amount of labeled calibration data, we employed an adaptive semi-supervised EM-GMM algorithm to update the binary classifier. We evaluated our method based on character-level prediction accuracy, information transfer rate (ITR), and BCI utility. We applied calibration on training data and reported results on testing data. Our results indicate that, out of 15 participants, 9 participants exceed the minimum character-level accuracy of 0.7 using either on our adaptive method or the benchmark, and 7 out of these 9 participants showed that our adaptive method performed better than the benchmark. The proposed semi-supervised learning framework provides a practical and efficient alternative to improve the overall spelling efficiency in the real-time BCI speller system, particularly in contexts with limited labeled data.
Paper Structure (22 sections, 10 equations, 8 figures, 1 algorithm)

This paper contains 22 sections, 10 equations, 8 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Existing Methods and Challenges
  4. Our Contributions
  5. Methods
  6. Overall Framework
  7. Model Assumptions
  8. Notations
  9. Offline Learning
  10. Expectation Step (E-Step)
  11. Maximization Step (M-Step)
  12. Online Learning
  13. Update within Sequence.
  14. Update Across Sequences.
  15. Simulation Analysis
  16. ...and 7 more sections

Figures (8)

  • Figure 1: This figure illustrates the stimulus presentation in the Row-Column Paradigm (RCP) of a P300 BCI speller, where rows and columns of a 6×6 virtual keyboard are sequentially and randomly flashed to evoke P300 responses. The fourth row is currently being highlighted.
  • Figure 2: Conventional framework of P300 ERP-based BCI speller system
  • Figure 3: The Adaptive Procedure of Semi-supervised EM-GMM
  • Figure 4: True Mean Vectors and Covariance Matrix of Signals
  • Figure 5: Character Level Accuracy on the Testing Set
  • ...and 3 more figures