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Unsupervised learning for brain–computer interfaces based on event-related potentials

Unsupervised learning for brain–computer interfaces based on event-related potentials

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Machine learning has become a core component in brain-computer interfaces (BCIs). Unfortunately, the use of machine learning typically requires the collection of subject specific labelled data. This process is time-consuming and not productive from a user's point of view, as during calibration the user has to follow given instructions and cannot make own decisions. Only after calibration, the user is able to use the BCI freely. In this chapter, we describe how the supervised calibration process can be circumvented by unsupervised learning in which the decoder is trained while the user is utilising the system. We discuss three variations. First, expectation-maximisation (EM)-based training, which works wells empirically but can sometimes be unstable. Learning from label proportions (LLP)-based training, which is guaranteed to converge to the optimal solution, but learns more slowly. Third, a hybrid approach combining the stability of LLP with the speed of learning of EM in a highly efficient and effective approach that can readily replace supervised decoders for event-related potential BCI.

Chapter Contents:

  • Abstract
  • 6.1 Introduction
  • 6.2 Event-related potential based brain–computer interfaces
  • 6.3 Decoding based on expectation maximisation
  • 6.3.1 The probabilistic model for ERP BCI
  • 6.3.2 Training the model
  • 6.4 Decoding based on learning from label proportions
  • 6.4.1 Learning from label proportions
  • 6.4.2 A modified ERP paradigm
  • 6.4.3 Training of the LLP model
  • 6.5 Combining EM and LLP decoders analytically
  • 6.5.1 Training the MIX model
  • 6.6 Experimental setup
  • 6.6.1 Data
  • 6.6.2 Data processing
  • 6.6.3 Methods and hyperparameters
  • 6.7 Results
  • 6.8 Conclusion
  • Acknowledgements
  • References

Inspec keywords: brain-computer interfaces; learning (artificial intelligence); calibration; unsupervised learning

Other keywords: event-related potential BCI; expectation-maximisation-based training; subject specific labelled data; time-consuming; brain-computer interfaces; core component; given instructions; machine learning; unsupervised learning; label proportions; supervised calibration process; event-related potentials

Subjects: Biology and medical computing; Knowledge engineering techniques; Data handling techniques; Other topics in statistics

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