Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller
Pieter-Jan Kindermans,Michael Tangermann,Klaus-Robert Müller,Klaus-Robert Müller,Benjamin Schrauwen +4 more
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This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs) and shows how the framework harvests the structure suitable to solve the decoding task by transfer learning, unsupervised adaptation, language model and dynamic stopping.Abstract:
Objective Most BCIs have to undergo a calibration session in which data is recorded to train decoders with machine learning Only recently zero-training methods have become a subject of study This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs) For the example of a visual P300 speller we show how the framework harvests the structure suitable to solve the decoding task by (a) transfer learning, (b) unsupervised adaptation, (c) language model and (d) dynamic stopping Approach A simulation study compares the proposed probabilistic zero framework (using transfer learning and task structure) to a state-of-the-art supervised model on n = 22 subjects The individual influence of the involved components (a)–(d) are investigated Main results Without any need for a calibration session, the probabilistic zero-training framework with inter-subject transfer learning shows excellent performance—competitive to a state-of-the-art supervised method using calibration Its decoding quality is carried mainly by the effect of transfer learning in combination with continuous unsupervised adaptation Significance A high-performing zero-training BCI is within reach for one of the most popular BCI paradigms: ERP spelling Recording calibration data for a supervised BCI would require valuable time which is lost for spelling The time spent on calibration would allow a novel user to spell 29 symbols with our unsupervised approach It could be of use for various clinical and non-clinical ERP-applications of BCIread more
Citations
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A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10-year Update
Fabien Lotte,Laurent Bougrain,Andrzej Cichocki,Andrzej Cichocki,Maureen Clerc,Marco Congedo,Alain Rakotomamonjy,Florian Yger +7 more
TL;DR: A comprehensive overview of the modern classification algorithms used in EEG-based BCIs is provided, the principles of these methods and guidelines on when and how to use them are presented, and a number of challenges to further advance EEG classification in BCI are identified.
Journal ArticleDOI
An auditory multiclass brain-computer interface with natural stimuli: Usability evaluation with healthy participants and a motor impaired end user.
TL;DR: The study demonstrated the feasibility of the auditoryBCI with healthy users and stresses the importance of training with auditory multiclass BCIs, especially for potential end-users of BCI with disease.
Journal ArticleDOI
Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces
TL;DR: This paper proposes to generate artificial EEG trials from the few EEG trials initially available, in order to augment the training set size, and surveys existing approaches to reduce or suppress calibration time and proposes three different methods to do so.
Journal ArticleDOI
Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach
He He,Dongrui Wu +1 more
TL;DR: Zhang et al. as discussed by the authors proposed an approach to align EEG data from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject.
Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain-Computer Interfaces This paper addresses machine learning tools to reduce or overall suppress calibration times for BCIs.
TL;DR: In this paper, the authors present a survey of existing approaches to reduce or suppress calibration time for brain-computer interfaces (BCI) and propose new tools to reduce BCI calibration time.
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Maximum likelihood from incomplete data via the EM algorithm
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