A Bayesian machine learning framework for true zero-training brain-computer interfaces

摘要

Brain-Computer Interfaces (BCI) are developed to allow the user to take control of a computer (e.g. a spelling application) or a device (e.g. a robotic arm) by using just his brain signals. The concept of BCI was introduced in 1973 by Jacques Vidal. The early types of BCI relied on tedious user training to enable them to modulate their brain signals such that they can take control over the computer. Since then, training has shifted from the user to the computer. Hence, modern BCI systems rely on a calibration session, during which the user is instructed to perform specific tasks. The result of this calibration recording is a labelled data-set that can be used to train the (supervised) machine learning algorithm. Such a calibration recording is, however, of no direct use for the end user. Hence, it is especially important for patients to limit this tedious process. For this reason, the BCI community has invested a lot of effort in reducing the dependency on calibration data. Nevertheless, despite these efforts, true zero-training BCIs are rather rare. Event-Related Potential based spellers One of the most common types of BCI is the Event-Related Potentials (ERP) based BCI, which was invented by Farwell and Donchin in 1988. In the ERP-BCI, actions, such as spelling a letter, are coupled to specific stimuli. The computer continuously presents these stimuli to the user. By attending a specific stimulus, the user is able to select an action. More concretely, in the original ERP-BCI, these stimuli were the intensifications of rows and column in a matrix of symbols on a computer screen. By detecting which row and which column elicit an ERP response, the computer can infer which symbol the user wants to spell. Initially, the ERP-BCI was aimed at restoring communication, but novel applications have been proposed too. Examples are web browsing, gaming, navigation and painting. Additionally, current BCIs are not limited to using visual stimuli, but variations using auditory or tactile stimuli have been developed as well. In their quest to improve decoding performance in the ERP-BCI, the BCI community has developed increasingly more complex machine learning algorithms. However, nearly all of them rely on intensive subject-specific fine-tuning. The current generation of decoders has gone beyond a standard ERP classifier and they incorporate language models, which are similar to a spelling corrector on a computer, and extensions to speed up the communication, commonly referred to as dynamic stopping. Typically, all these different components are separate entities that have to be tied together by heuristics. This introduces an additional layer of complexity and the result is that these state of the art methods are difficult to optimise due to the large number of free parameters. We have proposed a single unified probabilistic model that integrates language models and a natural dynamic stopping strategy. This coherent model is able to achieve state of the art performance, while at the same time, minimising the complexity of subject-specific tuning on labelled data. A second and major contribution of this thesis is the development of the first unsupervised decoder for ERP spellers. Recall that typical decoders have to be tuned on labelled data for each user individually. Moreover, recording this labelled data is a tedious process, which has no direct use for the end user. The unsupervised approach, which is an extension of our unified probabilistic model, is able to learn how to decode a novel user’s brain signals without requiring such a labelled dataset. Instead, the user starts using the system and in the meantime the decoder is learning how to decode the brain signals. This method has been evaluated extensively, both in an online and offline setting. Our offline validation was executed on three different datasets of visual ERP data in the standard matrix speller. Combined, these datasets contain 25 different subjects. Additionally, we pres