Adaptive Interactive Learning: a Novel Approach to Training Brain-Computer Interface Systems
Name
Ilja Kuzovkin
Abstract
A Brain-Computer Interface is a system which allows communication between a human and a computer. Using various neuroimaging techniques the brain activity is recorded and transmitted to the computer, where the signal is analyzed with the help of machine learning methods. The ultimate goal of BCI is to empower the human with the ability to control the external device with the power of thought.
However, distinguishing mental states of a human is a challenging task and standard machine learning alone is not enough to solve the problem. Acceptable level of performance can be achieved after a long training process, during which the human learns how to produce suitable mental states and the machine creates a model, which is able to classify the signal.
In this thesis we proposed a conceptually new approach to the process of training a BCI system. It relies on the idea of the interaction between the test subject and the machine and the ability of those two agents to adapt their behavior accordingly to the information they receive during the learning process. The approach is proposed as a counterpart to the traditional BCI training, where the test subject does not receive any feedback.
Another novelty in comparison to the traditional approach is using an unsupervised learning algorithm (SOM) as the core of the learning system. The original concept of self-organizing maps is amended to represent a probabilistic predictive model, which can be used to classify the brain signal, provide feedback and adapt the model in real time.
Based on the results of the conducted experiments we conclude that adaptive learning process has the multiple major advantages over the traditional one.
Graduation Thesis language
English
Graduation Thesis type
Master - Computer Science
Supervisor(s)
Konstantin Tretjakov
Defence year
2013