Machine Learning in VEP-based BCI

Anti Ingel
In this thesis, a classification method for SSVEP-based BCI is proposed. The classification method is based on simple comparisons of extracted feature values and thresholds and it involves a way of optimising the thresholds. Optimising the thresholds is formalised as a maximisation task of the information transfer rate of BCI, but instead of using the standard formula for calculating ITR, more general formula is derived. This allows the thresholds to be automatically optimised and avoids calculating incorrect ITR estimate.
The proposed method shows good performance in classifying targets of a BCI and achieves ITR as high as 60 bit/min. The proposed method also provides a way to reduce false classifications, which is important in real-world applications. BCIs have high potential to be used in the field of medicine as they provides a way for severely disabled people to control external devices.
Graduation Thesis language
Graduation Thesis type
Master - Computer Science
Ilya Kuzovkin, Raul Vicente
Defence year