In this work, Anti Ingel has developed a classification method for steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI). Brain-computer interface is a direct communication channel between the brain and an external device. SSVEP-based BCI uses visual stimuli, called targets, to elicit a response in the brain of the user, extract features from the recorded brain activity and, finally, classify the result as one of the commands that the user can send to the external device.
Often, the visual stimuli used in SSVEP-based BCIs are multiple squares displayed on a computer screen, each flickering with different frequency. Different flickering frequencies elicit different brain responses and there are many feature extraction methods to evaluate the presence of this signal in a recorded electroencephalography (EEG) signal.
The novelty of the classification method proposed in this work is that it is based on direct information transfer rate (ITR) maximisation. ITR is a standard measure of performance for BCIs. It combines the accuracy and the speed of the classifier into a single number which shows how much information is transferred by the BCI in one unit of time. Therefore, maximising ITR maximises the amount of information that the user can transfer to an external device (computer, robot, etc) in a fixed time interval.
Anti Ingel, Ilya Kuzovkin and Raul Vicente Direct information transfer rate optimisation for SSVEP-based BCI in Journal of Neural Engineering, Volume 16, Number 1
Often, the visual stimuli used in SSVEP-based BCIs are multiple squares displayed on a computer screen, each flickering with different frequency. Different flickering frequencies elicit different brain responses and there are many feature extraction methods to evaluate the presence of this signal in a recorded electroencephalography (EEG) signal.
The novelty of the classification method proposed in this work is that it is based on direct information transfer rate (ITR) maximisation. ITR is a standard measure of performance for BCIs. It combines the accuracy and the speed of the classifier into a single number which shows how much information is transferred by the BCI in one unit of time. Therefore, maximising ITR maximises the amount of information that the user can transfer to an external device (computer, robot, etc) in a fixed time interval.
Anti Ingel, Ilya Kuzovkin and Raul Vicente Direct information transfer rate optimisation for SSVEP-based BCI in Journal of Neural Engineering, Volume 16, Number 1
