Exploring multiple protocols for a brain-computer interface mouse

Abstract

Abstract-In recent years, various visual protocols have been explored for P300-based BCI. In stimulus-driven BCI paradigms such as P300 BCIs it is vital to optimise the stimulation protocol as much as possible in order to achieve the best performance. Due to the inherent variability between subjects and the complex nature of the brain it is unlikely that an optimal protocol will be identified through a single iteration of random exploration. That is why in this paper we explore 8 different visual protocol configurations based on recent literature, in the hope of identifying key features that can later be used to create further improved protocols. Results indicate that luminosity changes, the standard method of stimulation used in visual P300 BCI protocols, do provide the best performance of the variations presented here.

Novel sequential protocols for a ERP based BCI mouse

“…However, preliminary online tests and further offline analysis showed that the mouse pointer was hard to control. So, we explored a variety of alternatives to this approach in [15] where 8 different stimuli (circles at the centre of the screen: 4 for "up", "down", "left" and "right", and 4 for the 45 degree diagonal directions) were used. A total of 8 oddball protocols were analysed which differed on the basis of SOA as well as in the features of the stimuli which were randomly varied.…”

Evolution of a Brain-Computer Interface Mouse via Genetic Programming

“…The point is chosen by projecting the end-point of the trajectory produced by a linear SVM trained on the same data as GP in the same way as in [10]. In this way, when comparing the results obtained by GP to those produced by an SVM, we ensure both had the same ideal trajectories.…”

“…However, the success of an analogue approach to BCI mouse design and the benefits accrued in it through the use a machine learning techniques suggest that there is more mileage in this approach. In particular, we should note that, effectively, although both in [3] and [10] feature selection was performed using powerful algorithms, only semilinear transformations were used to transform EEG signals into mouse movements. Linear systems have obvious limitations, particularly in relation to noise and artefact rejection.…”

“…A variety of alternatives to this scheme were explored in [10] where 8 different stimuli (4 for up, down, left and right, and 4 for the 45 degree diagonal directions) were used. A linear support-vector machine (SVM) was trained to classify the ERPs produced in response to stimuli into two classes: target and non-target.…”

Evolution of an Effective Brain-Computer Interface Mouse via Genetic Programming with Adaptive Tarpeian Bloat Control

“…Given the point-and-click nature of most modern user interfaces, an important application of BCI is controlling 2-D pointer movements. Some initial success in developing an ERP-based mouse has been reported in (Citi et al, 2008) and a variety of alternatives to this scheme were explored in (Salvaris et al, 2010). However, in both cases only semilinear transformations were used to transform EEG signals into mouse movements.…”