Data from: Adaptive multi-degree of freedom Brain Computer Interface using online feedback: Towards novel methods and metrics of mutual adaptation between humans and machines for BCI.

This paper proposes a novel adaptive online-feedback methodology for Brain
Computer Interfaces (BCI). The method uses ElectroEncephaloGraphic (EEG)
signals and combines motor with speech imagery to allow for tasks that
involve multiple degrees of freedom (DoF). The main approach utilizes the
covariance matrix descriptor as feature, and the Relevance Vector Machines
(RVM) classifier. The novel contributions include, (1) a new method to
select representative data to update the RVM model, and (2) an online
classifier which is an adaptively-weighted mixture of RVM models to
account for the users' exploration and exploitation processes during
the learning phase. Instead of evaluating the subjects' performance
solely based on the conventional metric of accuracy, we analyze their
skill's improvement based on 3 other criteria, namely the confusion
matrix's quality, the separability of the data, and their
instability. After collecting calibration data for 8 minutes in the first
run, 8 participants were able to control the system while receiving visual
feedback in the subsequent runs. We observed significant improvement in
all subjects, including two of them who fell into the BCI illiteracy
category. Our proposed BCI system complements the existing approaches in
several aspects. First, the co-adaptation paradigm not only adapts the
classifiers, but also allows the users to actively discover their own way
to use the BCI through their exploration and exploitation processes.
Furthermore, the auto-calibrating system can be used immediately with a
minimal calibration time. Finally, this is the first work to combine motor
and speech imagery in an online feedback experiment to provide multiple
DoF for BCI control applications.