Abstract
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.
Highlights
In an effort to ameliorate rehabilitation and neural pathology treatment, Brain Computer Interfaces (BCI) aim to provide a solution where users can use brain signals to directly interact with the environment
Despite being successful at a considerable number of subjects [2, 3], BCI based on motor imagery still suffers from several deficiencies, which restrict its use in some practical applications
Most BCI systems rely on binary classification, such as left vs. right hand imagery, whereas the highest number of degrees of freedom (DoF) is achieved based on classification between four classes [6]
Summary
In an effort to ameliorate rehabilitation and neural pathology treatment, Brain Computer Interfaces (BCI) aim to provide a solution where users can use brain signals to directly interact with the environment. Despite being successful at a considerable number of subjects [2, 3], BCI based on motor imagery still suffers from several deficiencies, which restrict its use in some practical applications. The conventional BCI systems often require a lengthy, off-line calibration step, which includes recording brain signals without feedback and training a statistic model, before it can be used. BCI systems can usually offer an only limited number of DoF. Most BCI systems rely on binary classification, such as left vs right hand imagery, whereas the highest number of DoF is achieved based on classification between four classes [6]
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