Gaussian Process for a Single-channel EEG Decoder with Inconspicuous Stimuli and Eyeblinks

A single-channel electroencephalography (EEG) device, despite being widely accepted due to convenience, ease of deployment and suitability for use in complex environments, typically poses a great challenge for reactive brain-computer interface (BCI) applications particularly when a continuous command from users is desired to run a motorized actuator with different speed profiles. In this study, a combination of an inconspicuous visual stimulus and voluntary eyeblinks along with a machine learning-based decoder is considered as a new reactive BCI paradigm to increase the degree of freedom and minimize mismatches between the intended dynamic command and transmitted control signal. The proposed decoder is constructed based on Gaussian Process model (GPM) which is a nonparametric Bayesian approach that has the advantages of being able to operate on small datasets and providing measurements of uncertainty on predictions. To evaluate the effectiveness of the proposed method, the GPM is compared against other competitive techniques which include k-Nearest Neighbors, linear discriminant analysis, support vector machine, ensemble learning and neural network. Results demonstrate that a significant improvement can be achieved via the GPM approach with average accuracy reaching over 96% and mean absolute error of no greater than 0.8 cm/s. In addition, the analysis reveals that while the performances of other existing methods deteriorate with a certain type of stimulus due to signal drifts resulting from the voluntary eyeblinks, the proposed GPM exhibits consistent performance across all stimuli considered, thereby manifesting its generalization capability and making it a more suitable option for dynamic commands with a single-channel EEG-controlled actuator.