Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation

Publication date: Available online 1 June 2019Source: NeuroImageAuthor(s): C. Vidaurre, A. Ramos-Murguialday, S. Haufe, M. Gómez-Fernández, K.-R. Müller, V.V. NikulinAbstractAn important goal in Brain-Computer Interfacing (BCI) is to find and enhance procedural strategies for users for whom BCI control is not sufficiently accurate. To address this challenge, we conducted offline analyses and online experiments to test whether the classification of different types of motor imagery could be improved when the training of the classifier was performed on the data obtained with the assistive muscular stimulation below the motor threshold. 10 healthy participants underwent three different types of experimental conditions: a) Motor imagery (MI) of hands and feet b) sensory threshold neuromuscular electrical stimulation (STM) of hands and feet while resting and c) STM - when performing motor imagery involving a stimulated joint (BOTH). Also, another group of 10 participants underwent conditions a) and c). Then, online experiments with 15 users were performed. These subjects received neurofeedback during MI using classifiers calibrated either on MI or BOTH data recorded in the same experiment. Offline analyses showed that decoding MI alone using a classifier based on BOTH resulted in a better BCI accuracy compared to using a classifier based on MI alone. Online experiments confirmed accuracy improvement of MI alone being decoded with the classifier trained on BOTH data. In addition,...
Source: NeuroImage - Category: Neuroscience Source Type: research