A Comparison between BCI Simulation and Neurofeedback for ForwardBackward Navigation in Virtual Reality

Abstract EN

A brain-computer interface (BCI) decodes the brain signals representing a desire to do something and transforms those signals into a control command.

However, only a limited number of mental tasks have been previously investigated and classified.

This study aimed to investigate two motor imagery (MI) commands, moving forward and moving backward, using a small number of EEG channels, to be used in a neurofeedback context.

This study also aimed to simulate a BCI and investigate the offline classification between MI movements in forward and backward directions, using different features and classification methods.

Ten healthy people participated in a two-session (48 min each) experiment.

This experiment investigated neurofeedback of navigation in a virtual tunnel.

Each session consisted of 320 trials where subjects were asked to imagine themselves moving in the tunnel in a forward or backward motion after a randomly presented (forward versus backward) command on the screen.

Three electrodes were mounted bilaterally over the motor cortex.

Trials were conducted with feedback.

Data from session 1 were analyzed offline to train classifiers and to calculate thresholds for both tasks.

These thresholds were used to form control signals that were later used online in session 2 in neurofeedback training to trigger the virtual tunnel to move in the direction requested by the user’s brain signals.

After 96 min of training, the online band-power neurofeedback training achieved an average classification of 76%, while the offline BCI simulation using power spectral density asymmetrical ratio and AR-modeled band power as features, and using LDA and SVM as classifiers, achieved an average classification of 80%.