Prediction of Navigational Decisions in the Real-World: A Visual P300 Event-Related Potentials Brain-Computer Interface

Abstract

ABSTRACT Despite the widespread availability of portable neuroimaging systems, current applications of brain-computer interfaces (BCI) have largely remained confined to laboratory and clinical settings. In order to develop BCI that will assist individuals across a wide range of everyday life behaviors, it is critical to test paradigms that have been successful in highly controlled setups within the frame of real-world environments. In the present study, a visual P300 paradigm was used to elicit neural responses reflective of decision making within the frame of real-world navigation. Participants (n = 8) were equipped with a tablet and a mobile EEG system while navigating through university corridors. Upon reaching an intersection, participants were presented with left and right arrows flashing on the tablet (and additional non-directional stimuli in the distractor condition). Neural responses elicited by the presentation of such stimuli (Event-Related Potentials) were recorded along with the participant’s navigational decisions. The participants completed two separate sessions to collect data for both the training and testing of an offline BCI system aimed at predicting their navigational decisions based on the extraction of event-related potentials features. Single-trial classification accuracy reached 59.6% (up to 72.3% when classifying group averages of six trials). Individual classification results were contrasted with single-trial analysis. Results are discussed in terms of their implications for the design of real-world BCI applications, and several recommendations to improve experimental protocols are proposed.