Abstract
Studies have established that it is possible to differentiate between the brain's responses to observing correct and incorrect movements in navigation tasks. Furthermore, these classifications can be used as feedback for a learning-based BCI, to allow real or virtual robots to find quasi-optimal routes to a target. However, when navigating it is important not only to know we are moving in the right direction toward a target, but also to know when we have reached it. We asked participants to observe a virtual robot performing a 1-dimensional navigation task. We recorded EEG and then performed neurophysiological analysis on the responses to two classes of correct movements: those that moved closer to the target but did not reach it, and those that did reach the target. Further, we used a stepwise linear classifier on time-domain features to differentiate the classes on a single-trial basis. A second data set was also used to further test this single-trial classification. We found that the amplitude of the P300 was significantly greater in cases where the movement reached the target. Interestingly, we were able to classify the EEG signals evoked when observing the two classes of correct movements against each other with mean overall accuracy of 66.5 and 68.0% for the two data sets, with greater than chance levels of accuracy achieved for all participants. As a proof of concept, we have shown that it is possible to classify the EEG responses in observing these different correct movements against each other using single-trial EEG. This could be used as part of a learning-based BCI and opens a new door toward a more autonomous BCI navigation system.
Highlights
Studies concerning robotic movement and navigation tasks have previously used electroencephalography (EEG) to investigate the brain’s responses to observing correct and erroneous movements
We evaluated data from a virtual robotic navigation task
The navigation scenarios presented in this study provided a further challenge compared to many previous P300-related systems, as each stimulus was only presented once
Summary
Studies concerning robotic movement and navigation tasks have previously used electroencephalography (EEG) to investigate the brain’s responses to observing correct and erroneous movements. You Have Reached Your Destination control the low-level action decisions in order to navigate semi-autonomously toward a target, with feedback provided via implicit communication with a user through brain signals spontaneously generated while observing the task (Iturrate et al, 2015; Zander et al, 2016). None of these previous studies have investigated whether it is possible to classify EEG responses to different types of correct actions against each other. It is highly important to consider whether there are significant neurophysiological differences between the brain’s responses to observing different correct movements: those that get closer to a target, compared to those that reach it
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