Recognition of user’s mental engagement is imperative to the success of Human Machine Interaction (HMI). By conducting a series of human experiments, we obtain implicit information about the cognitive and affective state of the subject using passive BCI recordings. We highlight the drawbacks of using conventional workload metrics as indicators of human engagement and assert that motor and cognitive workloads be differentiated. We propose new indices for flow measurements and advocate the development of an adaptive system that ensures high mental engagement while utilizing the benefits of implicit training in robotic rehabilitation via two case studies.
We conduct passive BCI recordings on 8 subjects while they guide a robot with variable haptic resistance through a maze. We propose a new set of features for workload assessment, i.e. relative C3-C4 at beta and sigma frequency bands and differential Fz-POz at theta and alpha bands. A clear distinction between motor and cognitive workload has been made. We conclude that an increase in motor workload does not necessarily increase the cognitive workload and vice versa. We do not use any of the conventional workload metrics for engagement identification and suggest a novel paradigm for the same.
In another study, we instruct 8 subjects to play a game with varying difficulty levels while recording their passive EEG activity. We use power spectral density and coherence values features as a combination to develop a classifier that separates the 3 difficulty levels in the game with a mean accuracy of 82.46%. We propose the use of higher frequency bands in flow measurement, as opposed to traditional flow classification methods that use lower frequency bands for the same. We conclude that high beta and sigma bands are the most informative bands in flow classification. Our method reports an accuracy increase of 19.98% when compared to the ones using the lower frequency bands for flow measurement.