Detecting Attention Breakdowns in Robotic Neurofeedback Systems

This paper focuses on the EEG measures suitable for robotic neurofeedback systems, able to detect and intervene in case of attention breakdowns. Such systems can be useful tools in cognitive remediation of children with ASD, in particular to improve their attention span. The proposed study focuses on a particular EEG measure, the Theta/Beta ratio, as convenient feature for recognizing attention states in scenarios of cognitive effort. A setup for the validation of this measure employing pupil dilation as attention baseline is introduced. Results show coherence between the Theta/Beta ratio and the pupil dilation.