Previously we contributed to the development of a brain-computer interface (Brainput) using functional near infrared spectroscopy (NIRS). This NIRS-based BCI was designed to improve performance on a human-robot team task by dynamically adapting a robot’s autonomy based on the person’s multitasking state. Two multitasking states (corresponding to low and high workload) were monitored in real-time using an SVM-based model of the person’s hemodynamic activity in the prefrontal cortex. In the initial evaluation of Brainput’s efficacy, the NIRS-based adaptivity was found to significantly improve performance on the human-robot team task (from a baseline success rate of 45% to a rate of 82%). However, failure to find any performance improvements in an extension of the original evaluation prompted a reinvestigation of the system via: (1) a reanalysis of Brainput’s signal processing on a larger NIRS dataset and (2) a placebo-controlled replication using random (instead of NIRS-based) state classifications [1].
@inproceedings{straitscheutz14hcii,
title={NIRS-based BCIs: Reliability and Challenges},
author={Megan Strait and Matthias Scheutz},
year={2014},
booktitle={HCI International},
url={https://hrilab.tufts.edu/publications/straitscheutz14hcii.pdf}
doi={10.1007/978-3-319-07857-1_81}
}