The lab uses principles of neuroscience, engineering and mathematics to assess emerging prosthetic devices and to direct their improvement. This work started as part of a large project that aimed to provide upper-limb amputees with more capable prostheses by directly connecting devices with nervous systems. Being able to measure prosthesis system performance is important to direct improvements in the lab and track patient progress in clinical settings.
The Control Bottleneck Index
One assessment we have developed is the Control Bottleneck Index (CBI) which aims to separately assess the quality of the control system, sensory feedback, and training regimen to help biomedical engineers target improvements to the weakest link in their systems. In this work we use mathematical methods to simulate the human nervous system and solve for theoretical parameters of interest using empirical data.
We are also working on several assessments to measure aspects of limb embodiment, i.e. how a limb (real, prosthetic or virtual) becomes considered a part of one's own body. Improving limb embodiment is thought to be important in improving the acceptance and utility of prosthetic devices. As we try to move away from subjective self-report surveys to measure embodiment, we have developed psychophysical methods to measure specific aspects of embodiment, including ownership and agency.
Cognitive load and attention assessments
To broaden our assessment toolkit we are using brain activity recordings (electroencephalography, or EEG) as an indicator of cognitive load. We are trying to use a certain type of detected brain activity (alpha waves) to measure, in real time, how much mental processing is occurring. We hope to use this to test out prosthetic devices and quickly identify when new systems are overlying taxing on mental resources. Using cameras to measure eye gaze direction, we are also incorporating attentional information into our prosthesis assessments. Where a person focuses their attention gives us clues as to how much trust a person has in the limb they are moving.
Marasco, P, J Hebert, J Sensinger, C Shell, J Schofield, Z Thumser, R Nataraj, D Beckler, M Dawson, D Blustein, S Gill, B Mensh, R Granja-Vazquez, M Newcomb, J Carey, B Orzell. 2018. Illusory movement perception improves motor control for prosthetic hands. Science Translational Medicine. DOI:10.1126/scitranslmed.aao6990
Blustein, D. H., Gill, S., Wilson, A. W. & Sensinger, J. W. The control bottleneck index: a novel outcome metric providing generalizable and actionable assessment of upper-limb prosthetic systems. in (2017).
Blustein, D. & Sensinger, J. Extending a Bayesian estimation approach to model human movements. in (Society for Neuroscience, 2016).
Blustein, D, J Sensinger. 2017. Validation of a constrained-time movement task for use in rehabilitation outcome
measures. International Conference on Rehabilitation Robotics (ICORR). DOI:10.1109/ICORR.2017.8009410
Blustein, D, A Wilson & J Sensinger. 2018. Assessing the quality of supplementary sensory feedback using the crossmodal congruency task. Scientific Reports. DOI:10.1038/s41598-018-24560-3
Blustein, D. et al. 2020. Towards Objective Assessment of Ownership Over a Prosthesis. MEC20 Symp. Conference abstract
Stiegelmar, C., Blustein, D., Sensinger, J., Hebert, J. & Shehata, A. 2020. Towards Quantifying the Sense of Agency and Its Contribution to Embodiment of Myoelectric Prostheses. MEC20 Symp. Conference abstract.
Wilson, A, D Blustein, J Sensinger. 2017. A third arm - Design of a bypass prosthesis enabling incorporation. International Conference on Rehabilitation Robotics (ICORR). DOI:10.1109/ICORR.2017.8009441
Blustein, D., Gill, S., Wilson, A. & Sensinger, J. 2019. Crossmodal congruency effect scores decrease with repeat test exposure. PeerJ. DOI: 10.7717/peerj.6976
Ortiz, O., Blustein, D. & Kuruganti, U. Frontoparietal power-based connectivity analysis across different frequencies during a working memory task. CMBES Proc. 44, (2021).
Ortiz, O., Blustein, D. & Kuruganti, U. Test-retest reliability of time-domain EEG features to assess cognitive load using a wireless dry-electrode system. in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (2020).
Ortiz, O., Kuruganti, U. & Blustein, D. A Platform to Assess Brain Dynamics Reflective of Cognitive Load During Prosthesis Use. MEC20 Symp. (2020).