Cortically-driven Assistance Adaptation in Sensorimotor Training (CA2ST)

After neurologic injury such as a stroke, humans may lose full control over affected limbs due to lesions in functional brain areas which are responsible for motor control and sensation. Traditional therapy approaches focus on the patient’s active participation with the affected limb. Such active therapy concepts are inapplicable for severely impaired patients with no or very limited residual motor function.
The goal of this project is to investigate a novel therapy concept that allows severely impaired patients to actively participate in the therapy. By means of a brain-computer interface (BCI), the intention to move the impaired limb is detected, and assistance is provided by a robot accordingly. In other words, we aim at artificially bypass the injured sensorimotor loops by classifying cortical signals and use them to control assistance from robotic devices.
Cortical data are obtained by near infrared spectroscopy (NIRS). This non-invasive brain imaging technique uses the spectral properties of near-infrared light (600 to 900 nm wavelength) to measure the cortical hemodynamics. Through neurovascular coupling, the local brain activation alters the hemodynamics as measured with NIRS. Besides using commercially available NIRS devices, we focus on the design of sensor prototypes with increased sensitivity, modular configuration and miniaturized footprint.
Functional measurements of cortical activity together with the data of various systemic recordings (such as heart rate, galvanic skin response, respiration frequency and blood pressure) are being used to classify the intention to accomplish a given motor task.
By interacting with this brain-computer-interface (BCI) through the remaining intact brain regions, we believe to enforce to reorganization of the sensorimotor loops and thus increase further the therapeutic outcome.

Contacts

Raphael Zimmermann, MSc ETH ME
PhD Student
Biomedical Optics Research Lab (BORL)
Division of Neonatology
Department of Obstetrics and Gynecology
CH-8091 Zurich/Switzerland

Raphael.Zimmermann@usz.ch

References

Zimmermann, R., Braun, F., Achtnich, T., Lambercy, O., Gassert, R. and Wolf, M. (2013). Silicon photomultipliers for improved detection of low light levels in miniature near-infrared spectroscopy instruments. Biomedical Optics Express, 4(5):659–666.

Zimmermann, R., Marchal-Crespo, L., Edelmann, J., Lambercy, O., Fluet, M.-C., Riener, R., Wolf, M. and Gassert, R. (2013). Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study. Journal of NeuroEngineering and Rehabilitation, 10(4).

Marchal-Crespo, L., Zimmermann, R., Lambercy, O., Edelmann, J., Fluet, M.-C., Wolf, M., Gassert, R. and Riener, R. (2013). Motor Execution Detection Based on Autonomic Nervous System Responses. Physiological Measurements, 34(1):35–51.

Zimmermann, R., Marchal-Crespo, L., Lambercy, O., Fluet, M.-C., Metzger, J.-C., Edelmann, J., Brand, J., Eng, K., Riener, R., Wolf, M., and Gassert, R. (2013). What’s Your Next Move? Detecting Movement Intention for Stroke Rehabilitation. In Guger, Christoph, Allison, Brendan Z., and Edlinger, Günter, editors, Brain-Computer Interface Research: A State-of-the-Art Summary. Springer Berlin Heidelberg. Chapter 4, pages 23-37.

Metzger, J.-C., Lambercy, O., and Gassert, R. (2012). High-Fidelity Rendering of Virtual Objects with the ReHapticKnob - Novel Avenues in Robot-Assisted Rehabilitation of Hand Function. Proc. Haptics Symposium 2012, pp. 51-56.

Brand, J., Geisseler, O., Holper, L., Hepp-Reymond, M.-C., Morari, M., Kiper, D., and Eng, K. (2011). The effects of manipulation of visual feedback in virtual reality on cortical activity: A pilot study. Virtual Rehabilitation (ICVR), 2011 International Conference on, pages 1-2.

Metzger, J.-C., Lambercy, O., Chapuis, D. and Gassert, R. (2011). Design and characterization of the ReHapticKnob, a robot for assessment and therapy of hand function, Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 3074-3080.

Zimmermann, R., Marchal-Crespo, L., Lambercy, O., Fluet, M.-C., Riener, R., Wolf, M., and Gassert, R. (2011). Towards a BCI for sensorimotor training: initial results from simultaneous fNIRS and biosignal recordings. Proc. IEEE Engineering in Medicine and Biology Conference (EMBC), pages 6339–6343

Collaborators

BORL (Biomedical Optics Research Lab. Head: PD Dr. Martin Wolf)
SMS (Sensory-Motor Systems Lab. Head: Prof. Dr. Robert Riener)
INI (Institute of Neuroinformatics. Group Leader: Dr. Kynan Eng)

Financial Support

Collaborative, Highly Interdisciplinary Research Projects, stage 1 (CHIRP1) of ETH Zurich