Home Research Project Details 3d - Adaptive multi-electrode monitoring of cortical movement plans for neuroprosthetic control
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3d - Adaptive multi-electrode monitoring of cortical movement plans for neuroprosthetic control

Alexander Gail, Stefan Treue, and Thomas Recording GmbH

Signals from different brain regions can be used to control motor-prosthetic devices. Major limitations for such neuroprosthetic brain-machine interfaces result from the mixture of motor-related activity with other cognitive signals, especially during unconstraint (‘free’) movements, and insufficient long-term stability of the neural signals with the recording techniques available today. If brain signals can not be identified unambiguously, and recorded stably, this will lead to misinter­pretations of the motor commands and improper control to the prosthetic device. Signal loss could be prevented by continuous adaptive re-adjusting of microelectrode positions, but not in currently available chronic multi-electrode implants, since these are not adjustable.

The goal of this project is to achieve proper motor-goal identification from cortical activity, independent of other cog­nitive signals, and to develop an adaptive multi-electrode positioning (AMEP) system for semi-autonomous cortical multi-channel recordings. Both developments are important for improved brain machine interfacing in neuroprosthetic control and provide links to subprojects 3a and 3b.

Fig. SP3d: Identification of movement plans proper from cortical sensorimotor areas by separating them from other cognitive signals, avoiding confounds in time-continuous on-line decoding as needed for neuroprosthetic control. The project aims at technologies for improved motor-goal identification and long-term recording stability.

Belongs to Group(s):
Thomas RECORDING GmbH, Cognitive Neuroscience, Sensorimotor transformations

Is part of  Section 3 

Members working within this Project:
Gail, Alexander  
Thomas , Uwe  
Treue, Stefan  

Selected Publication(s):

Chakrabarti, S, Hebert, P, Wolf, MT, Campos, M, Burdick, JW, and Gail, A (2012).
Expert-like performance of an autonomous spike tracking algorithm in isolating and maintaining single units in the macaque cortex
Journal of Neuroscience Methods 205:72-85.

Klaes, C, Schneegans, S, Schöner, G, and Gail, A (2012).
Sensorimotor learning biases choice behavior: a learning neural field model for decision making
PLOS Computational Biology 8(11):1-19.

Klaes, C, Westendorff, S, Chakrabarti, S, and Gail, A (2011).
Choosing Goals, not Rules: Deciding among Rule-Based Action Plans
Neuron 70:536-548.

Westendorff, S, and Gail, A (2011).
What is ‚anti‘ about anti-reaches? - How reference frames affect reach reaction times
Experimental Brain Research 208:287-296.

Westendorff, S, Klaes, C, and Gail, A (2010).
The cortical timeline for deciding on reach motor-goals
Journal of Neuroscience 30(15):5426-5436.

Gail, A, Klaes, C, and Westendorff, S (2009).
Implementation of Spatial Transformation Rules for Goal-Directed Reaching via Gain Modulation in Monkey Parietal and Premotor Cortex
Journal of Neuroscience 29:9490-9499.