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BFNT-Chair Neuroinformatics

The BFNT-Chair Neuroinformatics is headed by Dario Farina.

The generation of a movement is the combination of discrete events (action potentials) generated in the brain, spinal cord, nerves, and muscles. These discrete events are the result of ion exchanges across membranes, electrochemical mechanisms, and active ion pumping through energy expenditure. The ensemble of spike trains discharged in the various parts of the neuromuscular system constitutes the neural code for movements. Recording and interpretation of this code provides the means for decoding the motor system. The main current limitation in the investigation of the motor system is the impossibility of detecting and processing in the intact human the activity of a sufficiently large number of neural cells and sensory afferents to associate a functional meaning to the cellular mechanisms that ultimately determine a movement. This limitation in turn impedes to answer many fundamental questions on the control of human movements, with important implications in neurorehabilitation technologies, such as man-machine interfaces.

Signals used for controlling man-machine interfaces may be detected from the brain, peripheral nerves, muscles, or can be directly the forces produced by the motor system. These levels correspond to decreased levels of complexity in interpretation as the level of “biological decoding” increases. Within the Bernstein Center, this project plan addresses the decoding of the neural code at the peripheral nerve and muscle level for new paradigms of man-machine interfaces.

The project will advance electrodes for interfacing motor neuron and nerve activity, either with implantation into nerves and muscles or with non-invasive systems, and develop novel strategies for decoding spike trains from these recordings. These methods will be used for furthering our understanding of the neural control of human movement and for the design of new paradigms of control of man-machine interface systems, especially myoelectric prostheses. The approach proposed will be based on a deeper understanding of fundamental open issues in motor control, on which the new man-machine interface systems will be based.

The project is characterized by the link between new knowledge on the neural mechanisms that are the determinants of movement and the motor functions, in vivo in humans. If the task is successful, it will be possible to decode the neural code underlying movements. In addition to the contributions to our understanding on human movement, this achievement will permit development of interfaces with external devices in a robust and intuitive way, which will lead to a new generation of closed-loop man-machine interfaces.

Belongs to Group(s):
Neurorehabilitation Engineering

Is part of  BFNT-Chair Neuroinformatics 

Members working within this Project:
Farina, Dario 

Selected Publication(s):

Amsüss, S, Goebel, PM, Jiang, N, Graimann, B, Paredes, L, and Farina, D (2014).
Self-Correcting Pattern Recognition System of Surface EMG Signals for Upper Limb Prosthesis Control
IEEE Transaction on Biomedical Enigeering 61(4):1167-1176.

D'Alonzo, M, Dosen, S, Cipriani, C, and Farina, D (2014).
HyVE: Hybrid Vibro-Electrotactile Stimulation for Sensory Feedback and Substitution in Rehabilitation
IEEE Transactions on Neural Systems and Rehabilitation Engineering 22(2):290-301.

D'Alonzo, M, Dosen, S, Cipriani, C, and Farina, D (2014).
HyVE—Hybrid Vibro-Electrotactile Stimulation—Is an Efficient Approach to Multi-Channel Sensory Feedback
IEEE Transactions on Haptics 7(2):1-10.

Jiang, N, Rehbaum, H, Vujaklija, I, Graimann, B, and Farina, D (2014).
Intuitive, Online, Simultaneous, and Proportional Myoelectric Control Over Two Degrees-of-Freedom in Upper Limb Amputees
IEEE Transactions on Neural Systems and Rehabilitation Engineering 22(3):501-510 (10pp).

Jiang, N, Vujaklija, I, Rehbaum, H, Graimann, B, and Farina, D (2014).
Is accurate mapping of EMG signals on kinematics needed for precise online myoelectric control?
22 3(549-558):10.

Jorgovanovic, N, Dosen, S, Djozic, DJ, Krajoski, G, and Farina, D (2014).
Virtual Grasping: Closed-Loop Force Control Using Electrotactile Feedback
Computational and Mathematical Methods in Medicine 2014:13.

Markovic, M, Dosen, S, Cipriani, C, Popovic, D, and Farina, D (2014).
Stereovision and augmented reality for closed-loop control of grasping in hand prostheses
Journal of Engineering 11(046001):1-17.

Roche, AD, Rehbaum, H, Farina, D, and Aszmann, OC (2014).
Prosthetic Myoelectric Control Strategies: A Clinical Perspective
Curr Surg Rep 2:44:1-11.

Amsüss, S, Paredes, LP, Rudigkeit, N, Graimann, B, Herrmann, MJ, and Farina, D (2013).
Long term stability of surface EMG pattern classification for prosthetic control
35th Annual International Conference of the IEEE EMBS:4.

Farina, D, Negro, F, and Jiang, N (2013).
Identification of common synaptic inputs to motor neurons from the rectified electromyogram
The Journal of Physiology 591(10):2403-2418.

Jiang, N, Muceli, S, Graimann, B, and Farina, D (2013).
Effect of arm position on the prediction of kinematics from EMG in amputees
Med Biol Eng Comput 51((1-2)):143-51.

Dideriksen, JL, Negro, F, Enoka, RM, and , DF (2012).
Motor unit recruitment strategies and muscle properties determine the influence of synaptic noise on force steadiness.
J Neurophysiol 107(12):3357-69.

Farina, D, and Negro, F (2012).
Accessing the Neural Drive to Muscle and Translation to Neurorehabilitation Technologies
IEEE Rev Biomed Eng. PP(99).

Gianfelici, F, and Farina, D (2012).
An Effective Classification Framework for Brain-Computer Interfacing Based on a Combinatoric Setting
IEEE Trans Signal Proc. 60:1446-1459.

Jiang, N, Dosen, S, Müller, KR, and Farina, D (2012).
Myoelectric Control of Artificial Limbs - Is There a Need to Change Focus?

Jiang, N, Nielsen, LG, Muceli, S, and Farina, D (2012).
EMG-based Simultaneous and Proportional Estimation of Wrist/Hand Dynamics in Unilateral Trans-radial Amputees
Journal of NeuroEngineering and Rehabilitation 9(42).

Muceli, S, and Farina, D (2012).
Simultaneous and proportional estimation of hand kinematics from EMG during mirrored movements at multiple degrees-of-freedom.
IEEE Trans Neural Syst Rehabil Eng. 20(3):371-8.

Negro, F, and Farina, D (2011).
Decorrelation of cortical inputs and motoneuron output
J Neurophysiol. 106:2688-97.