Lecture

Motor Neuroprosthetics: Brain-Machine Interfaces

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In course

BIOENG-448: Fundamentals of neuroengineering

Neuroengineering is at the frontier between neuroscience and engineering: understanding how the brain works allows developing engineering applications and therapies of high impact, while the design of new measurement and data analysis techniques contribute ...

Description

This lecture covers the fundamentals of neuroengineering, focusing on brain-machine interfaces (BMI) for motor neuroprosthetics. Topics include recording techniques, invasive and non-invasive BMIs, communication BMIs, EEG signal understanding, event-related potentials, and motor decoding using EEG signals.

Instructors (2)

Solaiman Shokur

2019 - now : Senior Scientist, Translational neuroengineering Laboratory, EPFL(Geneva) Team leader: CHRONOS project, a multi-center European project that aims at developing the first chronically implanted prosthetic hand for transradial amputee patients with bidirectional communication capabilities. NCCR Robotics: Bi-directional control of supernumerary limbs.2014-2019: Research Coordinator, AASDAP (São Paulo, Brazil) Responsible for both the scientific production and the clinical protocol at the AASDAP neurorehabilitation laboratory. 2013-2014: Postdoctoral associate, Walk again project, Insituto Santos Dumond. (Natal, RN, Brazil)Head of the engineering team in charge of the system integration. 2010-2012: Visiting scientist, Nicolelis Lab (Duke University). Development and validation of a virtual-reality based brain-machine interface for rhesus monkeys. 2007 – 2010: Teaching assistant, Laboratory of Robotics Systems (EPFL)

Silvestro Micera

Short biographical sketch Silvestro Micera received the University degree (Laurea) in Electrical Engineering from the University of Pisa, in 1996, and the Ph.D. degree in Biomedical Engineering from the Scuola Superiore Sant'Anna, in 2000. From 2000 to 2009, he has been an Assistant Professor of BioRobotics at the Scuola Superiore Sant'Anna where he is now the Head of the Neural Engineering group. In 2007 he was a Visiting Scientist at the Massachusetts Institute of Technology, Cambridge, USA with a Fulbri

Official source

Ontological neighbourhood

Biology

Neuroscience: Computational neuroscience

Related concepts (32)

Brain–computer interface

A brain–computer interface (BCI), sometimes called a brain–machine interface (BMI) or smartbrain, is a direct communication pathway between the brain's electrical activity and an external device, most commonly a computer or robotic limb. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. They are often conceptualized as a human–machine interface that skips the intermediary component of the physical movement of body parts, although they also raise the possibility of the erasure of the discreteness of brain and machine.

Neural oscillation

Neural oscillations, or brainwaves, are rhythmic or repetitive patterns of neural activity in the central nervous system. Neural tissue can generate oscillatory activity in many ways, driven either by mechanisms within individual neurons or by interactions between neurons. In individual neurons, oscillations can appear either as oscillations in membrane potential or as rhythmic patterns of action potentials, which then produce oscillatory activation of post-synaptic neurons.

Neural engineering

Neural engineering (also known as neuroengineering) is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, or enhance neural systems. Neural engineers are uniquely qualified to solve design problems at the interface of living neural tissue and non-living constructs (Hetling, 2008). The field of neural engineering draws on the fields of computational neuroscience, experimental neuroscience, neurology, electrical engineering and signal processing of living neural tissue, and encompasses elements from robotics, cybernetics, computer engineering, neural tissue engineering, materials science, and nanotechnology.

Single-unit recording

In neuroscience, single-unit recordings (also, single-neuron recordings) provide a method of measuring the electro-physiological responses of a single neuron using a microelectrode system. When a neuron generates an action potential, the signal propagates down the neuron as a current which flows in and out of the cell through excitable membrane regions in the soma and axon. A microelectrode is inserted into the brain, where it can record the rate of change in voltage with respect to time.

Neuronal ensemble

A neuronal ensemble is a population of nervous system cells (or cultured neurons) involved in a particular neural computation. The concept of neuronal ensemble dates back to the work of Charles Sherrington who described the functioning of the CNS as the system of reflex arcs, each composed of interconnected excitatory and inhibitory neurons. In Sherrington's scheme, α-motoneurons are the final common path of a number of neural circuits of different complexity: motoneurons integrate a large number of inputs and send their final output to muscles.

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