Lecture

Neural Signals and Signal Processing

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In course

NX-421: Neural signals and signal processing

Understanding, processing, and analysis of signals and images obtained from the central and peripheral nervous system ...

Description

This lecture covers topics related to neural signals and signal processing, including cortical signals, intracortical signals, EMG control for robotic systems, muscle synergies, spinal maps, HD-EMG, targeted muscle reinnervation, and EMG-based control of prosthetic devices. It explores the extraction of meaningful information from different signals, the advantages and disadvantages of various signal processing techniques, and the application of pattern recognition in controlling prosthetic limbs. The content delves into the decoding of neural drive for controlling prosthetic hands, the use of targeted muscle reinnervation for prosthetic control, and the benefits of parallel dual-site control in improving performance. The lecture also discusses the potential of EMG signals in controlling robotic systems and the importance of accurate decoding for brain-computer interfaces.

Instructors (2)

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

Dimitri Nestor Alice Van De Ville

Expertise Brain and spinal cord imaging, signal and image processing, network science, dynamic systems Teaching & PhD PhD Students Alexandre Cionca, Chun Hei Michael Chan, Flavia Petruso, Bassam El Rawas, Nikolaos Molochidis, Christophe Pascal Jérémie Chênes, Fabrice Jean Guibert, Alessio Siviglia, Ekansh Sareen, Beatrice Lugli Past EPFL PhD Students Djano Kandaswamy, Fikret Isik Karahanoglu, Nora Leonardi, Zafer Dogan, Jeffrey Adam Kasten, Naghmeh Ghazaleh, Rotem Roza Kopel, Daniela Maria Zöller, Thomas

Official source

Related concepts (32)

Physical neural network

A physical neural network is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse or a higher-order (dendritic) neuron model. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches. More generally the term is applicable to other artificial neural networks in which a memristor or other electrically adjustable resistance material is used to emulate a neural synapse.

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.

Biomechatronics

Bio-mechatronics is an applied interdisciplinary science that aims to integrate biology and mechatronics (electrical, electronics, and mechanical engineering). It also encompasses the fields of robotics and neuroscience. Biomechatronic devices cover a wide range of applications, from developing prosthetic limbs to engineering solutions concerning respiration, vision, and the cardiovascular system. Bio-mechatronics mimics how the human body works. For example, four different steps must occur to lift the foot to walk.

Neuroprosthetics

Neuroprosthetics (also called neural prosthetics) is a discipline related to neuroscience and biomedical engineering concerned with developing neural prostheses. They are sometimes contrasted with a brain–computer interface, which connects the brain to a computer rather than a device meant to replace missing biological functionality. Neural prostheses are a series of devices that can substitute a motor, sensory or cognitive modality that might have been damaged as a result of an injury or a disease.

Digital signal processing

Digital signal processing (DSP) is the use of digital processing, such as by computers or more specialized digital signal processors, to perform a wide variety of signal processing operations. The digital signals processed in this manner are a sequence of numbers that represent samples of a continuous variable in a domain such as time, space, or frequency. In digital electronics, a digital signal is represented as a pulse train, which is typically generated by the switching of a transistor.

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Lecture week 2 (September 17)

Optimization of Neuroprosthetic Systems

Explores the optimization of neuroprosthetic systems, including sensory feedback restoration and neural stimulation strategies.

Neuroprosthetics: Enhancing Sensory Feedback in Limb Control

Covers neuroprosthetics, focusing on enhancing sensory feedback for improved limb control.