Neural Signals and Signal Processing

This lecture covers topics related to neural signals and signal processing, focusing on muscle force production, EMG signals, neural peripheral signals, and decoding for robotic control. It explores techniques such as synergy extraction, hybrid models, cortical signals, and EMG signal properties. The use of EMG signals for muscle activation, locomotion, and prosthetic control is discussed, along with advanced methods like intramuscular EMG control and targeted muscle reinnervation. The lecture also delves into EMG decoding, muscle synergies, and the correlation between EEG and EMG activities.

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NX-421: Neural signals and signal processing

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

Neural coding

Neural coding (or neural representation) is a neuroscience field concerned with characterising the hypothetical relationship between the stimulus and the individual or ensemble neuronal responses and the relationship among the electrical activity of the neurons in the ensemble. Based on the theory that sensory and other information is represented in the brain by networks of neurons, it is thought that neurons can encode both digital and analog information.

Models of neural computation

Models of neural computation are attempts to elucidate, in an abstract and mathematical fashion, the core principles that underlie information processing in biological nervous systems, or functional components thereof. This article aims to provide an overview of the most definitive models of neuro-biological computation as well as the tools commonly used to construct and analyze them.

Sampling (signal processing)

In signal processing, sampling is the reduction of a continuous-time signal to a discrete-time signal. A common example is the conversion of a sound wave to a sequence of "samples". A sample is a value of the signal at a point in time and/or space; this definition differs from the term's usage in statistics, which refers to a set of such values. A sampler is a subsystem or operation that extracts samples from a continuous signal. A theoretical ideal sampler produces samples equivalent to the instantaneous value of the continuous signal at the desired points.

Downsampling (signal processing)

In digital signal processing, downsampling, compression, and decimation are terms associated with the process of resampling in a multi-rate digital signal processing system. Both downsampling and decimation can be synonymous with compression, or they can describe an entire process of bandwidth reduction (filtering) and sample-rate reduction. When the process is performed on a sequence of samples of a signal or a continuous function, it produces an approximation of the sequence that would have been obtained by sampling the signal at a lower rate (or density, as in the case of a photograph).

Neural network

A neural network can refer to a neural circuit of biological neurons (sometimes also called a biological neural network), a network of artificial neurons or nodes in the case of an artificial neural network. Artificial neural networks are used for solving artificial intelligence (AI) problems; they model connections of biological neurons as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed.

Optimization of Neuroprosthetic Systems NX-421: Neural signals and signal processing

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

Neural Signals and Signal Processing NX-421: Neural signals and signal processing

Explores neural signals, EMG control, muscle synergies, and HD-EMG for prosthetic devices.

Neural Signals and Signal Processing NX-421: Neural signals and signal processing

Explores neural signal processing for brain-computer interfaces, including decoding techniques like Kalman filters and spike sorting.

Neural Signals and Signal Processing

Explores neural signal modeling, nervous system structure, and fiber classification for understanding neural phenomena.

Neural Signals and Signal Processing: Modeling and Simulation NX-421: Neural signals and signal processing

Covers the fundamentals of neural signals and signal processing, focusing on modeling and simulation of neural systems.