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Depiction of myoelectric control of an ankle exoskeleton. Proportional myoelectric control can be used to (among other purposes) activate robotic lower limb exoskeletons.A proportional myoelectric control system utilizes a microcontroller or computer that inputs electromyography (EMG) signals from sensors on the leg muscle(s) and then activates the corresponding joint actuator(s ...
The spike-triggered averaging (STA) is a tool for characterizing the response properties of a neuron using the spikes emitted in response to a time-varying stimulus. The STA provides an estimate of a neuron's linear receptive field.
EMG measures action potentials, called Motor Unit Action Potentials (MUAPs), created during muscle contraction. A few common uses are determining whether a muscle is active or inactive during movement (onset of activity), assessing the velocity of nerve conduction, and the amount of force generated during movement.
The EEG signals can be captured with opensource hardware such as OpenBCI and the signal can be processed by freely available EEG software such as EEGLAB or the Neurophysiological Biomarker Toolbox. As part of an evaluation for epilepsy surgery, it may be necessary to insert electrodes near the surface of the brain, under the surface of the dura ...
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 ...
Rectification is the translation of the raw EMG signal to a signal with a single polarity, usually positive. The purpose of rectifying the signal is to ensure the signal does not average to zero, due to the raw EMG signal having positive and negative components. Two types of rectification are used: full-wave and half-wave rectification. [27]
Biosignals may also refer to any non-electrical signal that is capable of being monitored from biological beings, such as mechanical signals (e.g. the mechanomyogram or MMG), acoustic signals (e.g. phonetic and non-phonetic utterances, breathing), chemical signals (e.g. pH, oxygenation) and optical signals (e.g. movements).
The random brain activity together with other bio-signals (e.g., EOG, EMG, EKG) and electromagnetic interference (e.g., line noise, fluorescent lamps) constitute the noise contribution to the recorded ERP. This noise obscures the signal of interest, which is the sequence of underlying ERPs under study.