Neural Machine Interface
Neural Machine Interface - Related Videos
05:21
Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses
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Cited by 9 •
2019
Here we present a protocol which characterizes the sense of agency developed over the control of sensate virtual or robotic prosthetic hands. Psychophysical questionnaires are employed to capture the explicit experience of agency, and time interval estimates (intentional binding) are employed to implicitly measure the sense of agency.
10:51
An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
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Cited by 13 •
2011
We use a closed-loop fly-machine interface to investigate general principles in neuronal control.
07:51
Voltage Biasing, Cyclic Voltammetry, & Electrical Impedance Spectroscopy for Neural Interfaces
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Cited by 11 •
2012
The electrode-tissue interface of neural recording electrodes can be characterized with electrical impedance spectroscopy (EIS) and cyclic voltammetry (CV). Application of voltage biasing changes the electrochemical properties of the electrode-tissue interface and can improve recording capability. Voltage biasing, EIS, CV, and neural recordings are complementary.
11:06
A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
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Cited by 9 •
2016
A novel low-cost human-machine interface for interactive post-stroke balance rehabilitation system is presented in this article. The system integrates off-the-shelf low-cost sensors towards volitionally driven electrotherapy paradigm. The proof-of-concept software interface is demonstrated on healthy volunteers.
11:14
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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Cited by 3 •
2015
This protocol presents a novel methodology for the neural decoding of intent from freely-behaving infants during unscripted social interaction with an actor. Neural activity is acquired using non-invasive high-density active scalp electroencephalography (EEG). Kinematic data is collected with inertial measurement units and supplemented with synchronized video recording.
FAQs
The system first records electrical activity from the brain, spinal cord, or peripheral nerves. Signal-processing steps then identify patterns associated with an intended action, such as movement or communication. A control system translates those patterns into commands for a prosthesis, computer, or other device, allowing neural activity to produce a corresponding external response.
Signal processing is essential because recorded neural activity must be interpreted before it can control equipment. The processing stage distinguishes patterns that correspond to intended actions and converts them into usable commands. In bioengineering, this step links biological signals to device operation and determines how effectively the interface supports control or communication.
A two-way interface does more than read neural activity. It can also deliver sensory feedback through electrical stimulation, returning information from the machine to the user. This bidirectional connection may help integrate a device with the user’s interaction process, extending the system beyond command generation to include feedback during prosthetic or assistive use.
These interfaces obtain electrical signals from different locations within the nervous system: the brain, spinal cord, or peripheral nerves. The source determines where neural activity is recorded within the bioengineered system, while later processing still identifies intended actions and translates them into device commands. This range of signal locations supports different approaches to neural control.
A basic workflow begins by recording electrical signals from a selected part of the nervous system. The system then processes those recordings to identify patterns linked with intended actions and translates the patterns into commands for an external device. If sensory feedback is included, electrical stimulation adds information in the reverse direction, creating a two-way system.
Researchers may use neural machine interfaces when neural activity needs to control communication through an external device. Recorded signals are processed to identify intended actions, then converted into commands for a computer or another communication system. This application is part of bioengineering efforts to support people who need alternative ways to communicate.
For prosthetic control, processed neural signals can be translated into commands that operate an artificial device. In motor rehabilitation, the same general strategy connects nervous-system activity with external equipment during treatment development. These applications make the interface relevant to bioengineering because they use signal processing and device control to address motor limitations.
Neural machine interfaces support research into assistive communication, motor rehabilitation, prosthetic control, and treatments for neurological injury and disease. Their value comes from connecting measurable neural activity with external devices and, in some systems, providing electrical sensory feedback. This enables bioengineers to study how neural signals can be used in therapeutic and assistive settings.