Ssvep Bci
Ssvep Bci - Related Videos
11:01
SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
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Cited by 12 •
2015
The overall goal of this method is to establish an SSVEP-based experimental procedure by integrating multiple software programs to enable the study of brain-robot interaction with humanoid robots, which is prospective in assisting the sick and elderly as well as performing unsanitary or dangerous jobs.
12:07
Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000
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Cited by 26 •
2009
In this video, we demonstrate the steps required to run a brain-computer interface experiment, including setting up the EEG cap, calibrating the system, and training the user to move a cursor in two dimensions using imagined movements.
13:32
Recording Human Electrocorticographic (ECoG) Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
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Cited by 134 •
2012
We present a method for collecting electrocorticographic signals for research purposes from humans who are undergoing invasive epilepsy monitoring. We show how to use the BCI2000 software platform for data collection, signal processing and stimulus presentation. Specifically, we demonstrate SIGFRIED, a BCI2000-based tool for real-time functional brain mapping.
10:49
Development of automated imaging and analysis for zebrafish chemical screens.
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Cited by 48 •
2010
We report the development of a system for automated imaging and analysis of zebrafish transgenic embryos in multiwell plates. This demonstrates the ability to measure dose dependent effects of a small molecule, BCI, on Fibroblast Growth Factor reporter gene expression and provide technology for establishing high-throughput zebrafish chemical screens.
09:16
A Simple and Efficient Approach to Construct Mutant Vaccinia Virus Vectors
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Cited by 13 •
2016
Vaccinia virus (VV) has been widely used in biomedical research and the improvement of human health. This article describes a simple, highly efficient method to edit the VV genome using a CRISPR-Cas9 system.
FAQs
The visual response is not limited to the frequency of the flickering target. Neural activity synchronized with periodic stimulation also appears at harmonics, which are integer-related frequency components. Examining both the fundamental frequency and these harmonics gives signal-processing or classification methods more response information, helping them associate recorded EEG activity with the user’s focused target.
When a user focuses on one flickering target among available options, visual-area activity becomes synchronized with that target’s periodic stimulation. Each target can therefore produce a response pattern linked to its stimulation frequency and harmonics. EEG captures these patterns, allowing the system to distinguish the selected option and convert attention into a command.
EEG provides the recorded neural signal from which an SSVEP BCI extracts stimulus-related activity. Signal-processing methods examine the response components, while classification methods determine which target best matches the measured pattern. This separation between recording, processing, and classification enables the system to infer a selection without relying on muscular movement.
A typical operation begins with the user viewing periodic visual targets and focusing on the intended one. EEG then records the resulting synchronized neural activity. Signal-processing methods analyze the response frequency components and harmonics, after which a classifier identifies the corresponding target. The recognized target is finally mapped to a computer command, such as a cursor or device action.
Engineering applications include hands-free cursor control, device control, and communication interfaces. These systems provide a way to design non-muscular human-computer interaction, particularly when conventional physical input is unsuitable or unavailable. Their use in assistive technology development also allows engineers to investigate interfaces that translate visually guided neural responses into practical computer commands.
SSVEP BCIs offer an engineering framework for controlling interfaces through brain responses rather than muscle activity. This supports the development of assistive communication systems, cursor controls, and device interfaces that can be operated hands-free. The approach connects EEG measurement, neural signal classification, and interface design, making it relevant to technologies intended to expand computer access.