Brain - computer interface

Brain -computer interface - the interface that implements the connection between the human brain and the computer. The main idea is that when you think about action and do it, the same part of the brain is activated.In the middle of the XIX century, Emil Du Bois- Reymond showed the relationship between electric current and nerve impulses; in 1875. Richard ketone managed to register the electrical activity of the brain of animals. The psychiatrist Hans Berger in 1924 invented a method to record the electrical activity of the human brain. In 1967, psychiatrist Edmond Dewan published a paper in which he described the experiment where a man was trying to send a message to electroencephalogram by means of dot-and-dash,using brain activity.One of the first practically implemented IMC is considered a virtual keyboard made by Farwell and Donchyn which was created in 1988

Brain computer interface

This report presents an EEG-based brain-computer interface (BCI) in which subjects could select a picture from a set on a computer screen. The application is centred on detecting steady-state visual evoked potentials (SSVEP) in EEG signals recorded on the scalp of the subject. BCI2000 software platform is used in this project as a basis for the whole system. The platform will link its modules and the developed ones needed to achieve the closed-loop BCI system. In this context, a C++ computer application with 16 targets and a MATLAB signal processing module were then implemented using the proposed method. In offline tests for a set of frequencies with differences of amplitude up to 15 dB, detection was achieved. Detection was also achieved in online tests.Ingeniería de Telecomunicació

Brain-computer interface

A brain–computer interface (BCI), sometimes called a direct neural interface or a brain–machine interface (BMI), is a direct communication pathway between the brain and an external device. BCIs are often aimed at assisting, augmenting or repairing human cognitive or sensory-motor functions. The field of BCI has advanced mostly toward neuroprosthetics applications that aim at restoring damaged hearing, sight and movement. Thanks to the remarkable cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels. Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-nineties. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/2253

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

Personalized Brain-Computer Interface Models for Motor Rehabilitation

We propose to fuse two currently separate research lines on novel therapies for stroke rehabilitation: brain-computer interface (BCI) training and transcranial electrical stimulation (TES). Specifically, we show that BCI technology can be used to learn personalized decoding models that relate the global configuration of brain rhythms in individual subjects (as measured by EEG) to their motor performance during 3D reaching movements. We demonstrate that our models capture substantial across-subject heterogeneity, and argue that this heterogeneity is a likely cause of limited effect sizes observed in TES for enhancing motor performance. We conclude by discussing how our personalized models can be used to derive optimal TES parameters, e.g., stimulation site and frequency, for individual patients.Comment: 6 pages, 6 figures, conference submissio