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Brain - Computer Interfaces

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Marcela-Nicoleta Craciunescu

on 9 January 2014

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Transcript of Brain - Computer Interfaces

Brain - Computer Interfaces
Introduction
Definition
A brain–computer interface (BCI), often called a mind-machine interface (MMI), or sometimes called a direct neural interface (DNI), synthetic telepathy interface (STI) or a brain–machine interface (BMI), is a direct communication pathway between the brain and an external device. BCIs are often directed at assisting, augmenting, or repairing human cognitive or sensory-motor functions.
Overview
BCI operation depends on the interaction of two adaptive controllers, the user’s brain, which produces the activity measured by the BCI system, and the system itself, which translates that activity into specific commands. Successful BCI operation is essentially a new skill, a skill that consists not of proper muscle control but rather proper control of EEG (or single-unit) activity.

Like any communication and control system, a BCI has an input, an output, and a translation algorithm that converts the former to the latter. BCI input consists of a particular feature (or features) of brain activity and the methodology used to measure that feature.
Application of BCI Technology
BCIs let users mentally interact with a device using hardware to measure brain activity and software to process it in real time.

Hardware
Researchers have used a range of measurement technologies in BCI systems, including:
• electrocorticography (ECoG),
• Intra cortical electrodes (ICE),
• Functional near-infrared spectroscopy (fNIRS),
• Functional magnetic resonance imaging (fMRI),
• Magneto encephalography (MEG), and
• Electroencephalography (EEG)
Present BCI’s can be classified into two groups according to the nature of the signals they use as input.

Some depend on user control of endogenous electrophysiological activity, such as amplitude in a specific frequency band in EEG recorded over a specific cortical area.
Others depend on user control of exogenous electrophysiological activity, that evoked by specific stimuli (e.g., amplitude of the P300 potential produced in response to letter flash).
Vision

In vision science, direct brain implants have been used to treat non- congenital (acquired) blindness.

In 2002, Jens Naumann, also blinded in adulthood, became the first in a series of 16 paying patients to receive Dobelle’s second generation implant, marking one of the earliest commercial uses of BCIs. Immediately after his implant, Jens was able to use his imperfectly restored vision to drive an automobile slowly around the parking area of the research institute.
Conclusion
A brain–computer interface is a communication and control channel that does not depend on the brain’s normal output path-ways of peripheral nerves and muscles. At present, the main impetus to BCI research and development is the expectation that BCI technology will be valuable for those whose severe neuromuscular disabilities prevent them from using conventional augmentative communication methods.
Marcela - Nicoleta Craciunescu
History

The history of brain–computer interfaces (BCIs) starts with Hans Berger's discovery of the electrical activity of the human brain and the development of electroencephalography (EEG). In 1924 Berger was the first to record human brain activity by means of EEG. Berger was able to identify oscillatory activity in the brain by analyzing EEG traces. One wave he identified was the alpha wave (8–13 Hz), also known as Berger's wavThee.
In the past few years, BCI applications have broken out of laboratories and hospitals to include nonmedical appli-cations, such as gaming. Commercial products for home use are appearing, such as Uncle Milton’s The Force Trainer and Mattel’s Mindflex.
Evaluations in terms of information transfer rate and in terms of usefulness in specific applications are both important. Appropriate user populations must be identified, and BCI applications must be configured to meet their most important needs. The assessment of needs should focus on the actual desires of individual users rather than on preconceived notions about what these users ought to want.
Evaluation of specific applications ultimately rests on the extent to which people actually use them in their daily lives.

With further increasesin speed, accuracy, and range of applications, BCI technology could become applicable to larger populations and could thereby engage the interest and resources of private industry.
Software
Open source software is available to process measured brain signals in real time as part of designing, implementing, and assessing BCI systems.
One example would be :
OpenViBE (http://openvibe.inria.fr) is open source software for acquiring, filtering, processing, classifying, and visualizing brain signals in real time.

Software differs in the variety of signal-processing algo-rithms offered and ways to visualize brain activity.
Technological challenges
The typical user will want to operate the BCI without help or extensive training. Generally, users will have high expectations about the system’s usability and hardware’s wear ability. They will not want to wash their hair after every experience or to endure an uncomfortable cap. Rather, users must be able to rapidly don the EEG cap and set up the equipment quickly and intuitively. The system must also be hygienic for multiple users and require minimal maintenance.
Movement
BCIs focusing on motor neuroprosthetics aim to either restore movement in individuals with paralysis or provide devices to assist them, such as interfaces with computers or robot arms.

Tetraplegic Matt Nagle became the first person to control an artificial hand using a BCI in 2005 as part of the first nine-month human trial of Cyberkinetics’s Brain Gate chip-implant. Implanted in Nagle’s right precentral gyrus (area of the motor cortex for arm movement), the 96-electrode BrainGate implant allowed Nagle to control a robotic arm by thinking about moving his hand as well as a computer cursor, lights and TV.
Partially invasive BCIs
Partially invasive BCI devices are implanted inside the skull but rest outside the brain rather than within the grey matter. They produce better resolution signals than non-invasive BCIs where the bone tissue of the cranium deflects and deforms signals and have a lower risk of forming scartissue in the brain than fully invasive BCIs.

In a later trial, the researchers enabled a teenage boy to play Space Invaders using his ECoG implant. This research indicates that control is rapid, requires minimal training, and may be an ideal tradeoff with regards to signal fidelity and level of invasiveness.
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