Computer Steuern

Review of: Computer Steuern

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Computer Steuern

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Firefox als Standardbrowser in Windows 10 festlegen Tipp. um das Steuern des System in größeren Räumen zu vereinfachen Oder tun Sie zum Pairing einer Fernbedienung mit der Kamera, beispielsweise, weil eine abhanden gekommene Fernbedienung ersetzt werden muss, Folgendes: 1. Halten Sie die Taste an der Kamera gedrückt, bis die LED blinkt 2. Halten Sie die Taste am Bildschirm-. We use cookies, among other things, in order to give you an optimal website experience. They include standard cookies needed for website operation as well as comfort cookies for improved personalised use and benefit cookies that enable us to present you personalised offers after your visit to our website. To use a Pocket Computer, hold it in your hand and press the "use" key (this is a right-click by default). Both advanced and standard versions are available. It is also possible to craft Pocket Computers with wireless modems built in, which allows you to control your machines from anywhere or even open doors for you without having to do anything!

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ComputerCraft Blocks and Items. Advanced Computer. Command Computer. Disk Drive. Advanced Monitor. Wireless Modem. A possible implementation strategy is for each variable to have a thread-local key.

When the variable is accessed, the thread-local key is used to access the thread-local memory location by code generated by the compiler, which knows which variables are dynamic and which are lexical.

If the thread-local key does not exist for the calling thread, then the global location is used.

When a variable is locally bound, the prior value is stored in a hidden location on the stack. The thread-local storage is created under the variable's key, and the new value is stored there.

Further nested overrides of the variable within that thread simply save and restore this thread-local location.

When the initial, outermost override's context terminates, the thread-local key is deleted, exposing the global version of the variable once again to that thread.

With referential transparency the dynamic scope is restricted to the argument stack of the current function only, and coincides with the lexical scope.

In modern languages, macro expansion in a preprocessor is a key example of de facto dynamic scope. The macro language itself only transforms the source code, without resolving names, but since the expansion is done in place, when the names in the expanded text are then resolved notably free variables , they are resolved based on where they are expanded loosely "called" , as if dynamic scope were occurring.

The C preprocessor , used for macro expansion , has de facto dynamic scope, as it does not do name resolution by itself. For example, the macro:.

Properly, the C preprocessor only does lexical analysis , expanding the macro during the tokenization stage, but not parsing into a syntax tree or doing name resolution.

For example, in the following code, the a in the macro is resolved after expansion to the local variable at the expansion site:.

As we have seen, one of the key reasons for scope is that it helps prevent name collisions, by allowing identical names to refer to distinct things, with the restriction that the names must have separate scopes.

Sometimes this restriction is inconvenient; when many different things need to be accessible throughout a program, they generally all need names with global scope, so different techniques are required to avoid name collisions.

To address this, many languages offer mechanisms for organizing global names. The details of these mechanisms, and the terms used, depend on the language; but the general idea is that a group of names can itself be given a name — a prefix — and, when necessary, an entity can be referred to by a qualified name consisting of the name plus the prefix.

Normally such names will have, in a sense, two sets of scopes: a scope usually the global scope in which the qualified name is visible, and one or more narrower scopes in which the unqualified name without the prefix is visible as well.

And normally these groups can themselves be organized into groups; that is, they can be nested. Although many languages support this concept, the details vary greatly.

Other languages have mechanisms, such as packages in Ada and structures in Standard ML , that combine this with the additional purpose of allowing some names to be visible only to other members of their group.

And object-oriented languages often allow classes or singleton objects to fulfill this purpose whether or not they also have a mechanism for which this is the primary purpose.

In C, scope is traditionally known as linkage or visibility , particularly for variables. C is a lexically scoped language with global scope known as external linkage , a form of module scope or file scope known as internal linkage , and local scope within a function ; within a function scopes can further be nested via block scope.

However, standard C does not support nested functions. The lifetime and visibility of a variable are determined by its storage class. There are three types of lifetimes in C: static program execution , automatic block execution, allocated on the stack , and manual allocated on the heap.

Only static and automatic are supported for variables and handled by the compiler, while manually allocated memory must be tracked manually across different variables.

There are three levels of visibility in C: external linkage global , internal linkage roughly file , and block scope which includes functions ; block scopes can be nested, and different levels of internal linkage is possible by use of includes.

Internal linkage in C is visibility at the translation unit level, namely a source file after being processed by the C preprocessor , notably including all relevant includes.

C programs are compiled as separate object files , which are then linked into an executable or library via a linker. Thus name resolution is split across the compiler, which resolves names within a translation unit more loosely, "compilation unit", but this is properly a different concept , and the linker, which resolves names across translation units; see linkage for further discussion.

In C, variables with block scope enter context when they are declared not at the top of the block , go out of context if any non-nested function is called within the block, come back into context when the function returns, and go out of context at the end of the block.

In the case of automatic local variables, they are also allocated on declaration and deallocated at the end of the block, while for static local variables, they are allocated at program initialization and deallocated at program termination.

The following program demonstrates a variable with block scope coming into context partway through the block, then exiting context and in fact being deallocated when the block ends:.

There are other levels of scope in C. Since the name is not used, this is not useful for compilation, but may be useful for documentation.

Label names for GOTO statement have function scope, while case label names for switch statements have block scope the block of the switch.

All the variables that we intend to use in a program must have been declared with its type specifier in an earlier point in the code, like we did in the previous code at the beginning of the body of the function main when we declared that a, b, and result were of type int.

A variable can be either of global or local scope. A global variable is a variable declared in the main body of the source code, outside all functions, while a local variable is one declared within the body of a function or a block.

Modern versions allow nested lexical scope. Go is lexically scoped using blocks. Java is lexically scoped. A Java class can contain three types of variables: [18].

In general, a set of brackets defines a particular scope, but variables at top level within a class can differ in their behavior depending on the modifier keywords used in their definition.

The following table shows the access to members permitted by each modifier. JavaScript has simple scope rules , [20] but variable initialization and name resolution rules can cause problems, and the widespread use of closures for callbacks means the lexical context of a function when defined which is used for name resolution can be very different from the lexical context when it is called which is irrelevant for name resolution.

JavaScript objects have name resolution for properties, but this is a separate topic. JavaScript has lexical scope [21] nested at the function level, with the global context being the outermost context.

This scope is used for both variables and for functions meaning function declarations, as opposed to variables of function type.

Block scope can be produced by wrapping the entire block in a function and then executing it; this is known as the immediately-invoked function expression IIFE pattern.

While JavaScript scope is simple—lexical, function-level—the associated initialization and name resolution rules are a cause of confusion.

Firstly, assignment to a name not in scope defaults to creating a new global variable, not a local one.

Secondly, to create a new local variable one must use the var keyword; the variable is then created at the top of the function, with value undefined and the variable is assigned its value when the assignment expression is reached:.

This is known as variable hoisting [24] —the declaration, but not the initialization, is hoisted to the top of the function. Thirdly, accessing variables before initialization yields undefined , rather than a syntax error.

Fourthly, for function declarations, the declaration and the initialization are both hoisted to the top of the function, unlike for variable initialization.

For example, the following code produces a dialog with output undefined , as the local variable declaration is hoisted, shadowing the global variable, but the initialization is not, so the variable is undefined when used:.

Further, as functions are first-class objects in JavaScript and are frequently assigned as callbacks or returned from functions, when a function is executed, the name resolution depends on where it was originally defined the lexical context of the definition , not the lexical context or execution context where it is called.

The nested scopes of a particular function from most global to most local in JavaScript, particularly of a closure, used as a callback, are sometimes referred to as the scope chain , by analogy with the prototype chain of an object.

Closures can be produced in JavaScript by using nested functions, as functions are first-class objects. For example:.

Closures are frequently used in JavaScript, due to being used for callbacks. Indeed, any hooking of a function in the local context as a callback or returning it from a function creates a closure if there are any unbound variables in the function body with the context of the closure based on the nested scopes of the current lexical context, or "scope chain" ; this may be accidental.

When creating a callback based on parameters, the parameters must be stored in a closure, otherwise it will accidentally create a closure that refers to the variables in the enclosing context, which may change.

Name resolution of properties of JavaScript objects is based on inheritance in the prototype tree—a path to the root in the tree is called a prototype chain —and is separate from name resolution of variables and functions.

Lisp dialects have various rules for scope. The original Lisp used dynamic scope; it was Scheme , inspired by ALGOL , that introduced static lexical scope to the Lisp family.

Maclisp used dynamic scope by default in the interpreter and lexical scope by default in compiled code, though compiled code could access dynamic bindings by use of SPECIAL declarations for particular variables.

Common Lisp adopted lexical scope from Scheme , [29] as did Clojure. ISLISP has lexical scope for ordinary variables. It also has dynamic variables, but they are in all cases explicitly marked; they must be defined by a defdynamic special form, bound by a dynamic-let special form, and accessed by an explicit dynamic special form.

Some other dialects of Lisp, like Emacs Lisp , still use dynamic scope by default. Emacs Lisp now has lexical scope available on a per-buffer basis.

For variables, Python has function scope, module scope, and global scope. Names enter context at the start of a scope function, module, or global scope , and exit context when a non-nested function is called or the scope ends.

If a name is used prior to variable initialization, this raises a runtime exception. If a variable is simply accessed not assigned to , name resolution follows the LEGB Local, Enclosing, Global, Built-in rule which resolves names to the narrowest relevant context.

However, if a variable is assigned to, it defaults to declaring a variable whose scope starts at the start of the level function, module, or global , not at the assignment.

Both these rules can be overridden with a global or nonlocal in Python 3 declaration prior to use, which allows accessing global variables even if there is a masking nonlocal variable, and assigning to global or nonlocal variables.

Note that x is defined before f is called, so no error is raised, even though it is defined after its reference in the definition of f.

Lexically this is a forward reference , which is allowed in Python. Here assignment creates a new local variable, which does not change the value of the global variable:.

The cats were shown eight short movies, and their neuron firings were recorded. Using mathematical filters, the researchers decoded the signals to generate movies of what the cats saw and were able to reconstruct recognizable scenes and moving objects.

Miguel Nicolelis , a professor at Duke University , in Durham, North Carolina , has been a prominent proponent of using multiple electrodes spread over a greater area of the brain to obtain neuronal signals to drive a BCI.

After conducting initial studies in rats during the s, Nicolelis and his colleagues developed BCIs that decoded brain activity in owl monkeys and used the devices to reproduce monkey movements in robotic arms.

Monkeys have advanced reaching and grasping abilities and good hand manipulation skills, making them ideal test subjects for this kind of work.

By , the group succeeded in building a BCI that reproduced owl monkey movements while the monkey operated a joystick or reached for food.

But the monkeys could not see the arm moving and did not receive any feedback, a so-called open-loop BCI.

Later experiments by Nicolelis using rhesus monkeys succeeded in closing the feedback loop and reproduced monkey reaching and grasping movements in a robot arm.

With their deeply cleft and furrowed brains, rhesus monkeys are considered to be better models for human neurophysiology than owl monkeys.

The monkeys were trained to reach and grasp objects on a computer screen by manipulating a joystick while corresponding movements by a robot arm were hidden.

The BCI used velocity predictions to control reaching movements and simultaneously predicted handgripping force.

In O'Doherty and colleagues showed a BCI with sensory feedback with rhesus monkeys. The monkey was brain controlling the position of an avatar arm while receiving sensory feedback through direct intracortical stimulation ICMS in the arm representation area of the sensory cortex.

Other laboratories which have developed BCIs and algorithms that decode neuron signals include those run by John Donoghue at Brown University , Andrew Schwartz at the University of Pittsburgh and Richard Andersen at Caltech.

These researchers have been able to produce working BCIs, even using recorded signals from far fewer neurons than did Nicolelis 15—30 neurons versus 50— neurons.

Donoghue's group reported training rhesus monkeys to use a BCI to track visual targets on a computer screen closed-loop BCI with or without assistance of a joystick.

Andersen's group used recordings of premovement activity from the posterior parietal cortex in their BCI, including signals created when experimental animals anticipated receiving a reward.

In addition to predicting kinematic and kinetic parameters of limb movements, BCIs that predict electromyographic or electrical activity of the muscles of primates are being developed.

Miguel Nicolelis and colleagues demonstrated that the activity of large neural ensembles can predict arm position.

This work made possible creation of BCIs that read arm movement intentions and translate them into movements of artificial actuators. Carmena and colleagues [28] programmed the neural coding in a BCI that allowed a monkey to control reaching and grasping movements by a robotic arm.

Lebedev and colleagues [29] argued that brain networks reorganize to create a new representation of the robotic appendage in addition to the representation of the animal's own limbs.

In , researchers from UCSF published a study where they demonstrated a BCI that had the potential to help patients with speech impairment caused by neurological disorders.

Their BCI used high-density electrocorticography to tap neural activity from a patient's brain and used deep learning methods to synthesize speech.

The biggest impediment to BCI technology at present is the lack of a sensor modality that provides safe, accurate and robust access to brain signals.

It is conceivable or even likely, however, that such a sensor will be developed within the next twenty years. The use of such a sensor should greatly expand the range of communication functions that can be provided using a BCI.

Development and implementation of a BCI system is complex and time-consuming. In response to this problem, Gerwin Schalk has been developing a general-purpose system for BCI research, called BCI A new 'wireless' approach uses light-gated ion channels such as Channelrhodopsin to control the activity of genetically defined subsets of neurons in vivo.

In the context of a simple learning task, illumination of transfected cells in the somatosensory cortex influenced the decision making process of freely moving mice.

The use of BMIs has also led to a deeper understanding of neural networks and the central nervous system. Research has shown that despite the inclination of neuroscientists to believe that neurons have the most effect when working together, single neurons can be conditioned through the use of BMIs to fire at a pattern that allows primates to control motor outputs.

The use of BMIs has led to development of the single neuron insufficiency principle which states that even with a well tuned firing rate single neurons can only carry a narrow amount of information and therefore the highest level of accuracy is achieved by recording firings of the collective ensemble.

Other principles discovered with the use of BMIs include the neuronal multitasking principle, the neuronal mass principle, the neural degeneracy principle, and the plasticity principle.

BCIs are also proposed to be applied by users without disabilities. A user-centered categorization of BCI approaches by Thorsten O.

Zander and Christian Kothe introduces the term passive BCI. In a secondary, implicit control loop the computer system adapts to its user improving its usability in general.

Beyond BCI systems that decode neural activity to drive external effectors, BCI systems may be used to encode signals from the periphery. These sensory BCI devices enable real-time, behaviorally-relevant decisions based upon closed-loop neural stimulation.

The Annual BCI Research Award is awarded in recognition of outstanding and innovative research in the field of Brain-Computer Interfaces.

Each year, a renowned research laboratory is asked to judge the submitted projects. The jury consists of world-leading BCI experts recruited by the awarding laboratory.

Invasive BCI requires surgery to implant electrodes under scalp for communicating brain signals. The main advantage is to provide more accurate reading; however, its downside includes side effects from the surgery.

After the surgery, scar tissues may form which can make brain signals weaker. In addition, according to the research of Abdulkader et al.

Invasive BCI research has targeted repairing damaged sight and providing new functionality for people with paralysis. Invasive BCIs are implanted directly into the grey matter of the brain during neurosurgery.

Because they lie in the grey matter, invasive devices produce the highest quality signals of BCI devices but are prone to scar-tissue build-up, causing the signal to become weaker, or even non-existent, as the body reacts to a foreign object in the brain.

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

One of the first scientists to produce a working brain interface to restore sight was private researcher William Dobelle.

Dobelle's first prototype was implanted into "Jerry", a man blinded in adulthood, in A single-array BCI containing 68 electrodes was implanted onto Jerry's visual cortex and succeeded in producing phosphenes , the sensation of seeing light.

The system included cameras mounted on glasses to send signals to the implant. Initially, the implant allowed Jerry to see shades of grey in a limited field of vision at a low frame-rate.

This also required him to be hooked up to a mainframe computer , but shrinking electronics and faster computers made his artificial eye more portable and now enable him to perform simple tasks unassisted.

In , 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.

The second generation device used a more sophisticated implant enabling better mapping of phosphenes into coherent vision. Phosphenes are spread out across the visual field in what researchers call "the starry-night effect".

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.

Subsequently, when Mr. Naumann and the other patients in the program began having problems with their vision, there was no relief and they eventually lost their "sight" again.

Naumann wrote about his experience with Dobelle's work in Search for Paradise: A Patient's Account of the Artificial Vision Experiment [48] and has returned to his farm in Southeast Ontario, Canada, to resume his normal activities.

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.

Researchers at Emory University in Atlanta , led by Philip Kennedy and Roy Bakay, were first to install a brain implant in a human that produced signals of high enough quality to simulate movement.

Their patient, Johnny Ray — , suffered from ' locked-in syndrome ' after suffering a brain-stem stroke in Ray's implant was installed in and he lived long enough to start working with the implant, eventually learning to control a computer cursor; he died in of a brain aneurysm.

Tetraplegic Matt Nagle became the first person to control an artificial hand using a BCI in as part of the first nine-month human trial of Cyberkinetics 's BrainGate chip-implant.

Implanted in Nagle's right precentral gyrus area of the motor cortex for arm movement , the 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.

More recently, research teams led by the Braingate group at Brown University [52] and a group led by University of Pittsburgh Medical Center , [53] both in collaborations with the United States Department of Veterans Affairs , have demonstrated further success in direct control of robotic prosthetic limbs with many degrees of freedom using direct connections to arrays of neurons in the motor cortex of patients with tetraplegia.

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 scar-tissue in the brain than fully invasive BCIs.

There has been preclinical demonstration of intracortical BCIs from the stroke perilesional cortex. Electrocorticography ECoG measures the electrical activity of the brain taken from beneath the skull in a similar way to non-invasive electroencephalography, but the electrodes are embedded in a thin plastic pad that is placed above the cortex, beneath the dura mater.

In a later trial, the researchers enabled a teenage boy to play Space Invaders using his ECoG implant. Signals can be either subdural or epidural, but are not taken from within the brain parenchyma itself.

It has not been studied extensively until recently due to the limited access of subjects. Currently, the only manner to acquire the signal for study is through the use of patients requiring invasive monitoring for localization and resection of an epileptogenic focus.

ECoG is a very promising intermediate BCI modality because it has higher spatial resolution, better signal-to-noise ratio, wider frequency range, and less training requirements than scalp-recorded EEG, and at the same time has lower technical difficulty, lower clinical risk, and probably superior long-term stability than intracortical single-neuron recording.

This feature profile and recent evidence of the high level of control with minimal training requirements shows potential for real world application for people with motor disabilities.

There have also been experiments in humans using non-invasive neuroimaging technologies as interfaces. The substantial majority of published BCI work involves noninvasive EEG-based BCIs.

Noninvasive EEG-based technologies and interfaces have been used for a much broader variety of applications. Although EEG-based interfaces are easy to wear and do not require surgery, they have relatively poor spatial resolution and cannot effectively use higher-frequency signals because the skull dampens signals, dispersing and blurring the electromagnetic waves created by the neurons.

EEG-based interfaces also require some time and effort prior to each usage session, whereas non-EEG-based ones, as well as invasive ones require no prior-usage training.

Overall, the best BCI for each user depends on numerous factors. In report was given on control of a mobile robot by eye movement using Electrooculography EOG signals.

A mobile robot was driven from a start to a goal point using five EOG commands, interpreted as forward, backward, left, right, and stop.

A article [61] described an entirely new communication device and non-EEG-based human-computer interface, which requires no visual fixation , or ability to move the eyes at all.

The interface is based on covert interest ; directing one's attention to a chosen letter on a virtual keyboard, without the need to move one's eyes to look directly at the letter.

Each letter has its own background circle which micro-oscillates in brightness differently from all of the other letters. The letter selection is based on best fit between unintentional pupil-size oscillation and the background circle's brightness oscillation pattern.

Accuracy is additionally improved by the user's mental rehearsing of the words 'bright' and 'dark' in synchrony with the brightness transitions of the letter's circle.

In and , a BCI using functional near-infrared spectroscopy for "locked-in" patients with amyotrophic lateral sclerosis ALS was able to restore some basic ability of the patients to communicate with other people.

After the BCI challenge was stated by Vidal in , the initial reports on non-invasive approach included control of a cursor in 2D using VEP Vidal , control of a buzzer using CNV Bozinovska et al.

In the early days of BCI research, another substantial barrier to using Electroencephalography EEG as a brain—computer interface was the extensive training required before users can work the technology.

For example, in experiments beginning in the mids, Niels Birbaumer at the University of Tübingen in Germany trained severely paralysed people to self-regulate the slow cortical potentials in their EEG to such an extent that these signals could be used as a binary signal to control a computer cursor.

The experiment saw ten patients trained to move a computer cursor by controlling their brainwaves. The process was slow, requiring more than an hour for patients to write characters with the cursor, while training often took many months.

However, the slow cortical potential approach to BCIs has not been used in several years, since other approaches require little or no training, are faster and more accurate, and work for a greater proportion of users.

Another research parameter is the type of oscillatory activity that is measured. Gert Pfurtscheller founded the BCI Lab and fed his research results on motor imagery in the first online BCI based on oscillatory features and classifiers.

Together with Birbaumer and Jonathan Wolpaw at New York State University they focused on developing technology that would allow users to choose the brain signals they found easiest to operate a BCI, including mu and beta rhythms.

A further parameter is the method of feedback used and this is shown in studies of P signals. Patterns of P waves are generated involuntarily stimulus-feedback when people see something they recognize and may allow BCIs to decode categories of thoughts without training patients first.

By contrast, the biofeedback methods described above require learning to control brainwaves so the resulting brain activity can be detected.

In it was reported research on EEG emulation of digital control circuits for BCI, with example of a CNV flip-flop.

While an EEG based brain-computer interface has been pursued extensively by a number of research labs, recent advancements made by Bin He and his team at the University of Minnesota suggest the potential of an EEG based brain-computer interface to accomplish tasks close to invasive brain-computer interface.

Using advanced functional neuroimaging including BOLD functional MRI and EEG source imaging, Bin He and co-workers identified the co-variation and co-localization of electrophysiological and hemodynamic signals induced by motor imagination.

In addition to a brain-computer interface based on brain waves, as recorded from scalp EEG electrodes, Bin He and co-workers explored a virtual EEG signal-based brain-computer interface by first solving the EEG inverse problem and then used the resulting virtual EEG for brain-computer interface tasks.

Well-controlled studies suggested the merits of such a source analysis based brain-computer interface.

A study found that severely motor-impaired patients could communicate faster and more reliably with non-invasive EEG BCI, than with any muscle-based communication channel.

A study found that the application of evolutionary algorithms could improve EEG mental state classification with a non-invasive Muse device, enabling high quality classification of data acquired by a cheap consumer-grade EEG sensing device.

In the early s Babak Taheri, at University of California, Davis demonstrated the first single and also multichannel dry active electrode arrays using micro-machining.

The single channel dry EEG electrode construction and results were published in The device consisted of four sites of sensors with integrated electronics to reduce noise by impedance matching.

The advantages of such electrodes are: 1 no electrolyte used, 2 no skin preparation, 3 significantly reduced sensor size, and 4 compatibility with EEG monitoring systems.

The active electrode array is an integrated system made of an array of capacitive sensors with local integrated circuitry housed in a package with batteries to power the circuitry.

This level of integration was required to achieve the functional performance obtained by the electrode. The electrode was tested on an electrical test bench and on human subjects in four modalities of EEG activity, namely: 1 spontaneous EEG, 2 sensory event-related potentials, 3 brain stem potentials, and 4 cognitive event-related potentials.

The performance of the dry electrode compared favorably with that of the standard wet electrodes in terms of skin preparation, no gel requirements dry , and higher signal-to-noise ratio.

In researchers at Case Western Reserve University , in Cleveland , Ohio , led by Hunter Peckham, used electrode EEG skullcap to return limited hand movements to quadriplegic Jim Jatich.

As Jatich concentrated on simple but opposite concepts like up and down, his beta-rhythm EEG output was analysed using software to identify patterns in the noise.

A basic pattern was identified and used to control a switch: Above average activity was set to on, below average off.

As well as enabling Jatich to control a computer cursor the signals were also used to drive the nerve controllers embedded in his hands, restoring some movement.

In , the NCTU Brain-Computer-Interface-headband was reported. The researchers who developed this BCI-headband also engineered silicon-based MicroElectro-Mechanical System MEMS dry electrodes designed for application in non-hairy sites of the body.

These electrodes were secured to the DAQ board in the headband with snap-on electrode holders. The signal processing module measured alpha activity and the Bluetooth enabled phone assessed the patients' alertness and capacity for cognitive performance.

When the subject became drowsy, the phone sent arousing feedback to the operator to rouse them. This research was supported by the National Science Council, Taiwan, R.

Army Research Laboratory. In , researchers reported a cellular based BCI with the capability of taking EEG data and converting it into a command to cause the phone to ring.

This research was supported in part by Abraxis Bioscience LLP, the U. Army Research Laboratory, and the Army Research Office.

The electrodes were placed so that they pick up steady state visual evoked potentials SSVEPs. The scientists claim that their studies using a single channel fast Fourier transform FFT and multiple channel system canonical correlation analysis CCA algorithm support the capacity of mobile BCIs.

In , comparative tests were performed on android cell phone, tablet, and computer based BCIs, analyzing the power spectrum density of resultant EEG SSVEPs.

The stated goals of this study, which involved scientists supported in part by the U. Army Research Laboratory, were to "increase the practicability, portability, and ubiquity of an SSVEP-based BCI, for daily use".

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Computer Steuern


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