The Brain-Computer-Interface system allows a user to coordinate with their environment solely by the power of his/her thoughts — learn more about it here.
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A Brain-Computer-Interface (BCI) is described as a system that allows the user to coordinate only through thinking to his environment. The GRAZ-BCI is focused on detecting changes in the electroencephalogram (EEG), which are modified by the use of motor imagery (MI). Without their execution, MI might be described as a motor mental rehearsal task. Motor activities may be imagined as gripping a training ball, paddling water with both feet, or playing an instrument. Such thoughts may be used to produce a control signal for any system like a neuroprosthesis or a contact tool.
How a BCI Works
The BCI system functions in 4 phases:
Using electrodes placed on an EEG mask, brain waves are captured on the user's scalp. It occurs non-invasively; there is little damage caused to the user.
The calculated signals are shallow, so they are strongly affected by eye-blinks. Specific algorithms are then implemented to boost the signal strength to expose brain patterns.
Preprocessed signals are examined using advanced machine learning techniques to classify the specified imaginations' brain patterns.
Command and Feedback
Each behavior will elicit an adequate acknowledgment. You see the liquid through your fingertips as you grip a bottle, get a sense of its weight, and know its temperature. This "feedback" allows us to conduct our everyday activities without ever completely understanding them-such as changing the gripping force we place on the glass when we 'notice' it is more substantial than we thought. Those sensations can no longer be felt for a person with no feeling in the hand. Therefore substitutes-which are called feedback must be implemented.
In the Future
BCI will become one of the control techniques for the users inside the Moregrasp project. BCI preparation must take place directly from the outset, an integral aspect of each user's training. We aim to harness the latest evolutionary measures to make training much more compelling than monotony repetitions of different events-and; we firmly believe that this effort will also have positive impacts on the users.
BCIs' success depends on improvement in three crucial areas: secure, reliable, and stable signal acquisition hardware development; BCI verification and distribution; and demonstrated reliability and utility for several specific consumer populations.
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