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g.BCIsys

Applications < Brain-Computer Interface <
BCI examples

g.BCIsys - The complete development and research system

g.tec provides complete MATLAB-based development/research systems including all hard- and software components needed for data acquisition, real-time and off-line data analysis, data set classification and for providing neurofeedback.
The BCI system can be realized with g.MOBIlab+, g.USBamp or g.BSamp. g.MOBIlab+ is available with up to 8 EEG channels and is portable and available with wireless signal transmission. g.USBamp is available for 16-256 EEG channels and transmits the data over USB to the PC or notebook. g.BSamp is availabe for 8, 16 to 80 channels.
The software package High-Speed Online Processing under SIMULINK allows to read the biosignal data directly into SIMULINK. SIMULINK blocks are used to visualize and store the data. The parameter extraction and classification is performed either with standard SIMULINK blocks, with the g.RTanalyze library or with self-written S-functions.
After the EEG data acquisition the data can be analyzed with g.BSanalyze, the EEG and classification toolbox.
Ready-to-use BCI sample applications allow to make state-of-the-art BCI experiments within a few yours.
Highlights
  • complete BCI research system for EEG and ECoG             
  • ready to go paradigms for spelling and cursor control     
  • seamless integration of real-time experiments and off-line analysis        
  • runs either with g.MOBIlab+ or g.USBamp technology   
  • open source paradigms allow to make easily adaptations and to develop own applications          
  • MATLAB/Simulink Rapid Prototyping environment speeds up development times from month to days 
  • BCI technology proven by hundreds of subjects and labs             
Scientific approaches
During the past decade many groups all over the world intensified their work in the field of BCI-research. The different methods published in international scientific journals display the wide range of possible solutions for the problem. The following link provides an overview on some of the most prominent methods and authors working on this hot topic: State of the Art in BCI research
To support your start into the fascinating world of Brain-Computer Interface research see some literature here: Publications
Some of the most commonly used strategies to realize a BCI are:
  • Imagery of movements of different limbs cause changes in oscillatory EEG activity over sensorimotor areas of the central cortex. These changes can be classified by weighting spectral parameters of different frequency bands for different electrode positions.
  • A P300 component is produced if an unlike event occurs. The P300 occurs about 300 ms after the event and has to be detected by specific algorithms. The P300 components are mainly used to create a spelling device for paralyzed patients.
  • Slow shifts of cortical potentials occur when a subject performs an imagery of expecting an event (like waiting for a traffic light turning to green). The resulting DC-shift can be used for biofeedback to improve the training effects and to generate a control signal for communication.
  • Also other mental tasks such as mental arithmetic, mental cube rotation or attention versus relaxation are used to produce characteristic changes of EEG patterns. One attempt has also been not to guide the subjects with any strategy but use specific EEG-biofeedback, so that the user attempts to find his/her own strategy for producing the required changes in the EEG.
  • Another method uses steady-state visually evoked potentials (SSVEP) from flickering light sources. Directing attention to a source with a specific flicker frequency enlarges evoked components in the EEG with the same frequency.
It can be stated that none of all the methods used in BCI research yields perfect results but the performance was significantly improved by new parameter-extraction algorithms and pattern-recognition/classification methods. The usability of a BCI has to be evaluated with respect to the following aspects:
  • accuracy (classification error, hits vs. false, false positives, ...)
  • information transfer (decision speed, bit/min, ...)
  • number of classes (idling vs. activation of 1 class, 2 or more different classes, ...)
  • operation mode (synchronous: predefined decision intervals, asynchronous: free decision time)
  • intended application (spelling device, control of orthotic/prosthetic device, environmental control)
g.BCIsys comes with ready-to-use example BCI-paradigms based on changes in oscillatory EEG activity induced by two different types of motor imageries and paradigms based on the P300 component.
Prerequisites
  • MATLAB
  • Simulink
  • Signal Processing Toolbox (Release 2008b)
  • The Signal Processing Blockset is a useful extension for doing signal analysis in time and frequency domains.
European ICT Prize
The g.tec BCI research platform wins the European ICT Prize 2007.

Additional information

BCI examples

Brain-computer interfaces have been implemented using a variety of different signal processing strategies and with a wide range of different purposes in mind.  This section includes brief overviews of the major types of BCI implementations.

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