Programming Reference:Rapid Development: Difference between revisions
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*The [[Programming Reference:MatlabFilter]] lets Matlab code act upon BCI2000 data. | *The [[Programming Reference:MatlabFilter]] lets Matlab code act upon BCI2000 data. | ||
*There is also a [[Programming Tutorial:Implementing a Matlab-based Filter|tutorial on implementing a Matlab-based filter]] for temporal filtering, and [[Programming Tutorial:Implementing another Matlab-based Filter| another tutorial]] on implementing an adaptive spatial filter using beamforming. | *There is also a [[Programming Tutorial:Implementing a Matlab-based Filter|tutorial on implementing a Matlab-based filter]] for temporal filtering, and [[Programming Tutorial:Implementing another Matlab-based Filter| another tutorial]] on implementing an adaptive spatial filter using beamforming. | ||
==Rebuild== | |||
After changes are made, they must be rebuilt into BCI2000. [[Rebuild BCI2000|Follow these steps to efficiently prototype!]] | |||
[[Category:Contents]] | [[Category:Contents]] | ||
[[Category:Development]] | [[Category:Development]] | ||
Latest revision as of 20:05, 26 June 2025
This page describes elements of BCI2000 intended to simplify a programmer's task when implementing a new BCI2000 filter, or application.
Templates
- The Programming Reference:FeedbackDemo Application may serve as a starting point for a trial-based application.
- The Programming Reference:IIRFilterBase Class filter stub allows you to easily implement IIR filters.
Helper Classes
- Programming Reference:WavePlayer Class plays back audio files.
- Programming Reference:TextToSpeech Class provides synthetic speech.
- Programming Reference:MidiPlayer Class plays back MIDI data.
- Programming Reference:TrialStatistics Class computes accuracies and bit rates.
- Programming Reference:LogFile Class transparently maintains a log file in the current data directory.
Matlab Integration
- The Programming Reference:MatlabFilter lets Matlab code act upon BCI2000 data.
- There is also a tutorial on implementing a Matlab-based filter for temporal filtering, and another tutorial on implementing an adaptive spatial filter using beamforming.
Rebuild
After changes are made, they must be rebuilt into BCI2000. Follow these steps to efficiently prototype!