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I have recently taken up a project to integrate a BCI into outside applications to show concentration, stress, etc... After some research OpenBCI and Brainflow look like the best place to start. I was able to create a websocket server that sends raw data from the update() function in board.pde, however I am unsure of how to implement MLModels within the app.
Here is an example of one of the algorithms from the brainflow website.
So far I have tried adding this to the same update() function. It works briefly however the main process crashes after a few seconds, I believe because it is interfering with the main render loop. I'm writing here now in hopes that someone can guide me in the right direction for how to implement this feature. Any help would be much appreciated.
The text was updated successfully, but these errors were encountered:
Is there a thread to brainstorm/track the improvement of these models?
I’m not really a data scientist, but we have our pick of a few classification algorithms and we could further fine tune the “models” to increase the accuracy of “relaxation” and “concentration” metrics.
I guess we could talk about this on BrainFlow Slack or the OpenBCI Forum. Might be time to start a thread. 😎
Hello all,
I have recently taken up a project to integrate a BCI into outside applications to show concentration, stress, etc... After some research OpenBCI and Brainflow look like the best place to start. I was able to create a websocket server that sends raw data from the update() function in board.pde, however I am unsure of how to implement MLModels within the app.
Here is an example of one of the algorithms from the brainflow website.
Pair<double[], double[]> bands = DataFilter.get_avg_band_powers (data, eeg_channels, sampling_rate, true); double[] feature_vector = ArrayUtils.addAll (bands.getLeft (), bands.getRight ()); BrainFlowModelParams model_params = new BrainFlowModelParams (BrainFlowMetrics.CONCENTRATION.get_code (), BrainFlowClassifiers.REGRESSION.get_code ()); MLModel concentration = new MLModel (model_params); concentration.prepare (); System.out.print ("Concentration: " + concentration.predict (feature_vector)); concentration.release ();
So far I have tried adding this to the same update() function. It works briefly however the main process crashes after a few seconds, I believe because it is interfering with the main render loop. I'm writing here now in hopes that someone can guide me in the right direction for how to implement this feature. Any help would be much appreciated.
The text was updated successfully, but these errors were encountered: