Neurofeedback: Training in both directions

For now, the training of neural networks mostly happens on the AI side. The human makes arm gestures and presses keys on the keyboard. This provides the input (electrode and IMU signals) as well as the output/labels (keyboard actions) to the artificial neural network, which then learns the correlation between the two through supervised gradient descent.

Now I'm looking into how to train both the neural network of the AI as well as the nervous system of the user through neurofeedback, that is, by making the user more aware of their neural signals, which in turn allows them to fine-tune these.

My hope is that this will make up for the low quality of information that's available to the AI, due to noise, attenuation, and the low number/quality of electrodes. The user neither knows what signals the electrodes can access, nor how to willingly produce movements that create these signals. Some gestures work well, while others can't be detected at all, so the best bet is to use forceful gestures with maximal muscle activation. But if there was some sort of feedback to the user, like a visualization of the data that the neural network is extracting, the user could focus on the movements that work, and gradually lower the intensity, perhaps to the point where no actual movement is required anymore.

Of course there is already some feedback about the signals: The PsyLink UI shows the amplitude of each electrode in a rolling graph, and the GNURadio application shows detailed plots of the raw signals, both of which already help determining which movements will work for gestures and which will not. But the AI can of course combine, cross-correlate, filter, convolve and deconvolve the signals, which enables it to extract information that a human won't see in the raw signal data.

Ultimately, the goal is that the user learns to, on demand, fire off just enough neurons that PsyLink can pick up the signal and trigger the intended key press without any visible movement of the arm.


As described above, simply presenting the user with the raw electrode data is insufficient. A machine-learning approach will likely be optimal here, to overcome the preconceptions of a top-down designer. Since we already have an artificial neural network, why not use that one to generate the visualizations too?

In my current version of this idea:

  1. The user needs to invent some arbitrary gesture that should correspond to the action "Press key 'A'".
  2. The user is repeatedly asked to perform the gesture by the UI
    • At random intervals
    • For random durations
    • With 2-3 seconds of heads-up warning to account for reaction time
    • In between the gestures, the user should perform random other activities, but never do the gesture without being asked by the UI
  3. The AI is trained on the fly with
    • Electrode signals as input
    • A binary label of "Key 'A' pressed" vs. "Key 'A' not pressed" as output
    • Each data point is added randomly (80:20) to the training or validation dataset
    • After X seconds of collecting data, the AI is trained for Y epochs
  4. Every Z milliseconds, the AI is asked to predict the output from the current input, and the neural activations of the last non-output layer of the NN are presented to the user visually, along with the predicted output.
    • The visualization could be a heatmap or a scatterplot, for example
    • The visualization should cover a large dynamic range (both small changes and large changes to the values should be easily visible)
  5. Using the feedback, the user can tweak their gesture as desired, to e.g.
    • Minimize the movement required to trigger the key
    • Maximize the reliability with which the key press is predicted
  6. Over time, old data is dropped from the NN training to refine the visualization and to keep the training time short.

Once the user is ready, they can add a second action like "Press key 'B'" and so on.