mindaffectBCI 0.9.1

The MindAffect BCI python SDK

mindaffectBCI

This repository contains the python SDK code for the Brain Computer Interface (BCI) developed by the company Mindaffect.

File Structure

This repository is organized roughly as follows:

• mindaffectBCI - contains the python package containing the mindaffectBCI SDK. Important modules within this package are:
  • noisetag.py - This module contains the main API for developing User Interfaces with BCI control
  • utopiaController.py - This module contains the application level APIs for interacting with the MindAffect Decoder.
  • utopiaclient.py - This module contains the low-level networking functions for communicating with the MindAffect Decoder - which is normally a separate computer running the eeg analysis software.
  • stimseq.py -- This module contains the low-level functions for loading and codebooks - which define how the presented stimuli will look.
• codebooks - Contains the most common noisetagging codebooks as text files
• examples - contains python based examples for Presentation and Output parts of the BCI. Important sub-directories
  • output - Example output modules. An output module translates BCI based selections into actions.
  • presentation - Example presentation modules. A presentation module, presents the BCI stimulus to the user, and is normally the main UI.

Installing mindaffectBCI

That's easy::

pip3 install mindaffectBCI

Simple output module

An output module listens for selections from the mindaffect decoder and acts on them to create some output. Here we show how to make a simple output module which print's "Hello World" when the presentation 'button' with ID=1 is selected.

.. code:: python

Import the utopia2output module

from mindaffectBCI.utopia2output import Utopia2Output

Now we can create an utopia2output object and connect it to a running mindaffect BCI decoder.

.. code:: python

u2o=Utopia2Output() u2o.connect()

(Note: For this to succeed you must have a real or simulated mindaffectBCI decoder running somewhere on your network.)

Now we define a function to print hello-world

.. code:: python

def helloworld(): print("hello world")

And connect it so it is run when the object with ID=1 is selected.

.. code:: python

set the objectID2Action dictionary to use our helloworld function if 1 is selected

u2o.objectID2Action={ 1:helloworld }

Finally, run the main loop

.. code:: python

u2o.run()

For more complex output examples, and examples for controlling a lego boost robot, or a philips Hue controllable light, look in the examples\output directory.

Simple presention module

Presentation is inherently more complex that output as we must display the correct stimuli to the user with precise timing and communicate this timing information to the mindaffect decoder. Further, for the BCI operation we need to operation in (at least),

• calibration mode where we cue the user where to attend to obtain correctly labelled brain data to train the machine learning algorithms in the decoder and
• prediction mode where the user actually uses the BCI to make selections.

The noisetag module mindaffectBCI SDK provides a number of tools to hide this complexity from the application developers. Using the most extreeem of these all the application developer has to do is provide a function to draw the display as instructed by the noisetag module.

To use this. Import the module and creat the noisetag object.

.. code:: python

from mindaffectBCI.noisetag import Noisetag nt = Noisetag()

Note: Creation of the Noisetag object will also implictly create a connection to any running mindaffectBCI decoder - so you should have one running somewhere on your network.

Write a function to draw the screen. Here we will use the python gaming librar pyglet to draw 2 squares on the screen, with the given colors.

.. code:: python

import pyglet

make a default window, with fixed size for simplicty

window=pyglet.window.Window(width=640,height=480)

define a simple 2-squares drawing function

def draw_squares(col1,col2): # draw square 1: @100,190 , width=100, height=100 x=100; y=190; w=100; h=100; pyglet.graphics.draw(4,pyglet.gl.GL_QUADS, ('v2f',(x,y,x+w,y,x+w,y+h,x,y+h)), ('c3f',(col1)*4)) # draw square 2: @440,100 x=640-100-100 pyglet.graphics.draw(4,pyglet.gl.GL_QUADS, ('v2f',(x,y,x+w,y,x+w,y+h,x,y+h)), ('c3f',(col2)*4))

Now we write a function which,

1. asks the noisetag framework how the selectable squares should look,
2. updates the noisetag framework with information about how the display was updated.

.. code:: python

dictionary mapping from stimulus-state to colors

state2color={0:(.2,.2,.2), # off=grey 1:(1,1,1), # on=white 2:(0,1,0), # cue=green 3:(0,0,1)} # feedback=blue def draw(dt): # send info on the previous stimulus state. # N.B. we do it here as draw is called as soon as the vsync happens nt.sendStimulusState() # update and get the new stimulus state to display # N.B. update raises StopIteration when noisetag sequence has finished try : nt.updateStimulusState() stimulus_state,target_state,objIDs,sendEvents=nt.getStimulusState() except StopIteration : pyglet.app.exit() # terminate app when noisetag is done return # draw the display with the instructed colors # draw the display with the instructed colors if stimulus_state : draw_squares(state2color[stimulus_state[0]], state2color[stimulus_state[1]])

Finally, we tell the noisetag module to run a complete BCI 'experiment' with calibration and feedback mode, and start the pyglet main loop.

.. code:: python

tell the noisetag framework to run a full : calibrate->prediction sequence

nt.startExpt([1,2],nCal=10,nPred=10)

run the pyglet main loop

pyglet.clock.schedule(draw) pyglet.app.run()

This will then run a full BCI with 10 cued calibration trials, and uncued prediction trials. During the calibration trials a square turning green shows this is the cued direction. During the prediction phase a square turning blue shows the selection by the BCI.

For more complex presentation examples, including a full 6x6 character typing keyboard, and a color-wheel for controlling a philips Hue light see the examples/presentation directory.