The document discusses using a neural network to classify EEG signals to control a maze game on an iOS device. It describes taking 20-second EEG samples during baseline and task periods, using FFT to analyze the signals, and training a neural network with PyBrain to classify the samples. The trained network is then used on an iOS app to classify live EEG data and control movements in the maze game by alternating left/right and up/down based on the network's aggregate classification.

1. Your Mind is a Rat
or, using a neural network to classify EEG signals in
a maze game BCI

2. Electricity

3. Fast fourier transform decomposes raw signal
--FFT-->

4. Data samples
Baseline
7044:9689:11807:13458:13174
113325:102718:2949:29319:14806
Task
80259:521265:30119:9249:1319
29409:1510221:12880:2818:490

5. Neural network

6. 1.Sampling
Lightweight iOS app to take 20-second samples, at 1 sample/sec, of baseline and
task brain actions

7. 2. Training
PyBrain (Python-Based Reinforcement Learning, Artificial Intelligence and Neural
Network Library) to create and train a backpropagation neural net for classification

8. 3. BCI / Game
● Create a neural network using training weights with MLPNeuralNet (multilayer
perceptron neural network library for iOS and OSX)
● Classify a number of samples from incoming EEG signal and take aggregate
● Use result to make binary move choice, alternating between left/right and
up/down