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Learning representations from EEG with deep recurrent-convolutional neural networks
ICLR 2016
Bashivan, Pouya, Irina Rish, Mohammed Yeasin, and Noel Codella
Slides by Alberto BozalReadAI Reading Group
6th March, 2017
Index1. Introduction2. EEG data3. Images from EEG time-series4. Architecture5. Training6. Experiments on an EEG Dataset7. Results
Introduction
● EEG Electroencephalogram - Noninvasive method
● Deep belief network and ConvNets for fMRI and EEG
EEG data
● Measuring charges in electrical voltage
● Seems multi-channel “speech” from the electrodes
EEG data
● Multiples bands meaning○ Gamma○ Beta○ Alpha○ Theta○ Delta
● Oscillatory cortical activity○ Theta(4-7Hz)○ Alpha(8-13Hz)○ Beta(13-30Hz)
Images from EEG time-series
● EEG normal experiments○ Time○ Frequency
● Approach representation EEG○ Adding Space domine
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Images from EEG time-series
● Azimuthal Equidistant Projection - Polar Projection
● Toche Scheme - interpolation
For each frequency band
Architecture
● Single-Frame Approach○ ConvNet - Based VGG○ FFT - All trial duration(3.5 s)
Architecture
● Multi-Frame Approach○ C = 7-layers ConvNet - Based VGG○ max = maxpool○ FC = Fully Connected○ SM = Softmax○ L =LSTM
LSTM Equations:
Training
● Optimizing the cross-entropy loss function● Adam algorithm● Batch size 20● VGG few epoch
○ Large number of parameters in our model■ Many epoch -> overfitting
● Dropout
Experiments on an EEG Dataset
● 5 Chars shown○ Each for 0.5 s
● 1 TEST char at the end
● 2670 samples from 13/15 subjects
Results
● Single-Frame Approach
Results
● Multi-Frame Approach