learning representations from eeg with deep recurrent convolutional neural networks

Learning representations from EEG with deep recurrent-convolutional neural networks ICLR 2016 Bashivan, Pouya, Irina Rish, Mohammed Yeasin, and Noel Codella Slides by Alberto Bozal ReadAI Reading Group 6th March, 2017

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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

https://github.com/imatge-upc/readai/blob/master/README.md

Page 2: Learning representations from EEG with Deep Recurrent Convolutional neural networks

Index1. Introduction2. EEG data3. Images from EEG time-series4. Architecture5. Training6. Experiments on an EEG Dataset7. Results

Page 3: Learning representations from EEG with Deep Recurrent Convolutional neural networks

Introduction

● EEG Electroencephalogram - Noninvasive method

● Deep belief network and ConvNets for fMRI and EEG

Page 4: Learning representations from EEG with Deep Recurrent Convolutional neural networks

EEG data

● Measuring charges in electrical voltage

● Seems multi-channel “speech” from the electrodes

Page 5: Learning representations from EEG with Deep Recurrent Convolutional neural networks

EEG data

● Multiples bands meaning○ Gamma○ Beta○ Alpha○ Theta○ Delta

● Oscillatory cortical activity○ Theta(4-7Hz)○ Alpha(8-13Hz)○ Beta(13-30Hz)

Page 6: Learning representations from EEG with Deep Recurrent Convolutional neural networks

Images from EEG time-series

● EEG normal experiments○ Time○ Frequency

● Approach representation EEG○ Adding Space domine

Page 7: Learning representations from EEG with Deep Recurrent Convolutional neural networks

Images from EEG time-series

● Azimuthal Equidistant Projection - Polar Projection

● Toche Scheme - interpolation

For each frequency band

Page 8: Learning representations from EEG with Deep Recurrent Convolutional neural networks

Architecture

● Single-Frame Approach○ ConvNet - Based VGG○ FFT - All trial duration(3.5 s)

Page 9: Learning representations from EEG with Deep Recurrent Convolutional neural networks

Architecture

● Multi-Frame Approach○ C = 7-layers ConvNet - Based VGG○ max = maxpool○ FC = Fully Connected○ SM = Softmax○ L =LSTM

LSTM Equations:

Page 10: Learning representations from EEG with Deep Recurrent Convolutional neural networks

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

Page 11: Learning representations from EEG with Deep Recurrent Convolutional neural networks

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

Page 12: Learning representations from EEG with Deep Recurrent Convolutional neural networks

Results

● Single-Frame Approach

Page 13: Learning representations from EEG with Deep Recurrent Convolutional neural networks

Results

● Multi-Frame Approach

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