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 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