Learning representations from EEG with Deep Recurrent Convolutional neural networks
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Transcript of Learning representations from EEG with Deep Recurrent Convolutional neural networks
![Page 1: Learning representations from EEG with Deep Recurrent Convolutional neural networks](https://reader033.fdocuments.in/reader033/viewer/2022042515/58e4de381a28abf5048b661d/html5/thumbnails/1.jpg)
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
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Index1. Introduction2. EEG data3. Images from EEG time-series4. Architecture5. Training6. Experiments on an EEG Dataset7. Results
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Introduction
● EEG Electroencephalogram - Noninvasive method
● Deep belief network and ConvNets for fMRI and EEG
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EEG data
● Measuring charges in electrical voltage
● Seems multi-channel “speech” from the electrodes
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EEG data
● Multiples bands meaning○ Gamma○ Beta○ Alpha○ Theta○ Delta
● Oscillatory cortical activity○ Theta(4-7Hz)○ Alpha(8-13Hz)○ Beta(13-30Hz)
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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
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Architecture
● Single-Frame Approach○ ConvNet - Based VGG○ FFT - All trial duration(3.5 s)
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Architecture
● Multi-Frame Approach○ C = 7-layers ConvNet - Based VGG○ max = maxpool○ FC = Fully Connected○ SM = Softmax○ L =LSTM
LSTM Equations:
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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
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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
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Results
● Single-Frame Approach
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Results
● Multi-Frame Approach