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BenWolfaardt / Deep_Learning-EEG_Data

Deep Learning applied to EEG signals of semantic categorization

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thesis

DeepLearning-EEGData

Deep Learning applied to EEG signals of semantic categorization.

The code for preprocessing as well as the Deep Learning models can be found here.

Links

TODO

  • Update 1, 2 to class and having types

  • Implement cli

  • Config file for all (YAMl)

    • pass variables around making use of self in self.init
      • i.e. not having to parse class and triggers around everywhere
      • distinguish between pickles output
        • maybe data split
        • keras or TF2
          • test if there is actually a difference

  • Merge all into one file

    • Leave the original model out originally for first organise and commit
    • add it in afterwards and test it
      • Potentially keep seperate if library issues

  • Lint

    • Setup black , flake, etc...

  • Implemtne argparse from Makefile

  • Update output locations

  • for loops

  • Improve try catches

  • For loop

    • Pickles
      • Default folder location out put
    • Create model
      • best model save (run x times and then only save new model if better than previous)
        • Test call back for model saved
        • Give proper name
      • Save to specific folder
    • Confussion matrixes
      • Save all plots named nicely to folder
      • Create CM object with results and save as txt to easily send Dawie

  • Requirements update

  • Scripts update

  • Makefile

    • per OS
      • pass in the arguments when calling the python file
        • python -m mdoel.py arg1 arg2 arg3
      • Automatically copy reevant requirements into default file
    • Add tensorboard
      • dev normal
    • Run all the steps (for the py files 1 - 4) and have them dependant on the previous
  • TODO Need to actually test this and add in -y flags
# The below is if oyu experience some problems with grpc
# conda install -n env/ grpc
GRPC_PYTHON_BUILD_SYSTEM_OPENSSL=1 GRPC_PYTHON_BUILD_SYSTEM_ZLIB=1 python -m pip uninstall grpcio         
GRPC_PYTHON_BUILD_SYSTEM_OPENSSL=1 GRPC_PYTHON_BUILD_SYSTEM_ZLIB=1 python -m pip install --no-cache-dir grpcio==1.47.0

# v2.x + needed for Mac M1
python -m pip uninstall tensorboard
python -m pip install --no-cache-dir tensorboard==2.9.1
  • Below from the legacy setup.sh file
#!/bin/bash

# Chose OS from stdin
# Copy the contents of the relevant requirements.<OS>.in into requirremtne.in
# Generate the requirements.txt
# Install packages

# conda create --prefix ./env python=3.9 -y

# eval "$(conda shell.bash hook)"
# source conda activate base

source /Users/james.wolfaardt/miniforge3/bin/activate ./env
python --version
pip list

M1 Pro config

Reminders

GitHub

Setup

Configuration

  • Save libraries and dependancies to requirements.txt 
        pip freeze > requirements.txt
  • Conda create environment from requirements.txt 
        conda create --name <environment_name> --file requirements.txt
  • Conda install requirements.txt into current environment 
        conda install --file requirements.txt

conda activate env/

The below are notes I made whilst refactoring the project

Code Steps

  1. SET to CSV

    • William - Ben CSV
    • William - SET to CSV
      • Not sure why this exists
      • Much fewer lines of code

  2. Create Pickles

    • William - LoadingDataV7
      • Not sure what format of data it reads data in

  3. Train Model

    • 0 - William thank you.ipynb / <semantic_category>.ipynb
    • Ben - TrainingModel.py
      • Takes the pickles and trains the model

  4. Create Confusion Matrix

    • Ben - Model.py
      • Loads the model
      • Then generates the Confusion Matrix

Plan of action

  1. Plug in HDD
    • Copy Pickles over

Everything being run should be reading and saving info on new HDD
At least initially to determine file size and to see if we can run things on the NVMe (much faster) - View Disk usage

  1. Run <semantic_category>.ipynb

    • Time how long it takes
    • Should do the confusion Matrix as well
      • If not do this next

      Confirm Trigers relationships

  2. Run Set to CSV

  3. Create Pickles

  4. Refactor code

    • Model code
      • Siobhan insperation
      • f format for strings
      • Variables everywhere
      • All same file
        • Create Model
        • Train
        • Test
        • Load model
        • Confusion Matrix
      • Test
    • Set to CSV
      • Test
      • Add in if mian
    • Create Pickles
      • Test Add in if mian
    • Potentially combine more things together
      • Don't want things too big
        • But keep def functions clean
    • In CNN file
      • Just checking if the model that we are loading is the same as that I was training
        just a double check :)
        model.summary()
        model.get_weights()
    • Find comanality in the different files
      • Consider creating a yaml config file
        • Example for class split
          • Purple, Ball, Pen, etc..

  5. Consider creating a basic CLI

    • FxF for insperation

  6. Setup and run all on Linux to train all code

Triggers

Type Example Question Screen Word
Colours Purple T1 T2 T7
Colours Red T4 T3 T5
Type Example Digit Word Roman Numeral
Numbers Seven T14 T11 T9
Numbers Two T15 T13 T10
Type Example Phrase Picture Word
Objects Ball T17 T18 T16
Objects Pen T8 T6 T12

Pickles

Person Number
Tristan 20
Harry 21
Keanu 22
Natasha 23

7 1.22.26

(skripsie) E:\Skripsie\Code\Original>python 7.py
Using TensorFlow backend.
Training on fold: 1/3
Training new iteration on 240 training samples, 122 validation samples, this may be a while...
Train on 240 samples, validate on 122 samples
Epoch 1/8
2022-07-08 18:49:25.808559: I C:\tf_jenkins\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
240/240 [==============================] - 198s 826ms/step - loss: 0.7343 - acc: 0.6792 - val_loss: 0.0179 - val_acc: 1.0000
Epoch 2/8
240/240 [==============================] - 198s 825ms/step - loss: 0.0226 - acc: 0.9917 - val_loss: 3.1375e-06 - val_acc: 1.0000     
Epoch 3/8
240/240 [==============================] - 197s 821ms/step - loss: 0.0393 - acc: 0.9875 - val_loss: 3.7791e-05 - val_acc: 1.0000     
Epoch 4/8
240/240 [==============================] - 200s 833ms/step - loss: 0.0106 - acc: 1.0000 - val_loss: 8.9043e-06 - val_acc: 1.0000     
Epoch 5/8
240/240 [==============================] - 195s 814ms/step - loss: 0.0192 - acc: 0.9958 - val_loss: 3.3208e-06 - val_acc: 1.0000
Epoch 6/8
240/240 [==============================] - 197s 819ms/step - loss: 0.0254 - acc: 0.9917 - val_loss: 5.0627e-04 - val_acc: 1.0000
Epoch 7/8
240/240 [==============================] - 196s 818ms/step - loss: 0.0056 - acc: 1.0000 - val_loss: 2.4672e-06 - val_acc: 1.0000
Epoch 8/8
240/240 [==============================] - 196s 816ms/step - loss: 5.7771e-04 - acc: 1.0000 - val_loss: 2.8825e-07 - val_acc: 1.0000
Last training accuracy: 1.0, last validation accuracy: 1.0
Training on fold: 2/3
Training new iteration on 241 training samples, 121 validation samples, this may be a while...
Train on 241 samples, validate on 121 samples
Epoch 1/8
241/241 [==============================] - 196s 815ms/step - loss: 0.6824 - acc: 0.7261 - val_loss: 0.0936 - val_acc: 1.0000
Epoch 2/8
241/241 [==============================] - 196s 814ms/step - loss: 0.2296 - acc: 0.9046 - val_loss: 0.0029 - val_acc: 1.0000
Epoch 3/8
241/241 [==============================] - 196s 813ms/step - loss: 0.1310 - acc: 0.9627 - val_loss: 0.0201 - val_acc: 1.0000
Epoch 4/8
241/241 [==============================] - 196s 812ms/step - loss: 0.1083 - acc: 0.9544 - val_loss: 1.1573e-05 - val_acc: 1.0000
Epoch 5/8
241/241 [==============================] - 196s 814ms/step - loss: 0.0314 - acc: 0.9876 - val_loss: 5.8589e-04 - val_acc: 1.0000
Epoch 6/8
241/241 [==============================] - 196s 812ms/step - loss: 0.0324 - acc: 0.9917 - val_loss: 1.7274e-04 - val_acc: 1.0000
Epoch 7/8
241/241 [==============================] - 197s 818ms/step - loss: 0.0863 - acc: 0.9751 - val_loss: 0.0021 - val_acc: 1.0000
Epoch 8/8
241/241 [==============================] - 197s 816ms/step - loss: 0.0361 - acc: 0.9959 - val_loss: 1.1198e-04 - val_acc: 1.0000
Last training accuracy: 0.9958506214173503, last validation accuracy: 1.0
Training on fold: 3/3
Training new iteration on 243 training samples, 119 validation samples, this may be a while...
Train on 243 samples, validate on 119 samples
Epoch 1/8
243/243 [==============================] - 197s 812ms/step - loss: 0.5746 - acc: 0.7613 - val_loss: 0.0714 - val_acc: 0.9832
Epoch 2/8
243/243 [==============================] - 197s 812ms/step - loss: 0.0882 - acc: 0.9712 - val_loss: 0.0021 - val_acc: 1.0000
Epoch 3/8
243/243 [==============================] - 197s 810ms/step - loss: 0.0174 - acc: 0.9918 - val_loss: 4.8086e-05 - val_acc: 1.0000
Epoch 4/8
243/243 [==============================] - 197s 810ms/step - loss: 6.4367e-04 - acc: 1.0000 - val_loss: 4.7283e-07 - val_acc: 1.0000
Epoch 5/8
243/243 [==============================] - 197s 809ms/step - loss: 8.1133e-04 - acc: 1.0000 - val_loss: 3.4661e-07 - val_acc: 1.0000
Epoch 6/8
243/243 [==============================] - 197s 810ms/step - loss: 0.0023 - acc: 1.0000 - val_loss: 2.4443e-07 - val_acc: 1.0000
Epoch 7/8
243/243 [==============================] - 197s 809ms/step - loss: 1.4324e-05 - acc: 1.0000 - val_loss: 2.3842e-07 - val_acc: 1.0000
Epoch 8/8
243/243 [==============================] - 197s 809ms/step - loss: 5.6312e-04 - acc: 1.0000 - val_loss: 2.3842e-07 - val_acc: 1.0000
Last training accuracy: 1.0, last validation accuracy: 1.0
100.00% (+/- 0.00%)
[1. 1. 1.]
[1.0000000e+00 3.3988107e-10 1.9392887e-26]
[1.0000000e+00 7.8724999e-11 4.0537253e-28]
[1.0000000e+00 5.1808648e-11 1.6126082e-28]
[1.0000000e+00 7.1265943e-10 2.5077134e-25]
[1.0000000e+00 9.1078975e-09 2.2543007e-22]
[2.6691160e-10 1.0000000e+00 1.8465737e-13]
[1.5857763e-09 1.0000000e+00 3.7929394e-15]
[1.2745904e-10 1.0000000e+00 5.5758078e-15]
[3.1649053e-10 1.0000000e+00 1.0126757e-15]
[4.148320e-10 1.000000e+00 5.844798e-16]
[1.85090596e-18 1.03052024e-07 9.99999881e-01]
[0.0000000e+00 1.4195783e-17 1.0000000e+00]
[5.2113050e-28 1.7813436e-11 1.0000000e+00]
[3.2906028e-37 4.4432854e-15 1.0000000e+00]
[5.534547e-38 2.198336e-15 1.000000e+00]
0
0
0
0
0
1
1
1
1
1
2
2
2
2
2
Confusion matrix, without normalization
[[5 0 0]
 [0 5 0]
 [0 0 5]]
Normalized confusion matrix
[[1. 0. 0.]
 [0. 1. 0.]
 [0. 0. 1.]]
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_9 (Conv2D)            (None, 55, 448, 512)      5120      
_________________________________________________________________
activation_15 (Activation)   (None, 55, 448, 512)      0
_________________________________________________________________
max_pooling2d_7 (MaxPooling2 (None, 27, 224, 512)      0
_________________________________________________________________
conv2d_10 (Conv2D)           (None, 25, 222, 512)      2359808   
_________________________________________________________________
activation_16 (Activation)   (None, 25, 222, 512)      0
_________________________________________________________________
conv2d_11 (Conv2D)           (None, 23, 220, 256)      1179904   
_________________________________________________________________
activation_17 (Activation)   (None, 23, 220, 256)      0
_________________________________________________________________
max_pooling2d_8 (MaxPooling2 (None, 11, 110, 256)      0
_________________________________________________________________
dropout_7 (Dropout)          (None, 11, 110, 256)      0
_________________________________________________________________
conv2d_12 (Conv2D)           (None, 9, 108, 48)        110640
_________________________________________________________________
activation_18 (Activation)   (None, 9, 108, 48)        0
_________________________________________________________________
max_pooling2d_9 (MaxPooling2 (None, 4, 54, 48)         0
_________________________________________________________________
dropout_8 (Dropout)          (None, 4, 54, 48)         0
_________________________________________________________________
flatten_3 (Flatten)          (None, 10368)             0
_________________________________________________________________
dense_7 (Dense)              (None, 36)                373284
_________________________________________________________________
activation_19 (Activation)   (None, 36)                0
_________________________________________________________________
dense_8 (Dense)              (None, 18)                666
_________________________________________________________________
activation_20 (Activation)   (None, 18)                0
_________________________________________________________________
dropout_9 (Dropout)          (None, 18)                0
_________________________________________________________________
dense_9 (Dense)              (None, 3)                 57
_________________________________________________________________
activation_21 (Activation)   (None, 3)                 0
=================================================================
Total params: 4,029,479
Trainable params: 4,029,479
Non-trainable params: 0
_________________________________________________________________

Methodology Steps

  1. Record Data
  2. Convert Brain Products to Matlab readible
  3. Pre-process Data
  4. Convert data to CSV for each epoch of every stimulus
  5. first feature scaled – the standardization of the range of independent variables or features of data.
  6. Fed into DL... (wow, really Ben...)
    • Training
    • Validation
    • Run test data (unseen) through the model
    • (Above performed using k-fold)
  7. Confusion matrix generated
  8. 24x models created
    • A CNN model was trained for each semantic object for every participant
    • [(2+2+2)*4] = 24

    Note if model is run on validation (seen) data or testing data (unseen)

Each model was trained on the pre-processed EEG data representing a particular semantic object, with the goal of correctly classifying that object into its three modalities.

It should be noted that these accuracies are with respect to the training and validation of the model, and not based on the models performance on unseen data

About

Deep Learning applied to EEG signals of semantic categorization

thesis

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