How to upload scripts and the dataset and start work, manual in this YouTube video
Issues, Questions, join to PiEEG Discord via the next link and ask in the Book branch
Author - Dr. Ildar Rakhmatulin, LinkedIn
1 Fundamentals of EEG ................................................................. 1
1.1 Basic Principles of EEG Signals ................................................. 1
1.2 Understanding Brain Signals: Motivation and Real-World Applications ............. 3
1.3 Understanding EEG: What It Is and How It Works .................................. 4
1.4 EEG Signal Amplitudes: Are Microvolts Significant? .............................. 6
1.5 Measuring EEG Signals: Methods and Instrumentation .............................. 7
1.6 Electrode Placement in EEG: Locations and Systems ............................... 8
1.7 How Many EEG Channels Are Needed? Factors and Considerations .................... 9
1.8 Understanding EEG Noise: Sources and Challenges ................................. 10
1.9 EEG Hardware: An Introduction to Recording Systems .............................. 11
2 Getting Started with Signal Processing in Python .................................... 15
2.1 Introduction to Signal Processing ............................................... 15
2.2 Why Is Python Good for EEG? ..................................................... 16
2.2.1 NumPy: Numerical Computation .............................................. 17
2.2.2 SciPy: Signal Processing Library .......................................... 18
2.2.3 MNE-Python: A Specialized Library for EEG ................................. 19
2.3 Connect to Python Environment ................................................... 20
2.3.1 Getting Started with Google Colab ......................................... 20
2.3.2 Organize the Files ........................................................ 21
2.3.3 Launch in Google Colab .................................................... 21
2.3.4 Launch in Google Colab .................................................... 22
2.3.5 Connect the Dataset ....................................................... 23
3 EEG and Visualization ............................................................... 25
3.1 Introduction to Sources ......................................................... 25
3.1.1 Scripts ................................................................... 26
3.1.2 Dataset ................................................................... 26
3.1.3 Environment ............................................................... 26
3.1.4 Import Dataset ............................................................ 27
3.1.5 Dataset Details ........................................................... 28
3.2 Start Work with the Dataset ..................................................... 29
3.2.1 Convert Data from Digital Format to EEG ................................... 29
3.2.2 Data Visualization ........................................................ 30
3.2.3 What Is Raw Data .......................................................... 31
3.2.4 Heatmap of EEG ............................................................ 32
3.2.5 3D Graph for Data ......................................................... 32
3.2.6 Correlation Matrix ........................................................ 34
3.2.7 Popular Libraries for EEG ................................................. 36
3.2.8 Spatial Distribution of Brain Activity Over The Scalp ..................... 37
4 Bandpass Filter ..................................................................... 41
4.1 How Much Data and Channels Do We Need ........................................... 41
4.2 Typical Data Length Required for Algorithms ..................................... 42
4.3 Band-Pass Filter Implementation ................................................. 43
4.3.1 High-Pass Filter .......................................................... 44
4.3.2 Low-Pass Filter ........................................................... 45
4.3.3 Band-Pass Filter .......................................................... 46
5 Smoothing Filters ................................................................... 51
5.1 Average Filter .................................................................. 51
5.2 Gaussian Filter ................................................................. 54
5.3 Median Filter ................................................................... 56
6 Frequency Analysis .................................................................. 61
6.1 Fast Fourier Transform (FFT) .................................................... 61
6.2 Wavelet Transform ............................................................... 64
6.3 Hilbert Transform ............................................................... 66
7 Navigating Noise: Strategies for EEG Artefact Removal ............................... 73
7.1 Biological Artefacts ............................................................ 73
7.2 Electromagnetic Noise ........................................................... 74
7.3 Current Stage in Science ........................................................ 76
7.4 Independent Component Analysis (ICA) ............................................ 77
7.5 Canonical Correlation Analysis .................................................. 84
7.6 Empirical Mode Decomposition (EMD) .............................................. 85
7.6.1 K-Nearest Neighbors (KNN) to Detect Artifacts in EEG Data ................. 88
7.7 Evaluation of Artifact Removal .................................................. 94
7.7.1 Evaluation via Fast Fourier Transform (FFT) ............................... 94
7.7.2 Evaluation via Signal-to-Noise Ratio (SNR) ................................ 96
8 Real-Time Signal Processing in EEG .................................................. 101
8.1 Introduction .................................................................... 101
8.2 How It Works .................................................................... 102
9 Application Without Machine Learning ................................................ 109
9.1 Introduction .................................................................... 109
9.2 Entropy for Stress Control ...................................................... 109
9.3 Power Spectral Density (PSD) .................................................... 112
10 Machine Learning: EEG Perspective ................................................... 117
10.1 Introduction to ML ............................................................. 117
10.2 Why Use Machine Learning for EEG ............................................... 118
10.3 Introduction to Machine Learning for EEG ....................................... 119
10.4 Challenges and Considerations .................................................. 121
10.5 Applications ................................................................... 122
10.6 Conclusion ..................................................................... 123
References .......................................................................... 124
11 Dataset for Machine Learning ........................................................ 127
11.1 Introduction ................................................................... 127
11.2 Dataset Format ................................................................. 128
11.3 Visualization of the Dataset ................................................... 130
11.4 Correlation Matrix ............................................................. 133
11.5 Cap Visualization .............................................................. 135
12 Dataset Preprocessing ............................................................... 139
12.1 Prepare Dataset ................................................................ 139
12.2 Channel Selection .............................................................. 141
12.2.1 Signal-to-Noise Ratio (SNR) ............................................. 142
12.2.2 Preprocessing EEG Data Recursive Feature Elimination .................... 144
12.2.3 Pearson Correlation ..................................................... 146
12.2.4 Feature Importance via Machine Learning ................................. 147
13 ML and EEG .......................................................................... 153
13.1 Standardize and Normalize Data ................................................. 153
13.2 Hot Coding ..................................................................... 155
13.3 Take Power for ML .............................................................. 156
13.4 Logistic Regression ............................................................ 160
13.5 Hyperparameters ................................................................ 163
13.5.1 Grid Search and Random Search ........................................... 163
13.6 k-Nearest Neighbors (KNN) ...................................................... 169
13.7 Support Vector Machines (SVM) .................................................. 172
13.8 Random Forests ................................................................. 176
14 Case Studies and Applications ....................................................... 181
14.1 Brain-Computer Interfaces: Now and Future ...................................... 181
14.2 Consumer Applications .......................................................... 182
14.3 Gaming and Entertainment ....................................................... 183
14.4 Smart Home Control Systems ..................................................... 184
14.5 Large Language Models (LLM) and EEG ............................................ 185
14.6 Privacy and Data Security Concerns ............................................. 187