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https://olympias.lib.uoi.gr/jspui/handle/123456789/37651| Title: | Ανάπτυξη Εύρωστων Εφαρμογών Αλληλεπίδρασης Ανθρώπου -Μηχανής βασισμένων σε Ηλεκτροεγκεφαλογραφικές Καταγραφές: Μελέτη Μεθόδων Μηχανικής Μάθησης σε Γνωσιακές Καταστάσεις και Νευρολογικές Διαταραχές Towards Robust EEG-based Human-Computer Interaction Applications: Exploring Machine Learning Methods in Cognitive States and Neurological Disorders |
| Institution and School/Department of submitter: | Πανεπιστήμιο Ιωαννίνων. Σχολή Πληροφορικής και Τηλεπικοινωνιών |
| Subject classification: | Βιοπληροφορική Τεχνητή Νοημοσύνη |
| Keywords: | Ηλεκτροεγκεφαλογράφημα,Μηχανική Μάθηση,Αυτόματη Διάγνωση,Νευρολογικές Διαταραχές,Brain-Computer Interfaces |
| URI: | https://olympias.lib.uoi.gr/jspui/handle/123456789/37651 http://dx.doi.org/10.26268/heal.uoi.17359 |
| Table of contents: | Executive Summary 1 Επιτελική Σύνοψη 3 Table of Contents 5 List of Tables 11 List of Figures 13 1.1. Introduction 18 1.2. Research Objectives 19 1.3. Dissertation Structure 21 2.1. Human Brain Anatomy 24 2.2. Electroencephalography 26 2.2.1. Basic functionality of neurons 26 2.2.2. Traditional usage of EEG 27 2.2.3. Automated Methods in EEG Analysis 28 2.2.3.1. Historical overview 28 2.2.3.2. Overview 29 2.2.4. Frequency bands of Interest 30 2.3. Conditions investigated through EEG 31 2.3.1. Epilepsy 32 2.3.2. Alzheimer’s Disease and other Dementia 34 2.3.3. Other Conditions 36 2.4. Brain-Computer Interfaces 36 2.4.1. EEG-based BCI 36 2.5. Clinical devices and Wearable EEG 37 3.1. Preprocessing 40 3.1.1. Basic preprocessing techniques in EEG Analysis 40 3.1.1.1. Filtering 40 3.1.1.2. Rereferencing 42 3.1.1.3. Electrode Interpolation 43 3.1.1.4. Epoching 43 3.1.2. Artifact removal techniques 44 3.2. Frequency Domain Analysis 45 3.2.1. Fourier Transform 46 3.2.1.1. Fast Fourier Transform 47 3.2.2. Power Spectral Density 47 3.2.2.1. Welch Method 48 3.2.2.2. Burg’s Method 48 3.2.2.3. Multitaper Method 49 3.2.3. Limitations of Frequency Domain analysis 50 3.3. Time-Frequency Domain Analysis 50 3.3.1. Short-Time Fourier Transform 51 3.3.2. Wavelet Transform 51 3.3.2.1. Continuous Wavelet Transform 53 3.3.2.2. Discrete Wavelet Transform 53 3.3.3. Hilbert-Huang Transform 54 3.3.3.1. Empirical Mode Decomposition 54 3.3.4. Wigner-Ville Distribution 56 3.3.5. S-Transform 56 3.3.6. Limitations of Time-Frequency Analysis 57 3.4. Characteristics extracted from EEG 58 3.4.1. Statistical Features 58 3.4.2. Frequency Features 59 3.4.3. Complexity Features 60 3.4.3.1. Entropy-based measures 60 3.4.3.2. Fractal-Dimension 61 3.4.4. Aperiodic components – 1/f slope 62 3.5. Synchronization Features 63 4.1. Introduction and Importance 67 4.1.1. Problems that are addressed 67 4.2. Machine Learning Pipeline 67 4.2.1. Preprocessing & Feature Extraction 69 4.2.2. Feature Reduction 69 4.2.2.1. Principal Component Analysis 70 4.2.2.2. Feature Selection 71 4.2.3. Training, Testing & Validation methodologies 72 4.2.4. Evaluation Metrics 73 4.3. Machine Learning 75 4.3.1. Traditional Classifiers 75 4.3.1.1. Decision Trees 75 4.3.1.2. Naïve Bayes 76 4.3.1.3. Logistic Regression 76 4.3.1.4. Support Vector Machines (SVM) 77 4.3.1.5. k-Nearest Neighbors 78 4.3.1.6. Linear Discriminant Analysis (LDA) 78 4.3.2. Ensemble Classifiers 79 4.3.2.1. Random Forests 79 4.3.2.2. Extra Trees 79 4.3.2.3. Gradient Boosting 80 4.3.3. Deep Learning 80 4.3.3.1. Feed Forward Neural Networks (FFNs) 81 4.3.3.2. Training Neural Networks: Backpropagation 82 4.3.3.3. Convolutional Neural Networks (CNNs) 83 4.3.3.4. Recurrent Neural Networks (RNNs) 85 4.3.3.5. Long Short-Term Memory Networks (LSTMs) 86 4.3.3.6. Transformers 87 4.3.4. Hyperparameter optimization 88 4.4. Clinical Applications 89 4.4.1. Challenges, Limitations and Problems 89 5.1. Alzheimer’s Disease and Dementia Research 93 5.1.1. Alzheimer’s Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods 95 5.1.2. Enhanced Alzheimer’s disease and Frontotemporal Dementia EEG Detection: Combining lightGBM Gradient Boosting with Complexity Features 101 5.1.2.1. Preprocessing 102 5.1.2.2. Feature Extraction 103 5.1.2.3. Classification 103 5.1.2.4. Results 106 5.1.3. A dataset of scalp EEG recordings of Alzheimer’s disease, Frontotemporal Dementia and Healthy subjects from routine EEG 107 5.1.3.1. Dataset description 109 5.1.3.2. Dataset structure 111 5.1.3.3. Preprocessing 113 5.1.3.4. Benchmark experiments 113 5.1.4. DICE-net: A Novel Convolution-Transformer Architecture for Alzheimer Detection in EEG Signals 116 5.1.4.1. Methodology 118 5.1.4.2. Results 127 5.1.4.3. Discussion 132 5.2. Epilepsy Detection with EEG based Machine Learning Research 136 5.2.1. Evaluating the Window Size’s Role in Automatic EEG Epilepsy Detection 138 5.2.2. Methodology 138 5.2.2.1. The BFGS method 139 5.2.2.2. The multistart method 140 5.2.3. Results 141 5.2.4. Machine Learning Algorithms for Epilepsy Detection based on published EEG databases: A Systematic Review 144 5.2.4.1. Methodology 144 5.2.4.2. Results 147 5.2.4.3. Discussion and Statistics 157 5.2.4.4. Related work comparison 163 5.3. Brain-Computer Interfaces for analyzing cognitive states 165 5.3.1. Assessing Electroencephalography as a Stress Indicator: A VR High-Altitude Scenario monitored through EEG and ECG 165 5.3.1.1. Methodology 168 5.3.1.2. Results 172 5.3.1.3. Discussion 176 5.3.2. Classification of EEG signals from Young Dyslexic Adults combining a Brain Computer Interface device and an Interactive Linguistic Software Tool 179 5.3.3. Methodology 180 5.3.3.2. Results 183 5.3.3.3. Discussion 185 5.3.4. An ensemble method for EEG-based texture discrimination during open eyes active touch 187 5.3.4.1. Introduction 187 5.3.4.2. Methodology 188 5.3.4.3. Results 191 5.3.4.4. Discussion 194 Regarding Future Insights 197 References 198 Abbreviations list 214 |
| Appears in Collections: | Διδακτορικές Διατριβές - ΤΠΤ |
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