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Developing Brain Computer Interface for Imagined Movements
Blašková, Barbora ; Jawed, Soyiba (oponent) ; Malik, Aamir Saeed (vedoucí práce)
Brain disorders and diseases affect 1 in 6 people worldwide and in many cases result in a condition that profusely impacts the life of patient. Mental health topics surge as 1 in 10 people is diagnosed with a mental health disorder. It is therefore crucial to study the organ that is still in a big part a mystery to the researchers - brain. The focus of this thesis is on Brain Computer Interface (BCI) which can act as a intermediary between the brain and a device by acquiring the brain signals and translating them into a set of actions or commands. One of the methods to control a device by thoughts is motor imagery, which is based on the fact that imagining moving a part of the body elicits the same brain response as actual movement. This thesis proposes to utilize a recent field of the EEG for the BCI applications - microstate analysis. Classifier for distinguishing between the motor imagery tasks is proposed as a combination of microstate features extracted from different regions of the brain with the already established features such as from frequency or time-domain. The subject-specific classifiers was trained for 30 participants. Two distinct classifiers were implemented - one for the classification of the rest versus activity and second for the classification of the left versus right motor imagery. The mean accuracy across participants for the rest versus activity classification was 0.85. The mean accuracy across participants for the left versus right motor imagery classification was 0.74. The microstates proved to be helpful in distinguishing between different conditions in a task settings, but need some improvements in terms of the further research.
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Emotion Recognition from Brain Electroencephalogram (EEG) Signals
Fritz, Karel ; Jawed, Soyiba (oponent) ; Malik, Aamir Saeed (vedoucí práce)
This study targets classifying emotion states, from Electroencephalogram (EEG) signal. Combining knowledge about physiology of the brain (and emotions), with frequency anal- ysis, complexity analysis, signal processing and deep machine learning (CNN, GNN). Goal of this work is to create the emotion classification model and provide new insights into emotion recognition from EEG. Models created stands on the principles of CNN, GNN, multitask and self supervised training. One of the results achieved State of the Art results on the SEED dataset. Sharing process of understanding this task at the end of the thesis.
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Evolutionary Design of EEG Data Classifier
Kuželová, Simona ; Jawed, Soyiba (oponent) ; Mrázek, Vojtěch (vedoucí práce)
This thesis focuses on developing an effective classifier for candidate classification based on a set of extracted Electroencephalography (EEG) signal features. To achieve this, a genetic algorithm was utilized for feature selection and optimalization of the classifier’s parameters based on five criteria: minimizing the number of features, minimizing inference time, and maximizing classification sensitivity, specificity, and accuracy. The eyes opened EEG data of 31 candidates suffering from Major Depressive Disorder (MDD) and 28 healthy candidates were used for feature extraction, with the goal of classifying candidates as either having MDD or being healthy. Two algorithms, NSGA-II and NSGA-III, were tested. The proposed algorithm operated with three criteria, but two additional criteria, sensitivity and specificity, were added. NSGA-III was more effective in this case and was used in the remaining experiments. Constraints were introduced to improve performance, and different values for the mutation and crossover probability were tried. The classifiers from the final result have an average accuracy of $91.36\%$, sensitivity of $91.82\%$, and specificity of $90.84\%$. In the final experiments most frequently used channels were F3 and C3 channels and most commonly utilized waveband was gamma waveband. Overall, this work presents effective classifiers that were obtained using the proposed algorithm, which utilizes a genetic algorithm for parameter optimization.
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Artefacts Removal from Brain EEG Signals Using Adaptive Algorithms
Hatala, Juraj ; Jawed, Soyiba (oponent) ; Shakil, Sadia (vedoucí práce)
This thesis covers the problem of artifacts in electroencephalography (EEG) data and the methods used to remove them with a focus on adaptive filtering. Artifacts are an unavoid- able part of the EEG method and they have a negative impact on the analysis of the results by covering the brain signals of interest. Adaptive filtering is a versatile method that can be used for removal of these artifacts if the reference signal correlated with the artifact is pro- vided. The primary goal of this thesis is a proposal and implementation of the framework that can be used to apply methods of adaptive filtering on EEG data. The secondary goal is to examine the effectiveness of a novel Q-LMS algorithm on the task of removal of artifacts from EEG as it was not yet used in this scenario. The work is introducing a library in a Python environment for EEG adaptive filtering and shows and evaluates experiments for EEG artifact removal scenarios with a Q-LMS filter implemented in the proposed library. In this library, a user is able to construct customizable filtering pipelines. The library of- fers a variety of adaptive filters and reference-building methods with a focus on processing neurological data in BIDS format. However, the user is able to share his custom filters with the framework as well as use his own input data and reference signals. The experiments with Q-LMS showed that it is a well-functioning adaptive algorithm yet the filtering results were moderate in contrast to results obtained by other standard adaptive algorithms.
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EEG Classification Model for Emotion Detection Using Python
Vengerová, Veronika ; Zaheer, Muhammad Asad (oponent) ; Jawed, Soyiba (vedoucí práce)
This thesis deals with the task of recognizing emotions from electroencephalogram (EEG). Two models were trained for binary classification of emotions, where one classifies neutral emotion or fear and the other classifies happiness or sadness. During the work on this thesis many different architectures were tried, and the best result was obtained using a model with two branches of CNN-LSTM connected before the output layer. The resulting accuracy was 87.309% for sad-happy classification and 84.865% for neutral-fear emotion.
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