Browsing by Author "Boubchir, Larbi"

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Classification of Left/Right Hand and Foot Movements from EEG using Machine Learning Algorithms
(Institute of Electrical and Electronics Engineers Inc, 2023) Cherifi, Dalila; Berghouti, Baha Eddine; Boubchir, Larbi
In recent years, there has been growing interest in utilizing Electroencephalography (EEG) data and machine learning techniques to develop innovative solutions for individuals with disabilities. The ability to accurately classify hands and foot motion based on EEG signals holds great potential for enabling individuals to regain control and functionality of their disabled parts, improving their quality of life and independence. Making a better solution than the traditional ones that often require physical contact or can be challenging to operate. In our study, we have focused on hands (right/left) and foot motion disabilities, using supervised Machine Learning algorithms for the classification of EEG data related to left/right hand and foot movements; aiming to reach accurate results that can contribute to providing a solution for people with this kind of motion disabilities. Three supervised machine learning algorithms are considered for the EEG classification, namely Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), using Common Spatial Patterns (CSP) algorithm and logarithm of the variance (logvar) for feature extraction. In our experiments, we adopted these algorithms to classify the Motor Imagery EEG dataset for hands and foot movements given in BCI Competition IV. The data we used went through different steps before fitting into the models such as filtering, feature extraction, and discrimination. We achieved significant success in accurately classifying hand movements in the initial experiment, attaining an impressive classification accuracy of up to 97.5% with SVM and LDA. Furthermore, in the multi-classification task involving both hand (right/left) and foot movements, KNN and SVM classifiers yielded commendable results up to 87%. These models can be further used and developed, where a hardware implementation will be done as a further work for this study.
EEG signal feature extraction and classification for epilepsy detection
(Slovene Society Informatika, 2022) Cherifi, Dalila; Falkoun, Noussaiba; Ouakouak, Ferial; Boubchir, Larbi; Nait-Ali, Amine
Epilepsy is a neurological disorder of the central nervous system, characterized by sudden seizures caused by abnormal electrical discharges in the brain. Electroencephalogram (EEG) is the most common technique used for Epilepsy diagnosis. Generally, it is done by the manual inspection of the EEG recordings of active seizure periods (ictal). Several techniques have been proposed throughout the years to automate this process. In this study, we have developed three different approaches to extract features from the filtered EEG signals. The first approach was to extract eight statistical features directly from the time-domain signal. In the second approach, we have used only the frequency domain information by applying the Discrete Cosine Transform (DCT) to the EEG signals then extracting two statistical features from the lower coefficients. In the last approach, we have used a tool that combines both time and frequency domain information, which is the Discrete Wavelet Transform (DWT). Six different wavelet families have been tested with their different orders resulting in 37 wavelets. The first three decomposition levels were tested with every wavelet. Instead of feeding the coefficients directly to the classifier, we summarized them in 16 statistical features. The extracted features are then fed to three different classifiers k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Artificial Neural Network (ANN) to perform two binary classification scenarios: healthy versus epileptic (mainly from interictal activity), and seizure-free versus ictal. We have used a benchmark database, the Bonn database, which consists of five different sets. In the first scenario, we have taken six different combinations of the available data. While in the second scenario, we have taken five combinations. For Epilepsy detection (healthy vs epileptic), the first approach performed badly. Using the DCT improved the results, but the best accuracies were obtained with the DWT-based approach. For seizure detection, the three methods performed quite well. However, the third method had the best performance and was better than many state-of-the-art methods in terms of accuracy. After carrying out the experiments on the whole EEG signal, we separated the five rhythms and applied the DWT on them with the Daubechies7 (db7) wavelet for feature extraction. We have observed that close accuracies to those recorded before can be achieved with only the Delta rhythm in the first scenario (Epilepsy detection) and the Beta rhythm in the second scenario (seizure detection)
Multi-class EEG signal classification for epileptic seizure diagnosis
(Springer, 2020) Cherifi, Dalila; Afoun, Laid; Iloul, Zakaria; Boukerma, Billal; Adjerid, Chaouki; Boubchir, Larbi; Nait-Ali, Amine
EEG signal recordings are increasingly replacing the old methods of diagnosis in medical field of many neurological disorders. Our contribution in this article is the study and development of EEG signal classification algorithms for epilepsy diagnosis using one rhythm; for classification, an optimum classifier is proposed with only when used one rhythm so that both execution time and number of features are reduced. We used wavelet packet decomposition (WPD) to extract the five rhythms of brain activity from the public Epilepsy-EEG recordings in order to represent each signal with features vector; then we applied on it the well-known classification methods. A statistical study is done to validate the different algorithms
Prediction models for epilepsy detection on the EEG signal
(IEEE, 2022) Cherifi, Dalila; Zenati, Hichem; Ouchene, Mohamed Amine; Merbouti, Mohammed Abdenacer; Ibrahim, Dyhia; Boubchir, Larbi
Epilepsy is a neurological illness characterized by abnormal brain activity, resulting in seizures or episodes of odd behavior, feelings, and in some cases, loss of awareness. In this work, we propose a comparison between three deep learning models: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and bidirectional LSTM for epileptic seizure detection using EEG data which is the most common technique used for Epilepsy diagnosis. The objective of this work is to define the most suitable model for this sensitive task and to reach the highest possible accuracy. To evaluate the performance of the proposed methods, many experiments are conducted to study the effect of some parameters and using two categorical combinations of an EEG dataset. As a result, we reached a prediction accuracy of 90.26% with CNN, 86.17% with LSTM but the Bi-LSTM model consistently outperformed the other models reaching more than 98% accuracy. Finally, these results demonstrate the possibility of detecting the epileptic seizures while maintaining model interpretability, which may contribute to a better understanding of brain dynamics and enhance predictive performances