Classification of ten different motor imagery eeg signals by using deep neural networks

dc.contributor.advisor Dokur, Zümray
dc.contributor.author Korhan, Nuri
dc.contributor.authorID 518152011
dc.contributor.department Mechatronics Engineering
dc.date.accessioned 2024-01-24T06:43:53Z
dc.date.available 2024-01-24T06:43:53Z
dc.date.issued 2023-08-19
dc.description Thesis(Ph.D.) -- Istanbul Technical University, Graduate School, 2023
dc.description.abstract Brain-Computer Interface (BCI) is a research area that aims at establishing a sustainable communication infrastructure between the brain and machines. The primary purpose of BCI is to restore functionality to paralyzed individuals, but it can also be used for gaming applications. Various modalities such as Electroencephalogram (EEG) and Functional Magnetic Resonance Imaging (fMRI) can be employed in this field. This thesis focuses on EEG-based BCI and specifically explores the classification of ten different motor imagery (MI) tasks using deep neural networks. Motor imagery is a BCI method that aims to detect imagined movements through potential changes on the scalp, which are measured by electrodes during the imagined motor movement. Increasing the number of recognizable tasks in BCI systems, specifically in the field of mechatronics, holds considerable importance. The limited scope of a four-command system significantly inhibits the versatility of these applications, particularly as they become more complex. To illustrate, imagine the operational demands of a drone, which requires absolute control over direction, altitude, speed, and elaborate maneuvers to navigate obstacles in three-dimensional space. The limitations of a four-command system decrease the number of controllable actions, thus undermining the efficacy and the scope of BCI applications. A substantial increase in the number of recognizable tasks in a BCI system signifies not only the expansion of its capabilities, but also a progression in advancing its applicability and versatility. In the first chapter, the problems of BCI are introduced, and the relevant literature is reviewed. In the second chapter, the concepts related to MI, the specific BCI area of interest, are explained. The third chapter examines methods to increase the number of commands in the MI paradigm, discussing previous approaches and the proposed methods. In the fourth chapter, deep learning tools commonly used in the field and employed in this research are introduced and discussed. The fifth and final chapter discusses the obtained results, their implications, and potential future research directions. The findings contribute to the advancement of BCI and demonstrate the feasibility of classifying ten different motor imagery EEG signals using deep neural networks, alongside augmentation, and divergence-based feature extraction. In summarizing the research conducted in this study, emphasis must be placed on the success rates achieved through the application of the developed methods. The techniques of artificial EEG signal generation, data augmentation, and regularization have been utilized, resulting in enhancements in the performance and efficiency of the BCI tasks. The methods employed have demonstrated promising results in various test scenarios. The success rates exceed those observed in traditional approaches documented in the literature. These rates are expanded upon in their respective sections and numerically illustrated in tables within the fifth chapter. Looking at the classification of both simple and combined MI-EEG signals across various studies, mean accuracy rates of around 51.6% and 54.2% were reported using different techniques for feature extraction and classification on three simple and one combined MI-EEG signals across a varied number of subjects. When increasing the number of classes used, as in four simple and four combined MI-EEG signals, a trend of increased mean accuracy was observed. Studies reported accuracy rates of 55% (four simple and four combined classes, dataset 3) and a substantial 70% (four simple and three combined classes) using different methods. The methods developed in this study demonstrate a significant improvement. For dataset 1, the proposed approach achieved an 85% mean accuracy with only DivFE on four simple and six combined classes across three subjects. Dataset 2 shows a 78.6% accuracy across nine subjects. Lastly, for the dataset 3 (four simple and four combined), the model achieved a 77.8% accuracy across seven subjects. These success rates not only validate the effectiveness of the proposed methods but also highlight the potential for future enhancements in BCI applications.
dc.description.degree Ph. D.
dc.identifier.uri http://hdl.handle.net/11527/24436
dc.language.iso en_US
dc.publisher Graduate School
dc.sdg.type Goal 9: Industry, Innovation and Infrastructure
dc.subject deep neural networks
dc.subject derin sinir ağları
dc.subject eeg signals
dc.subject eeg işaretleri
dc.subject multiple signal classificaton
dc.subject çoklu sinyal sınıflandırma
dc.subject standard time signal
dc.subject standart zaman sinyali
dc.title Classification of ten different motor imagery eeg signals by using deep neural networks
dc.title.alternative On farklı motor hareket hayaline ait eeg işaretlerinin derin sinir ağları ile sınıflandırılması
dc.type Doctoral Thesis
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