Computer Engineering Commons^™

Cognitive Load Detection Using Ci-Ssa For Eeg Signal Decomposition And Nature-Inspired Feature Selection, Jammisetty Yedukondalu, Lakhan Dev Sharma

Turkish Journal of Electrical Engineering and Computer Sciences

Cognitive load detection is eminent during the mental assignment of neural activity because it indicates how the brain reacts to stimuli. The level of cognitive load experienced during mental arithmetic tasks can be determined using an electroencephalogram (EEG). The EEG data were collected from publicly available datasets, namely, mental arithmetic task (MAT) and simultaneous task workload (STEW). The first phase comprises decomposing the electroencephalogram (EEG) signal into intrinsic mode functions (IMFs) using circulant singular spectrum analysis (Ci-SSA). In the second phase, entropy-based features were evaluated using IMFs. After that, the extracted features were fed to nature-inspired feature selection algorithms: genetic …

Multi-View Contrastive Learning For Unsupervised Domain Adaptation In Brain-Computer Interfaces, Sepehr Asgarian

Electronic Thesis and Dissertation Repository

Electroencephalography (EEG) has been widely used to record electromagnetic fields for motor imagery (MI)-based brain-computer interfaces (BCIs). However, collecting MI signals is often time-consuming and challenging to classify due to the inter-subject variability of EEG signals. To address these issues, we propose a novel framework MACNet, which stands for Multi-view Adversarial Contrastive Network. MACNet employs a contrastive learning approach to learn spatial and temporal features in two views, using Riemannian and Euclidean encoders. By jointly extracting underlying features and learning domain-invariant representations in both source and target features, MACNet improves the alignment and accuracy. In addition, we propose a domain …

Affective States Classification Performance Of Audio-Visual Stimuli From Eeg Signals With Multiple-Instance Learning, Yaşar Daşdemi̇r, Rüstem Özakar

Turkish Journal of Electrical Engineering and Computer Sciences

Throughout various disciplines, emotion recognition continues to be an essential subject of study. With the advancement of machine learning methods, accurate emotion recognition from different data modalities (facial images, brain EEG signals) has become possible. Success of EEG-based emotion recognition systems depends on efficient feature extraction and pre/postprocessing of signals. Main objective of this study is to analyze the efficacy of multiple-instance learning (MIL) on postprocessing features of EEG signals using three different domains (time, frequency, time-frequency) for human emotion classification. Methods and results are presented for single-trial classification of valence (V), arousal (A), and dominance (D) ratings from EEG …

A Routine Electroencephalography Monitoring System For Automated Sports-Related Concussion Detection, Amirsalar Mansouri, Patrick Ledwidge, Khalid Sayood, Dennis L. Molfese

Department of Electrical and Computer Engineering: Faculty Publications

Cases of concussions in the United States keep increasing and are now up to 2 million to 3 million incidents per year. Although concussions are recoverable and usually not life-threatening, the degree and rate of recovery may vary depending on age, severity of the injury, and past concussion history. A subsequent concussion before full recovery may lead to more-severe brain damage and poorer outcomes. Electroencephalography (EEG) recordings can identify brain dysfunctionality and abnormalities, such as after a concussion. Routine EEG monitoring can be a convenient method for reducing unreported injuries and preventing long-term damage, especially among groups with a greater …

Classification Of P300 Based Brain Computer Interface Systems Using Longshort-Term Memory (Lstm) Neural Networks With Feature Fusion, Ali̇ Osman Selvi̇, Abdullah Feri̇koğlu, Derya Güzel

Turkish Journal of Electrical Engineering and Computer Sciences

Enabling to obtain brain activation signs, electroencephalography is currently used in many applications as a medical diagnostic method. Brain-computer interface (BCI) applications are developed to facilitate the lives of individuals who have not lost their brain functions yet have lost their motor and communication abilities. In this study, a BCI system is proposed to make classification using Bi-directional long short term memory (Bi-LSTM) neural networks. In the designed system, spectral entropy method including instantaneous frequency change of signal is used as feature fusion. In the study, electroencephalography (EEG) data of 10 participants are collected with Emotiv EPOC+ device using 2x2 …

Increasing Performance Of Classifiers For Ssvep-Based Brain-Computer Interfaces Using Extension Methods, Ethan Douglas Webster

Legacy Theses & Dissertations (2009 - 2024)

Brain-computer interfaces (BCI) provide an alternative communication method that does not require standard physical mediums (speech, typing, etc.). These systems have been implemented to provide additional communication and control options for people with certain motor disabilities. Classification is an important part of BCI systems and consists of inferring user commands from brain activity. Supervised classification methods often achieve higher accuracy, but unsupervised classification methods are useful when training is not practical for the user. This thesis focuses on unsupervised classification algorithms used for a BCI speller application and presents extensions for two existing classifiers that improve classification accuracy and thus …

Intent Recognition In Smart Living Through Deep Recurrent Neural Networks, Xiang Zhang, Lina Yao, Chaoran Huang, Quan Z. Sheng, Xianzhi Wang

Research Collection School Of Computing and Information Systems

Electroencephalography (EEG) signal based intent recognition has recently attracted much attention in both academia and industries, due to helping the elderly or motor-disabled people controlling smart devices to communicate with outer world. However, the utilization of EEG signals is challenged by low accuracy, arduous and time-consuming feature extraction. This paper proposes a 7-layer deep learning model to classify raw EEG signals with the aim of recognizing subjects’ intents, to avoid the time consumed in pre-processing and feature extraction. The hyper-parameters are selected by an Orthogonal Array experiment method for efficiency. Our model is applied to an open EEG dataset provided …

Deep Learning And Transfer Learning In The Classification Of Eeg Signals, Jacob M. Williams

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Deep learning is seldom used in the classification of electroencephalography (EEG) signals, despite achieving state of the art classification accuracies in other spatial and time series data. Instead, most research has continued to use manual feature extraction followed by a traditional classifier, such as SVMs or logistic regression. This is largely due to the low number of samples per experiment, high-dimensional nature of the data, and the difficulty in finding appropriate deep learning architectures for classification of EEG signals. In this thesis, several deep learning architectures are compared to traditional techniques for the classification of visually evoked EEG signals. We …

Eareeg Final Report, Tyler Stuessi, Jeremy Herwig, Dillon Hunneke, Evan Goble, Arthur Vidineyev

Chancellor’s Honors Program Projects

No abstract provided.

Empirical Modeling Of Asynchronous Scalp Recorded And Intracranial Eeg Potentials, Komalpreet Kaur

Electrical & Computer Engineering Theses & Dissertations

A Brain-Computer Interface (BCI) is a system that allows people with severe neuromuscular disorders to communicate and control devices using their brain signals. BCIs based on scalp-recorded electroencephalography (s-EEG) have recently been demonstrated to provide a practical, long-term communication channel to severely disabled users. These BCIs use time-domain s-EEG features based on the P300 event-related potential to convey the user's intent. The performance of s-EEG-based BCIs has generally stagnated in recent years, and high day-to-day performance variability exists for some disabled users. Recently intracranial EEG (i-EEG), which is recorded from the cortical surface or the hippocampus, has been successfully used …

A Novel Synergistic Model Fusing Electroencephalography And Functional Magnetic Resonance Imaging For Modeling Brain Activities, Konstantinos Michalopoulos

Browse all Theses and Dissertations

Study of the human brain is an important and very active area of research. Unraveling the way the human brain works would allow us to better understand, predict and prevent brain related diseases that affect a significant part of the population. Studying the brain response to certain input stimuli can help us determine the involved brain areas and understand the mechanisms that characterize behavioral and psychological traits.

In this research work two methods used for the monitoring of brain activities, Electroencephalography (EEG) and functional Magnetic Resonance (fMRI) have been studied for their fusion, in an attempt to bridge together the …

Low Cost Neurochairs, Frankie Pike

Master's Theses

Electroencephalography (EEG) was formerly confined to clinical and research settings with the necessary hardware costing thousands of dollars. In the last five years a number of companies have produced simple electroencephalograms, priced below $300 and available direct to consumers. These have stirred the imaginations of enthusiasts and brought the prospects of "thought-controlled" devices ever closer to reality. While these new devices were largely targeted at video games and toys, active research on enabling people suffering from debilitating diseases to control wheelchairs was being pursued. A number of neurochairs have come to fruition offering a truly hands-free mobility solution, but whether …

Determination Of Autoregressive Model Orders For Seizure Detection, Serap Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

In the present study, a step-wise least square estimation algorithm (SLSA), implemented in a Matlab package called as ARfit, has been newly applied to clinical data for estimation of the accurate Auto-Regressive (AR) model orders of both normal and ictal EEG series where the power spectral density (PSD) estimations are provided by the Burg Method. The ARfit module is found to be usefull in comparison to a large variety of traditional methods such as Forward Prediction Error (FPE), Akaike's Information Criteria (AIC), Minimum Description Lenght (MDL), and Criterion of Autoregressive Transfer function (CAT) for EEG discrimination. According to tests, the …

Comparison Of Basic Linear Filters In Extracting Auditory Evoked Potentials, Serap Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

In the present study, the performances of two well-known linear filtering techniques are compared for extraction of auditory Evoked Potential (EP) from a relatively small number of sweeps. Both experimental and simulated data are filtered by the two algorithms into two groups. Group A consists of Wiener filtering (WF) applications, where conventional WF and Coherence Weighted WF (CWWF)) have been assessed in combination with the Subspace Method (SM). Group B consists of the well-known adaptive filtering algorithms Least Mean Square (LMS), Recursive Least Square (RLS), and one-step Kalman filtering (KF). Both groups are tested with respect to signal-to-noise ratio (SNR) …