Engineering Commons^™

Evaluating Eeg–Emg Fusion-Based Classification As A Method For Improving Control Of Wearable Robotic Devices For Upper-Limb Rehabilitation, Jacob G. Tryon

Electronic Thesis and Dissertation Repository

Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices.

One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor …

Interpreting Energy Utilisation With Shapley Additive Explanations By Defining A Synthetic Data Generator For Plausible Charging Sessions Of Electric Vehicles, Prasant Kumar Mohanty, Gayadhar Panda, Malabika Basu, Diptendu Sinha Roy

Articles

Electric vehicles (EVs) are an effective solution for reducing reliance on non-renewable energy sources. However, the lack of charging infrastructure and concerns over their range are some of the biggest hurdles to adopting EVs. Charging infrastructure for EVs is, however, on the rise. Proper planning of charging stations vis-`a-vis road networks and related points of interest such as transportation hubs, schools, shopping centres, etc., alongside such roads become vital to laying out a plan for such infrastructure, particularly for developing countries like India where EV adoption is relatively in a nascent stage. Synthetic datasets can help overcome these hurdles and …

Volitional Control Of Lower-Limb Prosthesis With Vision-Assisted Environmental Awareness, S M Shafiul Hasan

FIU Electronic Theses and Dissertations

Early and reliable prediction of user’s intention to change locomotion mode or speed is critical for a smooth and natural lower limb prosthesis. Meanwhile, incorporation of explicit environmental feedback can facilitate context aware intelligent prosthesis which allows seamless operation in a variety of gait demands. This dissertation introduces environmental awareness through computer vision and enables early and accurate prediction of intention to start, stop or change speeds while walking. Electromyography (EMG), Electroencephalography (EEG), Inertial Measurement Unit (IMU), and Ground Reaction Force (GRF) sensors were used to predict intention to start, stop or increase walking speed. Furthermore, it was investigated whether …

Examining The Size Of The Latent Space Of Convolutional Variational Autoencoders Trained With Spectral Topographic Maps Of Eeg Frequency Bands, Taufique Ahmed, Luca Longo

Articles

Electroencephalography (EEG) is a technique of recording brain electrical potentials using electrodes placed on the scalp [1]. It is well known that EEG signals contain essential information in the frequency, temporal and spatial domains. For example, some studies have converted EEG signals into topographic power head maps to preserve spatial information [2]. Others have produced spectral topographic head maps of different EEG bands to both preserve information in The associate editor coordinating the review of this manuscript and approving it for publication was Ludovico Minati . the spatial domain and take advantage of the information in the frequency domain [3]. …

Detection Of Amyotrophic Lateral Sclerosis Disease By Variational Modedecomposition And Convolution Neural Network Methods From Event-Relatedpotential Signals, Fatma Lati̇foğlu, Firat Orhan Bulucu, Rami̇s İleri̇

Turkish Journal of Electrical Engineering and Computer Sciences

Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease, is a neurological disease that occurs as a result of damage to the nerves in the brain and restriction of muscle movements. Electroencephalography (EEG) is the most common method used in brain imaging to study neurological disorders. Diagnosis of neurological disorders such as ALS, Parkinson's, attention deficit hyperactivity disorder is important in biomedical studies. In recent years, deep learning (DL) models have been started to be applied in the literature for the diagnosis of these diseases. In this study, event-related potentials (ERPs) were obtained from EEG signals obtained as a …

Exploring The Attention Process Differentiation Of Attention Deficit Hyperactivity Disorder (Adhd) Symptomatic Adults Using Artificial Intelligence Onelectroencephalography (Eeg) Signals, Gökhan Güney, Esra Kisacik, Canan Kalaycioğlu, Görkem Saygili

Turkish Journal of Electrical Engineering and Computer Sciences

Attention deficit and hyperactivity disorder (ADHD) onset in childhood and its symptoms can last up till adulthood. Recently, electroencephalography (EEG) has emerged as a tool to investigate the neurophysiological connection of ADHD and the brain. In this study, we investigated the differentiation of attention process of healthy subjects with or without ADHD symptoms under visual continuous performance test (VCPT). In our experiments, artificial neural network (ANN) algorithm achieved 98.4% classification accuracy with 0.98 sensitivity when P2 event related potential (ERP) was used. Additionally, our experimental results showed that fronto-central channels were the most contributing. Overall, we conclude that the attention …

Sensor Approach For Brain Pathophysiology Of Freezing Of Gait In Parkinson's Disease Patients, Juan Sebastian Marquez Jaramillo

FIU Electronic Theses and Dissertations

Parkinson's Disease (PD) affects over 1% of the population over 60 years of age and is expected to reach 1 million in the USA by the year 2020, growing by 60 thousand each year. It is well understood that PD is characterized by dopaminergic loss, leading to decreased executive function causing motor symptoms such as tremors, bradykinesia, dyskinesia, and freezing of gait (FoG) as well as non-motor symptoms such as loss of smell, depression, and sleep abnormalities. A PD diagnosis is difficult to make since there is no worldwide approved test and difficult to manage since its manifestations are widely …

Deep Cellular Recurrent Neural Architecture For Efficient Multidimensional Time-Series Data Processing, Lasitha S. Vidyaratne

Electrical & Computer Engineering Theses & Dissertations

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in …

Statistical Methods To Unravel Cortical Mechanism Of Perception And Response To Auditory Stimuli, Ladan Moheimanian

Legacy Theses & Dissertations (2009 - 2024)

Behavioral responses to auditory stimuli have a critical role in our daily activities. The perception of these stimuli and the generation of appropriate behavioral responses requires the interaction of thousands of neurons in the auditory-motor pathways in the brain. Despite their importance, still many neuroscientific questions about these interactions are remained to be answered. This may result from the limitations of brain recordings as well as statistical methods to analyze brain recordings. In this dissertation, I investigated underlying mechanisms that govern these neural interactions in the auditory-motor pathways using novel statistical techniques applied to the brain recordings from the surface …

Prediction Of Preference And Effect Of Music On Preference: A Preliminary Study On Electroencephalography From Young Women, Bülent Yilmaz, Cengi̇z Gazeloğlu, Fati̇h Altindi̇ş

Turkish Journal of Electrical Engineering and Computer Sciences

Neuromarketing is the application of the neuroscientific approaches to analyze and understand economically relevant behavior. In this study, the effect of loud and rhythmic music in a sample neuromarketing setup is investigated. The second aim was to develop an approach in the prediction of preference using only brain signals. In this work, 19-channel EEG signals were recorded and two experimental paradigms were implemented: no music/silence and rhythmic, loud music using a headphone, while viewing women shoes. For each 10-sec epoch, normalized power spectral density (PSD) of EEG data for six frequency bands was estimated using the Burg method. The effect …

Brain Connectivity Networks For The Study Of Nonlinear Dynamics And Phase Synchrony In Epilepsy, Hoda Rajaei

FIU Electronic Theses and Dissertations

Assessing complex brain activity as a function of the type of epilepsy and in the context of the 3D source of seizure onset remains a critical and challenging endeavor. In this dissertation, we tried to extract the attributes of the epileptic brain by looking at the modular interactions from scalp electroencephalography (EEG). A classification algorithm is proposed for the connectivity-based separation of interictal epileptic EEG from normal. Connectivity patterns of interictal epileptic discharges were investigated in different types of epilepsy, and the relation between patterns and the epileptogenic zone are also explored in focal epilepsy.

A nonlinear recurrence-based method is …

Detecting Slow Wave Sleep And Rapid Eye Movement Stage Using Cortical Effective Connectivity, Aminollah Glorou, Ali Sheikhani, Ali Motie Nasrabadi, Mohammad Reza Saebipour

Turkish Journal of Electrical Engineering and Computer Sciences

In recent neuroimaging research, there has been considerable interest in identifying neuromarkers of sleep. Automatic slow wave sleep (SWS) and rapid eye movement (REM) are two known phases of sleep. However, the level by which those changes contribute to brain interactions has not been well characterized. In recent years, it has been shown that brain connectivity measuring can be helpful in investigation of behavioral states of the brain. By considering the fact that brains have different states in different stages of sleep, the present work employs effective connectivity and machine-learning analysis to quantify and classify SWS and REM stages of …

Analysis Of Arm Movement Prediction By Using The Electroencephalography Signal, Reza Darmakusuma, Ary Setijadi Prihatmanto, Adi Indrayanto, Tati Latifah Mengko, Lidwina Ayu Andarini, Achmad Furqon Idrus

Makara Journal of Technology

Various technological approaches have been developed in order to help those people who are unfortunate enough to be afflicted with different types of paralysis which limit them in performing their daily life activities independently. One of the proposed technologies is the Brain-Computer Interface (BCI). The BCI system uses electroencephalography (EEG) which is generated by the subject’s mental activity as input, and converts it into commands. Some previous experiments have shown the capability of the BCI system to predict the movement intention before the actual movement is onset. Thus research has predicted the movement by discriminating between data in the “rest” …

Verification Of Emotion Recognition From Facial Expression, Yanjia Sun

Dissertations

Analysis of facial expressions is an active topic of research with many potential applications, since the human face plays a significant role in conveying a person’s mental state. Due to the practical values it brings, scientists and researchers from different fields such as psychology, finance, marketing, and engineering have developed significant interest in this area. Hence, there are more of a need than ever for the intelligent tool to be employed in the emotional Human-Computer Interface (HCI) by analyzing facial expressions as a better alternative to the traditional devices such as the keyboard and mouse.

The face is a window …

A Comparative Analysis Of Feature Extraction Techniques For Eeg Signals From Alzheimer Patients, Ramya Priya Mudhiganti

Electrical Engineering Theses

This research deals with the study of Alzheimer Disease (AD). Electroencephalogram (EEG) signal is a clinical tool for the diagnosis and detection of AD. EEG signals are analyzed for the diagnosis of AD applying several linear and non-linear methods of signal processing. This work studies and implements several measures of EEG signal complexity and then compares the complexity features measured or extracted from EEG signals. Time domain analysis of EEG signals is performed using several signal processing techniques such as higher order moments, entropies and fractal dimension calculation using fractal analysis. Frequency domain analysis of EEG signals is performed using …

Hybrid Sensing And Adaptive Control For Direct Brain Actuation Of Artificial Limbs, Christopher Aasted

Electronic Theses and Dissertations

Developing a non-invasive direct brain control of artificial limbs is both challenging and desirable. Such a sensory and control system, if successful, will have a profound impact on the disabled. In this dissertation, we present the design and development of a non-invasive, hybrid sensory system, which uses near-infrared spectroscopy (NIRS) and electroencephalography (EEG) to measure brain activity with simultaneous electromyography (EMG) to provide feedback data in a healthy limb. Through the combination of these sensory techniques, we have successfully trained a control system capable of mapping brain activity onto muscle actuation. The design of a control algorithm capable of automatic …

Analysis Of Electroencephalogram Signals For The Identification Of Mental Tasks, My Thy Thi Tran

Electrical & Computer Engineering Theses & Dissertations

Electroencephalogram (EEG) signals can be used for implicit communication such as to control robots or medical equipment by brain activity or to detect an individual's intentions of committing premeditated crimes. An EEG based brain-computer interface allows paralyzed patients to express their thoughts. However, biological and technical artifacts heavily interfered with EEG signals due to blinking of the eyes, muscle activities and line noise. Sometimes the noise interference due to signal artifacts becomes more prominent than the information content. This thesis investigates novel feature extraction methodologies in EEG signals to represent different thought processes and employs neural network-based pattern classification techniques …