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Biomedical Engineering and Bioengineering Commons™
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Full-Text Articles in Biomedical Engineering and Bioengineering
A Dynamical Systems Approach To Characterizing Brain–Body Interactions During Movement: Challenges, Interpretations, And Recommendations, Derek C. Monroe, Nathaniel T. Berry, Peter C. Fino, Christopher K. Rhea
A Dynamical Systems Approach To Characterizing Brain–Body Interactions During Movement: Challenges, Interpretations, And Recommendations, Derek C. Monroe, Nathaniel T. Berry, Peter C. Fino, Christopher K. Rhea
Rehabilitation Sciences Faculty Publications
Brain–body interactions (BBIs) have been the focus of intense scrutiny since the inception of the scientific method, playing a foundational role in the earliest debates over the philosophy of science. Contemporary investigations of BBIs to elucidate the neural principles of motor control have benefited from advances in neuroimaging, device engineering, and signal processing. However, these studies generally suffer from two major limitations. First, they rely on interpretations of ‘brain’ activity that are behavioral in nature, rather than neuroanatomical or biophysical. Second, they employ methodological approaches that are inconsistent with a dynamical systems approach to neuromotor control. These limitations represent a …
Deep Cellular Recurrent Neural Architecture For Efficient Multidimensional Time-Series Data Processing, Lasitha S. Vidyaratne
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 …
Analysis Of Electroencephalogram Signals For The Identification Of Mental Tasks, My Thy Thi Tran
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 …