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Description
Electromyography (EMG) and Electroencephalography (EEG) are techniques used to detect electrical activity produced by the human body. EMG detects electrical activity in the skeletal muscles, while EEG detects electrical activity from the scalp. The purpose of this study is to capture different types of EMG and EEG signals and to determine if the signals can be distinguished between each other and processed into output signals to trigger events in prosthetics. Results from the study suggest that the PSD estimates can be used to compare signals that have significant differences such as the wrist, scalp, and fingers, but it cannot fully distinguish between signals that are closely related, such as two different fingers. The signals that were identified were able to be translated into the physical output simulated on the Arduino circuit.
ContributorsJanis, William Edward (Author) / LaBelle, Jeffrey (Thesis director) / Santello, Marco (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-12
Description
Electromyography (EMG) is an extremely useful tool in extracting control signals from the human body. Needle electromyography is the current standard for obtaining superior quality muscle signals and obtaining signals corresponding to individual muscles. However, needle EMG faces many problems when converting from the laboratory to marketable devices, specifically in home devices. Many patients have issues with needles and the extra care required of needle EMG is prohibitive. Therefore, a surface EMG device that can obtain clear signals from individual muscles would be valuable to many markets in the development of next generation in home devices. Here, signals from surface EMG were analyzed using a low noise EMG evaluation system (RHD 2000; Intan Technologies). The signal to noise ratio (SNR) was calculated using MatLab. The average SNR is 4.447 for the Extensor Carpi Ulnaris, and 7.369 for the Extensor Digitorum Communis. Spectral analysis was performed using the Welch approach in MatLab. The power spectrum indicated that low frequency signals dominate the EMG of small hand muscles. Also, harmonic bands of 60Hz noise were present as part of the signal which should be accounted for with filters in future iterations of the testing method. Provided is evidence that strong, independent signals were acquired and could be used in further application of surface EMG corresponding to lifting of the fingers.
ContributorsSnyder, Joshua Scott (Author) / Muthuswamy, Jit (Thesis director) / Buneo, Christopher (Committee member) / Harrington Bioengineering Program (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05