Elbow Joints for Upper-Limb Prosthesis: Analysis of Biomedical EEG Signals using Discrete Wavelet Transform
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : Umashankar G, Vimala Juliet A, Hari Krishnan G
|DOI : 10.14445/22315381/IJETT-V70I7P220|
How to Cite?
Umashankar G, Vimala Juliet A, Hari Krishnan G, "Elbow Joints for Upper-Limb Prosthesis: Analysis of Biomedical EEG Signals using Discrete Wavelet Transform" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 190-197, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P220
Signal classification is an essential feature in cognitive science, which separates large datasets into classes based on frequency. This research was conducted to analyze the brain signals by signal classification using a convolutional neural network (CNN) to obtain the required frequency spectrum. The signals can be used for upper-limb prostheses, especially elbow joint applications. The feature extraction process is an important step in brain signal classification. During the current study, electroencephalography (EEG) signals are extracted using a 10-20 electrode system from the flexion and extension movement of the elbow joints. Using MATLAB tools, it is done through a user interface. The expected performance is obtained as an exact parameter analysis, e.g., the classifier's precision, simplicity, and sensitivity using a convolution neural network should be connected as a benchmark for applications for the upper-limb prosthesis.
Convolutional Neural Network, Elbow joint, Electroencephalography, Prosthesis, Signals.
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