Suhaimi, N.S. and Yusoff, M.Z. and Saad, M.N.M. (2022) Artificial Neural Network Analysis On Motor Imagery Electroencephalogram. In: UNSPECIFIED.
Full text not available from this repository.Abstract
Research on brain signal analysis has been performed decades ago. This research field has benefited other industries such as health and analytics. Various analysis methods either conventional or intelligent methods had been explored in ensuring the best application was produced. In this project, a secondary dataset from motor cortex brain signals had been utilized and the dataset is captured by a non-invasive method using an electroencephalogram (EEG) tool. The dataset is then proposed to be extracted and classified using the Deep Learning Neural Network method. High accuracy and sensitivity of model analysis are expected as the outcome of the project. Besides, statistical analysis had been conducted to observe the significance between electrode placement and the output of the dataset. Thus, the Artificial Neural Network model was observed as the final finding. © 2022 IEEE.
Item Type: | Conference or Workshop Item (UNSPECIFIED) |
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Additional Information: | cited By 1; Conference of 5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022 ; Conference Date: 6 August 0202 Through 8 August 0202; Conference Code:183507 |
Uncontrolled Keywords: | Deep neural networks; Electroencephalography; Learning systems; Noninvasive medical procedures; Sensitivity analysis, Artificial neural network analysis; Brain signals; Deep learning neural network model.; Learning neural networks; Motor cortex brain signal; Motor imagery; Motor-cortex; Neural network model; Research fields; Signals analysis, Neural network models |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 19 Dec 2023 03:23 |
Last Modified: | 19 Dec 2023 03:23 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/17445 |