Artificial Neural Network Analysis On Motor Imagery Electroencephalogram

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.