Deep Learning Based Imaginary Finger Control Detection Algorithm

抄録

Conventionally, the brain signals were analysed manually by the neuroscientists on how the brain signals reacts in proportion with the human body. However, this process is very time consuming and unreliable. Therefore, we have proposed a brain signal detection system based on deep learning algorithm in response to the output of EEG device on the imagery finger movements. These fingers include thumb, index, middle, ring and little of right hand. In this study, 4 Convolutional Neural Network (CNN) classification models were developed. These 4 CNN models are different in terms of the pre-processing requirements and the neural network architecture. The best results for offline classification obtained in this project are 69.07% and 82.83% respectively in terms of average accuracy from 6-class and 2-class tests. Moreover, this project has also developed a proof of concept for applying the trained models in online or real-time classification.