Before the advancement of deep learning technology, the brain signals are to be analysed manually by the neuroscientists on how the brain signals reacts in proportion with the human body. This process is very time consuming and unreliable. Therefore, this project aims to develop 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. There are 4 CNN classification models being developed in this project. They differ with each other 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.
