Research on Brain Signals Classification Based on Deep Learning.

With the continuous expansion of brain-computer communication, the precise identification of brain signals has become an essential task for brain-computer equipment. However, existing classification methods are primarily concentrated on the extraction features of brain signals and obtain unacceptable performance when directly used the model to a new brain signals data, which is caused by the different people has extraordinary brain signals. In this work, we utilize the deep learning methods not only extract the features of brain signals but also learn the order information of brain signals, which can satisfy the universal brain signals. Indeed, we utilize the classification features dimension distance loss function to optimize the proposed model and enhance the classification accuracy and we compare our model with existing classification methods to evaluate proposed model. From our extensive experimental results and analysis, we can conclude that our model can achieve the classification of brain signals with the reasonable accuracy and acceptable costs.