Densely Feature Fusion Based on Convolutional Neural Networks for Motor Imagery EEG Classification

Abstract

Electroencephalogram (EEG) signals have been used in the Brain-computer interface (BCI) technology to implement direct communication between the human body and the outside world, which has important application prospects in the fields of cognitive science and medical rehabilitation. In recent years, deep learning technology has achieved remarkable results in the BCI system, especially the using of convolutional neural networks (CNNs) frameworks for the identification and analysis of motor imagery signals. However, practical applications are limited by the complex process of data representation, and the end-to-end method will deteriorate the recognition results. In this paper, we propose a densely feature fusion convolutional neural networks (DFFN). Combining the morphological information of EEG signals, we propose two data representation methods with low complexity, then design and optimize the densely feature fusion network framework for this form of inputs. DFFN considers the correlation between adjacent layers and cross layer features, which reduces the information loss in the process of convolutional operation and considers the local and global characteristics of the network. The simulation results showed that our network improve classification results by 5% in the BCI competition IV-2a data set compare to the ordinary CNNs framework. In order to verify the practical application of the densely feature fusion network framework, we train an adaptive global model method. The results of average classification are close to the baseline approach of the subject-dependent model and better than others.