Comparative study of EEG motor imagery classification based on DSCNN and ELM

With the popularization of brain-computer interface (BCI) technology, the research on intention recognition of motor imagery (MI) in electroencephalogram (EEG) has turned to the latest research points. The BCI-based system can provide a powerful rehabilitation guarantee for patients with stroke and spinal cord injury. However, EEG signals have certain complexity, which is easily interfered by other noises, resulting that it is still not enough to provide some better practical application scenarios. In this paper, an improved framework has been proposed through deep separation convolution neural network (DSCNN) and extreme learning machine (ELM) to address the recognition rate of patients' motor intention. First of all, the collected one-dimensional time series EEG signals are preprocessed into a two-dimensional grid containing spatial and temporal features. Afterwards, the DSCNN is utilized to extract the temporal features and spatial features, respectively. Thirdly, the ELM classifier is utilized to classify five kinds of MI actions according to the extracted temporal and spatial features. Experimental results indicate that the presented framework can achieve an excellent intention recognition rate of 97.88% through the public EEGMMIDB datasets. Moreover, the training time was greatly shortened from 13h30min to 9h10min with a reduction rate of about 32% under the same hardware configuration, which is superior to some advanced models. Therefore, the proposed idea not only accelerates the training speed of the model, but also can boost the application research of BCI based rehabilitation efficiently.