AUTHOR=Gu Yi , Hua Lei 

TITLE=A Novel Smart Motor Imagery Intention Human-Computer Interaction Model Using Extreme Learning Machine and EEG Signals

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 15 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.685119

DOI=10.3389/fnins.2021.685119

ISSN=1662-453X

ABSTRACT=The brain is the central nervous system that governs human activities. However, in modern society, more and more diseases threaten the health of the brain and nerves and spinal cord, making the human brain unable to conduct normal information interaction with the outside world. The rehabilitation training of the brain-computer interface can promote the nerve repair of the sensorimotor cortex in patients with brain diseases. Therefore, the research of brain-computer interface for motor imaging is of great significance for patients with brain diseases to restore motor function. Due to the characteristics of non-stationary, nonlinear and individual differences of EEG signals, there are still many difficulties in the analysis and classification of EEG signals at this stage. This article aims at the classification of motor imagery EEG signals. The regression method of Extreme Learning Machine is used to approximate class labels to achieve the purpose of classification. Specifically, first, the EEG image signal is collected and preprocessed. Second, the signal after feature extraction is input into the ELM model for classification. The classification result is the intention recognition result. Two sets of BCI competition data in the BCI competition public database are used to verify the validity of the model. The experimental results show that the ELM model has achieved a classification accuracy of 0.7832 in the classification task of Data Sets IIb, which is higher than other comparison algorithms, and shows universal applicability among different subjects. In addition, the average recognition rate of this model in the Data Sets IIIa classification task reaches 0.8347, which has obvious advantages compared with the comparative classification algorithm. The classification effect is smaller than the classification effect obtained by the champion algorithm of the same project, which has certain reference value.