Abstract:The study of electroencephalogram signals based on brain-computer interface gets more and more attention, but the acquisition of the electroencephalogram signals by using traditional electrode cap cannot be accepted by most users because of the use of electrode cap and cement. So we acquire electroencephalogram signals by using independent electrode. However using independent electrode to acquire electroencephalogram signals makes big interference, and the signal is not stable. In order to quickly and efficiently extract brain signal characteristics, low pass filtering is used to remove power frequency interference, and we use independent component analysis (ICA) to realize the separation of eye artifact and electroencephalogram signals. Then we achieve the identification of eye artifact through the horizontal eye electrical threshold settings, vertical eye electrical threshold settings and spatial distribution characteristics of individual components in the location of the brain. Then we can use the energy of β-band and sample entropy to measure the concentration in this paper. Simulation results show that both the energy and entropy are positively related with the concentration. Experimental measurement is on the basis of patented algorithm of Neuroscan. Compare the two algorithms with the algorithm of Neuroscan respectively, the results show that the correlation coefficient between the entropy and the value of attention of Neuroscan increases 26% comparing with the correlation coefficient between the energy and the value of attention of Neuroscan, which indicates that sample entropy can accurately track the change of attention and has a better effect in the feature extraction.