The use of compressed sensing (CS) to achieve low-power consumption in electroencephalogram (EEG) measurement devices has attracted considerable research interest. In this study, we developed a method that resulted in a shortened reconstruction time and a high reconstruction accuracy with a high compression ratio (CR) by utilizing selected EEG signals. When EEG signals were sorted using the mean frequency and only the most frequently occurring EEG signals were used in the basis matrix, a compressed EEG signal could be recovered in only 26 ms, and an average normalized mean square error of 0.11 was achieved at CR = 5.