EEG Measurements with Compressed Sensing Utilizing EEG Signals as the Basis Matrix

The use of compressed sensing (CS) to achieve low-power consumptions in electroencephalogram (EEG) measurement devices has attracted considerable research interest. However, a signal processing issue in utilizing CS is the trade-off between the compression ratio (CR), reconstruction accuracy, and reconstruction time. In this study, we developed a method that resulted in a shortened reconstruction time and a high reconstruction accuracy with a high 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 with an original time length of 1 s could be recovered in only approximately 26 ms, and an average normalized mean square error of 0.11 was achieved at a CR of 5.