EEG Measurements with Compressed Sensing Utilizing EEG Signals as the Basis Matrix
EEG Measurements with Compressed Sensing Utilizing EEG Signals as the Basis Matrix
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DOI：
10.1109/iscas46773.2023.10181710
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
D. Kanemoto;Tetsuya Hirose
中科院分区：
文献类型：
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作者：
D. Kanemoto;Tetsuya Hirose
利用压缩感知技术实现脑电（EEG）测量设备的低功耗已经引起了广泛的研究兴趣。然而，利用CS的信号处理问题是压缩比（CR）、重建精度和重建时间之间的权衡。在这项研究中，我们开发了一种方法，导致在一个缩短的重建时间和高CR重建精度高，利用选定的EEG信号。当使用平均频率对EEG信号进行排序并且在基矩阵中仅使用最频繁出现的EEG信号时，可以在仅约26 ms内恢复原始时间长度为1 s的压缩EEG信号，并且在CR为5时实现平均归一化均方误差为0.11。
The use of compressed sensing (CS) to achieve low-power consumptions in electroencephalogram (EEG) mea-surement 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.