Nicoletti D W, Onaral B
Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA.
Ann Biomed Eng. 1991;19(1):1-14. doi: 10.1007/BF02368458.
The large volume of digital data and the demanding processing task involved in electroencephalogram (EEG) analysis place stringent requirements on computer resources in terms of data transfer, computation speed, and temporary or permanent storage. The reduction of the database to a manageable size is therefore necessary for economical use of transmission channels and the storage media. The two criteria, waveform reproducibility and processing applications, must be analyzed and optimized in terms of signal-to-noise ratio (SNR) using the various factors affecting the coding. This analysis and optimization can become cumbersome, and a specialized workstation has been developed specifically for analysis of digital coding. Our interest in data compression stems from the study of the feasibility of predicting pilots' acceleration (Gz) tolerance during flight by processing both their uncoded and coded EEG.
脑电图(EEG)分析中大量的数字数据以及艰巨的处理任务,对计算机资源在数据传输、计算速度以及临时或永久存储方面提出了严格要求。因此,为了经济地使用传输通道和存储介质,有必要将数据库缩减到可管理的大小。必须根据影响编码的各种因素,从信噪比(SNR)方面分析和优化波形再现性和处理应用这两个标准。这种分析和优化可能会变得繁琐,为此专门开发了一种用于数字编码分析的专业工作站。我们对数据压缩的兴趣源于通过处理未编码和编码的脑电图来预测飞行员飞行过程中加速度(Gz)耐受性的可行性研究。