Abstract

Electroencephalogram (EEG) plays an essential role in analysing and recognizing brain-related diseases. EEG has been increasingly used as a new type of biometrics in person identification and verification systems. These EEG-based systems are important components in applications for both police and civilian works, and both areas process a huge amount of EEG data. Storing and transmitting these huge amounts of data are significant challenges for data compression techniques. Lossy compression is used for EEG data as it provides a higher compression ratio (CR) than lossless compression techniques. However, lossy compression can negatively influence the performance of EEG-based person identification and verification systems via the loss of information in the reconstructed data. To address this, we propose introducing performance measures as additional features in evaluating lossy compression techniques for EEG data. Our research explores if a common value of CR exists for different systems using datasets with lossy compression that could provide almost the same system performance with those using datasets without lossy compression. We performed experiments on EEG-based person identification and verification systems using two large EEG datasets, CHB MIT Scalp and Alcoholism, to investigate the relationship between standard lossy compression measures and our proposed system performance measures with the two lossy compression techniques, discrete wavelet transform—adaptive arithmetic coding and discrete wavelet transform—set partitioning in hierarchical trees. Our experimental results showed a common value of CR exists for different systems, specifically, 70 for person identification systems and 50 for person verification systems.

中文翻译：

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脑电信号有损压缩的生物识别系统性能测量

摘要

脑电图 (EEG) 在分析和识别脑相关疾病方面发挥着重要作用。EEG 已越来越多地用作人员识别和验证系统中的一种新型生物识别技术。这些基于 EEG 的系统是警察和民用工程应用中的重要组成部分，并且这两个领域都处理大量 EEG 数据。存储和传输这些海量数据是数据压缩技术面临的重大挑战。有损压缩用于 EEG 数据，因为它提供比无损压缩技术更高的压缩比 (CR)。然而，有损压缩会通过重建数据中的信息丢失对基于 EEG 的人员识别和验证系统的性能产生负面影响。为了解决这个问题，我们建议在评估 EEG 数据的有损压缩技术时引入性能度量作为附加功能。我们的研究探讨了使用有损压缩数据集的不同系统是否存在 CR 的共同值，这可以提供与使用没有有损压缩的数据集几乎相同的系统性能。我们使用两个大型 EEG 数据集 CHB MIT Scalp 和酒精中毒对基于 EEG 的人员识别和验证系统进行了实验，以研究标准有损压缩测量与我们使用两种有损压缩技术（离散小波变换）提出的系统性能测量之间的关系——自适应算术编码和离散小波变换——层次树中的集合划分。