文章基本信息

标题：A Personalized Compression Method for Steady-State Visual Evoked Potential EEG Signals
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作者：Sitao Zhang ; Kainan Ma ; Yibo Yin 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2022
卷号：13
期号：4
页码：186
DOI：10.3390/info13040186
语种：English
出版社：MDPI Publishing
摘要：As an informative electroencephalogram (EEG) signal, steady-state visual evoked potential (SSVEP) stands out from many paradigms for application in wireless wearable devices. However, its data are usually enormous, occupy too many bandwidth sources and require immense power when transmitted in the raw data form, so it is necessary to compress the signal. This paper proposes a personalized EEG compression and reconstruction algorithm for the SSVEP application. In the algorithm, to realize personalization, a primary artificial neural network (ANN) model is first pre-trained with the open benchmark database towards BCI application (BETA). Then, an adaptive ANN model is generated with incremental learning for each subject to compress their individual data. Additionally, a personalized, non-uniform quantization method is proposed to reduce the errors caused by compression. The recognition accuracy only decreases by 3.79% when the compression rate is 12.7 times, and is tested on BETA. The proposed algorithm can reduce signal loss by from 50.43% to 81.08% in the accuracy test compared to the case without ANN and uniform quantization.