CNN-Based Personal Identification System Using Resting State Electroencephalography
| Autor: | Yongdong Fan, Xiaoyu Shi, Qiong Li |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Biometry
General Computer Science Article Subject General Mathematics General Neuroscience Computer applications to medicine. Medical informatics R858-859.7 Records Neurosciences. Biological psychiatry. Neuropsychiatry Electroencephalography General Medicine ComputingMethodologies_PATTERNRECOGNITION Research Design Humans Neural Networks Computer RC321-571 Research Article |
| Zdroj: | Computational Intelligence and Neuroscience Computational Intelligence and Neuroscience, Vol 2021 (2021) |
| ISSN: | 1687-5273 |
| Popis: | As a biometric characteristic, electroencephalography (EEG) signals have the advantages of being hard to steal and easy to detect liveness, which attract researchers to study EEG-based personal identification technique. Among different EEG protocols, resting state signals are the most practical option since it is more convenient to operate than the other protocols. In this paper, a personal identification system based on resting state EEG is proposed, in which data augmentation and convolutional neural network are combined. The cross-validation is performed on a public database of 109 subjects. The experimental results show that when only 14 EEG channels and 0.5 seconds data are employed, the average accuracy and average equal error rate of the system can reach 99.32% and 0.18%, respectively. Compared with some existing representative works, the proposed system has the advantages of short acquisition time, low computational complexity, and rapid deployment using market available low-cost EEG sensors, which further advances the implementation of practical EEG-based identification systems. |
| Databáze: | OpenAIRE |
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