.png){kind=icon}
返回
Task sensitivity in EEG biometric recognition
.png){kind=icon}
OA
AI
DOI：10.1007/s10044-016-0569-4.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This work explores the sensitivity of electroencephalographic-based biometric recognition to the type of tasks required by subjects to perform while their brain activity is being recorded. A novel wavelet-based feature is used to extract identity information from a database of 109 subjects who performed four different motor movement/imagery tasks while their data were recorded. Training and test of the system was performed using a number of experimental protocols to establish if training with one type of task and tested with another would significantly affect the recognition performance. Also, experiments were conducted to evaluate the performance when a mixture of data from different tasks was used for training. The results suggest that performance is not significantly affected when there is a mismatch between training and test tasks. Furthermore, as the amount of data used for training is increased using a combination of data from several tasks, the performance can be improved. These results indicate that a more flexible approach may be incorporated in data collection for EEG-based biometric systems which could facilitate their deployment and improved performance.
Keyword:
EEG
Biometrics
Identification
Verification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2
论文数:
1.9K
被引数:
1.9K
机构
引用论文
.png){kind=icon}
分享收藏分享收藏
查看更多内容