Abstract | ||
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In order to explore the effect of low frequency stimulation on pupil size and electroencephalogram (EEG), we presented subjects with 1-6Hz black-and-white-alternating flickering stimulus, and compared the differences of signal-to-noise ratio (SNR) and classification performance between pupil size and visual evoked potentials (VEPs). The results showed that the SNR of the pupillary response reached the highest at 1Hz (17.19 +/- 0.10dB) and 100% accuracy was obtained at 1s data length, while the performance was poor at the stimulation frequency above 3Hz. In contrast, the SNR of VEPs reached the highest at 6Hz (18.57 +/- 0.37dB), and the accuracy of all stimulus frequencies could reach 100%, with the minimum data length of 1.5s. This study lays a theoretical foundation for further implementation of a hybrid brain-computer interface (BCI) that integrates pupillometry and EEG. |
Year | DOI | Venue |
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2020 | 10.1109/EMBC44109.2020.9175893 | 42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20 |
DocType | Volume | ISSN |
Conference | 2020 | 1557-170X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Jiang Lu | 1 | 10 | 3.70 |
Xiaoyang Li | 2 | 9 | 0.82 |
Yijun Wang | 3 | 308 | 46.68 |
Weihua Pei | 4 | 64 | 13.18 |
Xiaorong Gao | 5 | 598 | 81.99 |
Hongda Chen | 6 | 99 | 20.06 |