可视化快速呈现
脑-机接口
计算机科学
接口(物质)
信息传递
可视化
语音识别
人工智能
感知
脑电图
神经科学
电信
心理学
最大气泡压力法
气泡
并行计算
作者
Shayan Jalilpour,Sepideh Hajipour Sardouie,Amir Mohammad Mijani
标识
DOI:10.1016/j.cmpb.2020.105326
摘要
Steady-state visual evoked potential (SSVEP) and rapid serial visual presentation (RSVP) are useful methods in the brain-computer interface (BCI) systems. Hybrid BCI systems that combine these two approaches can enhance the proficiency of the P300 spellers. In this study, a new hybrid RSVP/SSVEP BCI is proposed to increase the classification accuracy and information transfer rate (ITR) as compared with the other RSVP speller paradigms. In this paradigm, RSVP (eliciting a P300 response) and SSVEP stimulations are presented in such a way that the target group of characters is identified by RSVP stimuli, and the target character is recognized by SSVEP stimuli. The proposed paradigm achieved accuracy of 93.06%, and ITR of 23.41 bit/min averaged across six subjects. The new hybrid system demonstrates that by using SSVEP stimulation in Triple RSVP speller paradigm, we could enhance the performance of the system as compared with the traditional Triple RSVP paradigm. Our work is the first hybrid paradigm in RSVP spellers that could obtain the higher classification accuracy and information transfer rate in comparison with the previous RSVP spellers.
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