基于边缘响应叠加的cVEP BCI激励设计新方法
New Approach for Designing cVEP BCI Stimuli Based on Superposition of Edge Responses
摘要：
本研究的目的是开发一种新的方法来设计刺激序列cVEP脑机接口的基础上的行为和属性的实际EEG响应的视觉系统编码的视觉刺激的实验研究，这样的训练时间减少，目标的可能数量增加。来自8个枕骨部位的EEG以每通道2000个样本/秒记录，响应于在具有60 Hz刷新率的计算机监视器上呈现的视觉刺激。通过160次信号平均获得长视觉刺激脉冲的起始和偏移EEG响应。这些边缘响应被用来预测任意刺激序列使用叠加原理的EEG响应。一个BCI拼写器，它利用由这一原则产生的目标模板也实现和测试。结果发现，某些短刺激模式可以准确地预测叠加原理。与将叠加原理应用于常规m序列和随机生成的序列时相比，通过这些最佳模式的组合构建的BCI序列产生更高的准确度（95.9%）和ITR（57.2 bpm）。BCI应用程序的训练时间仅涉及边缘响应的采集，并且不到4分钟，并且可能有大量的序列。这是第一个研究中，cVEP BCI序列的设计基于通过观察几个刺激模式的实际大脑反应获得的约束。
英文摘要：
The purpose of this study is to develop a new methodology for designing stimulus sequences for cVEP BCI based on experimental studies regarding the behavior and the properties of the actual EEG responses of the visual system to coded visual stimuli, such that training time is reduced and the possible number of targets is increased. EEG from 8 occipital sites is recorded with 2000 samples/sec per channel, in response to visual stimuli presented on a computer monitor with 60Hz refresh rate. Onset and offset EEG responses to long visual stimulus pulses are obtained through 160-trial signal averaging. These edge responses are used to predict the EEG responses to arbitrary stimulus sequences using the superposition principle. A BCI speller which utilizes the target templates generated by this principle is also implemented and tested. It is found that certain short stimulus patterns can be accurately predicted by the superposition principle. BCI sequences that are constructed by combinations of these optimal patterns yield higher accuracy (95.9%) and ITR (57.2 bpm) compared to when the superposition principle is applied to conventional m-sequences and randomly generated sequences. Training time for the BCI application involves only the acquisition of the edge responses and is less than 4 minutes, and a huge number of sequences is possible. This is the first study in which cVEP BCI sequences are designed based on constraints obtained by observing the actual brain responses to several stimulus patterns.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
