摘要
稳态视觉诱发电位(SSVEP)脑-机接口(BCI)是无创脑-机接口研究领域的3种主流范式之一。使用传统SSVEP-BCI有训练算法需预先采集大量被试者的脑电数据用于训练模型,不利于脑-机接口技术的推广。为减少训练数据量,提出了一种基于多码元时分编码的SSVEP-BCI少训练检测算法,利用相同码元刺激在不同码字刺激之间的可复用性,在保证较高的信号识别准确率和信息传输速率的前提下,以少量脑电训练数据即可识别出大量备选目标,有望提升SSVEP-BCI的实际应用价值。
Steady-state visual-evoked potential(SSVEP) brain computer interface(BCI) is one of the three mainstream paradigms in the field of non-invasive brain-computer interface research. The traditional SSVEP-BCI training algorithm has to collect a large number of electroencephalogram data before training, which greatly increases the cost of SSVEP-BCI. To reduce the training time of SSVEP-BCI, we propose a less-training detection algorithm based on multi-symbol time-division coding. This algorithm takes advantage of the transferable symbol response between code responses and uses a small amount of data to identify a large number of targets with high recognition accuracy and information transmission rate, which can hopefully enhance the overall performance of SSVEP-BCI.
作者
张舒玲
杨晨
张洪欣
叶晓晨
ZHANG Shuling;YANG Chen;ZHANG Hongxin;YE Xiaochen(School of Electronic Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China;Beijing Key Laboratory of Work Safety Intelligent Monitoring,Beijing University of Posts and Telecommunications,Beijing 100876,China)
出处
《北京邮电大学学报》
EI
CAS
CSCD
北大核心
2022年第6期40-45,共6页
Journal of Beijing University of Posts and Telecommunications
基金
国家自然科学基金项目(62006024)
航空科学基金项目(2019ZG073001)。
关键词
稳态视觉诱发电位
脑-机接口
多码元
时分编码
少训练
steady-state visual evoked potential
brain-computer interface
multi-symbol
time division coding
less training