Wang Luzhou, Wang Suogang, Kuang Guangtao
Institute of Biomedical Engineering , Tianjin Medical University, Tianjin 300070, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2013 Jun;30(3):469-75.
In the traditional P300 brain-computer interface (BCI) system, the electroencephalogram (EEG) signals can only provide limited information with a low signal-to-noise ratio. A BCI paradigm under visual stimulus was proposed in our study aiming to effectively activate the related brain areas and response signal while dealing with specific cognitive task (mental arithmetic task), so as to enhance the EEG signals. The result was compared with the traditional P300 counting task paradigm. Then the collected EEG data were preprocessed including extracting signal features with coherent averaging method, and analyzing the influences of different experimental paradigms on main components of event related potential (ERP). In the improved paradigm experiments the average increasing rate of P300 amplitude was 6. 83MV (73. 94%). The brain activity from 400ms was more active and lasted longer. Besides, unlike traditional counting task, mental arithmetic task appeared to have apparent activation at 650ms. The results showed that the improved paradigm could activate the related brain areas better and enhance the characteristics of signal. This provides a new system paradigm for BCI.
在传统的P300脑机接口（BCI）系统中，脑电图（EEG）信号只能提供有限的信息，且信噪比很低。我们的研究提出了一种视觉刺激下的BCI范式，旨在在处理特定认知任务（心算任务）时有效激活相关脑区并产生响应信号，从而增强EEG信号。将结果与传统的P300计数任务范式进行比较。然后对采集到的EEG数据进行预处理，包括用相干平均法提取信号特征，并分析不同实验范式对事件相关电位（ERP）主要成分的影响。在改进范式实验中，P300波幅的平均增加率为6.83μV（73.94%）。400毫秒后的大脑活动更活跃且持续时间更长。此外，与传统计数任务不同，心算任务在650毫秒时似乎有明显的激活。结果表明，改进后的范式能更好地激活相关脑区并增强信号特征。这为BCI提供了一种新的系统范式。
