Abstract：One of the key issues in constructing a brain-computer interface is the single-trial estimation of its communication carriers.The support vector machine was exploited to do the estimation of single-trial VEP evoked by imitating-natural-reading.In order to get a better accuracy of classification,we investigated how the classification accuracy was affected by the selection of signal length and interval.The results show that the longer the length of signals,the better the accuracy of classification.The accuracy approaches to maximum value when the length of signal is up to 300 ms.The selection of signal interval also played a key role in classification.The best accuracy appeared at the interval about 250~350 ms after the target stimulus onset.The works are essential for boosting up the speed of whole BCI system.
收稿日期: 2006-02-25
引用本文:
官金安,王艳凤,陈亚光. 特征筛选对脑-机接口信号单次提取精度的影响[J]. , 2006, 45(2): 0-0.
官金安,王艳凤,陈亚光. Selection of features in single-trial BCI signal estimation. , 2006, 45(2): 0-0.
