Sakamoto Yuya, Aono Masaki
Department of Information and Computer Sciences, Toyohashi University of Technology, Toyohashi, Aichi 441-8580, Japan.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:567-70. doi: 10.1109/IEMBS.2009.5334054.
To realize Brain Computer Interface, a recording electroencephalogram (EEG) and determining whether or not P300 is evoked by the presented stimulus have become increasingly important. Using the machine learning method for this classification is effective, but constructing feature vectors with all data points might result in very high-dimensional data. Because such redundant features are undesirable from the viewpoint of computation and classification performance, EEG has been downsampled in several studies. In the present study, we propose a new downsampling method aiming at the improvement of P300 classification accuracy. In particular, each single trial EEG is segmented at non-uniform intervals and then averaged in each segment. The segmentation is decided in such a way that the degree of separating two classes from training data is increased by applying a time series segmentation algorithm. Our experiment using the BCI Competition III P300 Speller paradigm data set demonstrated that our method resulted in higher accuracy than traditional downsampling methods.
为了实现脑机接口，记录脑电图（EEG）并确定所呈现的刺激是否诱发P300变得越来越重要。使用机器学习方法进行这种分类是有效的，但用所有数据点构建特征向量可能会导致数据维度非常高。由于从计算和分类性能的角度来看，这种冗余特征是不可取的，因此在一些研究中对脑电图进行了下采样。在本研究中，我们提出了一种新的下采样方法，旨在提高P300分类准确率。特别是，每个单次试验脑电图以非均匀间隔进行分割，然后在每个段中进行平均。分割的确定方式是，通过应用时间序列分割算法，增加从训练数据中分离两类的程度。我们使用BCI竞赛III P300拼写器范式数据集进行的实验表明，我们的方法比传统下采样方法具有更高的准确率。
