Teli Mohammad Nayeem, Anderson Charles
Department of Computer Science, Colorado State University, Fort Collins, CO 80526, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2486-9. doi: 10.1109/IEMBS.2009.5334802.
Patterns in electroencephalogram (EEG) signals are analyzed for a Brain Computer Interface (BCI). An important aspect of this analysis is the work on transformations of high dimensional EEG data to low dimensional spaces in which we can classify the data according to mental tasks being performed. In this research we investigate how a Neural Network (NN) in an auto-encoder with bottleneck configuration can find such a transformation. We implemented two approximate second-order methods to optimize the weights of these networks, because the more common first-order methods are very slow to converge for networks like these with more than three layers of computational units. The resulting non-linear projections of time embedded EEG signals show interesting separations that are related to tasks. The bottleneck networks do indeed discover nonlinear transformations to low-dimensional spaces that capture much of the information present in EEG signals. However, the resulting low-dimensional representations do not improve classification rates beyond what is possible using Quadratic Discriminant Analysis (QDA) on the original time-lagged EEG.
针对脑机接口（BCI）对脑电图（EEG）信号模式进行分析。该分析的一个重要方面是关于将高维EEG数据转换到低维空间的工作，在低维空间中我们可以根据所执行的心理任务对数据进行分类。在本研究中，我们探究具有瓶颈配置的自动编码器中的神经网络（NN）如何找到这样一种转换。我们实现了两种近似二阶方法来优化这些网络的权重，因为对于具有三层以上计算单元的此类网络，更常用的一阶方法收敛非常缓慢。时间嵌入EEG信号的所得非线性投影显示出与任务相关的有趣分离。瓶颈网络确实发现了到低维空间的非线性转换，这些转换捕获了EEG信号中存在的大部分信息。然而，所得的低维表示并没有将分类率提高到超过对原始时间滞后EEG使用二次判别分析（QDA）所能达到的水平。
