论文标题
使用暹罗神经网络对想象的语音进行分类
Classification of Imagined Speech Using Siamese Neural Network
论文作者
论文摘要
由于将其用作直观的通信工具，因此想象中的语音被视为脑机界面的新趋势。但是，以前的研究表明，分类表现低，因此其在现实生活中的使用是不可行的。另外，没有找到合适的分析方法。最近，深度学习算法已应用于此范式。但是，由于数据量少，分类性能的提高受到限制。为了解决这些问题，在这项研究中，我们提出了一个使用暹罗神经网络编码器的端到端框架，该框架通过考虑类之间的距离来学习判别特征。使用RAW Eletroeletroectrophalography（eeg）信号对想象中的单词（例如Arriba（up），Abajo（down），derecha（down），derecha（右），izquierda（左），阿德兰特（向前）（向后））进行了分类。对于想象的语音，我们获得了31.40％的6级分类精度，这显着优于其他方法。之所以可以使用，是因为使用了相似样品之间的距离，同时使用了相似样品之间的距离，从而增加了不同样本之间的距离。在这方面，我们的方法可以从一个小数据集中学习判别功能。提出的框架将有助于提高少量数据的想象语音的分类性能，并实施直观的通信系统。
Imagined speech is spotlighted as a new trend in the brain-machine interface due to its application as an intuitive communication tool. However, previous studies have shown low classification performance, therefore its use in real-life is not feasible. In addition, no suitable method to analyze it has been found. Recently, deep learning algorithms have been applied to this paradigm. However, due to the small amount of data, the increase in classification performance is limited. To tackle these issues, in this study, we proposed an end-to-end framework using Siamese neural network encoder, which learns the discriminant features by considering the distance between classes. The imagined words (e.g., arriba (up), abajo (down), derecha (right), izquierda (left), adelante (forward), and atrás (backward)) were classified using the raw electroencephalography (EEG) signals. We obtained a 6-class classification accuracy of 31.40% for imagined speech, which significantly outperformed other methods. This was possible because the Siamese neural network, which increases the distance between dissimilar samples while decreasing the distance between similar samples, was used. In this regard, our method can learn discriminant features from a small dataset. The proposed framework would help to increase the classification performance of imagined speech for a small amount of data and implement an intuitive communication system.
