论文标题
使用基于深度学习的长度培训的语音图像分类
Speech Imagery Classification using Length-Wise Training based on Deep Learning
论文作者
论文摘要
大脑计算机界面使用大脑信号来控制外部设备，而无需实际控制行为。最近，已经研究了使用语言直接沟通的语音图像。语音图像使用用户想象语音时产生的大脑信号。与汽车图像不同，语音图像仍然具有未知的特征。此外，脑电图具有复杂和非稳态特性，导致解码性能不足。此外，语音图像很难利用空间特征。在这项研究中，我们设计了长度训练，使模型可以对一系列少数单词进行分类。此外，我们提出了分层卷积神经网络结构和损失功能，以最大化训练策略。提出的方法在语音图像分类中表现出竞争性能。因此，我们证明了单词的长度是提高分类性能的线索。
Brain-computer interface uses brain signals to control external devices without actual control behavior. Recently, speech imagery has been studied for direct communication using language. Speech imagery uses brain signals generated when the user imagines speech. Unlike motor imagery, speech imagery still has unknown characteristics. Additionally, electroencephalography has intricate and non-stationary properties resulting in insufficient decoding performance. In addition, speech imagery is difficult to utilize spatial features. In this study, we designed length-wise training that allows the model to classify a series of a small number of words. In addition, we proposed hierarchical convolutional neural network structure and loss function to maximize the training strategy. The proposed method showed competitive performance in speech imagery classification. Hence, we demonstrated that the length of the word is a clue at improving classification performance.
