论文标题
选择适当的脑电图渠道，用于使用深度学习的主题意图分类
Selection of Proper EEG Channels for Subject Intention Classification Using Deep Learning
论文作者
论文摘要
大脑信号可用于控制设备以帮助残疾人。诸如脑电图之类的信号很复杂且难以解释。收集一组信号，应分类以确定主题的意图。在将频道发送到分类器之前，不同的方法试图减少频道的数量。我们提出了一种基于学习的深度方法，用于选择产生高分类准确性的通道的信息子集。可以为选择适当的渠道的个人主题培训所提出的网络。减少通道的数量可以降低脑部计算机接口设备的复杂性。我们的方法可以找到一部分通道。我们方法的准确性与在所有渠道上训练的模型相当。因此，我们的模型的时间和功率成本较低，而其准确性保持较高。
Brain signals could be used to control devices to assist individuals with disabilities. Signals such as electroencephalograms are complicated and hard to interpret. A set of signals are collected and should be classified to identify the intention of the subject. Different approaches have tried to reduce the number of channels before sending them to a classifier. We are proposing a deep learning-based method for selecting an informative subset of channels that produce high classification accuracy. The proposed network could be trained for an individual subject for the selection of an appropriate set of channels. Reduction of the number of channels could reduce the complexity of brain-computer-interface devices. Our method could find a subset of channels. The accuracy of our approach is comparable with a model trained on all channels. Hence, our model's temporal and power costs are low, while its accuracy is kept high.
