论文标题
Time多数投票，这是一个基于PC的脑电图分类器，用于非专家用户
Time Majority Voting, a PC-based EEG Classifier for Non-expert Users
论文作者
论文摘要
使用机器学习和深度学习来预测脑电图（EEG）信号的认知任务是脑部计算机界面（BCI）的迅速前进的领域。与计算机视觉和自然语言处理的领域相反，这些试验的数据数量仍然很小。开发基于PC的机器学习技术来增加非专家最终用户的参与，可以帮助解决此数据收集问题。我们为机器学习创建了一种新颖的算法，称为时间多数投票（TMV）。在我们的实验中，TMV的性能要比尖端算法更好。它可以在个人计算机上有效运行，以进行涉及BCI的分类任务。这些可解释的数据还可以帮助最终用户和研究人员更好地理解脑电图测试。
Using Machine Learning and Deep Learning to predict cognitive tasks from electroencephalography (EEG) signals is a rapidly advancing field in Brain-Computer Interfaces (BCI). In contrast to the fields of computer vision and natural language processing, the data amount of these trials is still rather tiny. Developing a PC-based machine learning technique to increase the participation of non-expert end-users could help solve this data collection issue. We created a novel algorithm for machine learning called Time Majority Voting (TMV). In our experiment, TMV performed better than cutting-edge algorithms. It can operate efficiently on personal computers for classification tasks involving the BCI. These interpretable data also assisted end-users and researchers in comprehending EEG tests better.
