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.
翻译:利用机器学习和深层学习来预测电子脑电学信号中的认知任务,是脑-计算机界面(BCI)的一个快速发展领域。 与计算机视觉和自然语言处理领域相比,这些试验的数据数量仍然很小。 开发一种基于PC的机器学习技术以增加非专家终端用户的参与,可以帮助解决数据收集问题。 我们为机器学习创造了一种新型算法,称为“时间多数表决 ” (TMV )。 在我们的实验中,TMV比尖端算法表现得更好。 它可以在个人计算机上高效运行,用于涉及BCI的分类任务。 这些可解释的数据也有助于终端用户和研究人员更好地理解EEG测试。