论文标题
AI方法的演变用于运动图像基于脑电图的BCIS
The evolution of AI approaches for motor imagery EEG-based BCIs
论文作者
论文摘要
基于脑电图（MI）的脑电图（EEG）大脑计算机界面（BCIS）允许人与机器之间的直接通信通过利用与运动想象相关的神经途径，可以直接通信。因此，这些系统开辟了开发可能跨越医疗领域到娱乐业的应用程序的可能性。在这种情况下，人工智能（AI）的方法变得至关重要，尤其是在希望向BCI用户提供正确且连贯的反馈时。此外，已广泛利用了基于MI EEG的BCI的公共数据集，以测试AI域的新技术。在这项工作中，对不同年份和不同设备收集的数据集的AI方法进行了研究，但使用连贯的实验范式进行了研究，目的是提供对AI技术对基于MI EEG的BCI数据的进化和影响的简洁但充分的全面调查。
The Motor Imagery (MI) electroencephalography (EEG) based Brain Computer Interfaces (BCIs) allow the direct communication between humans and machines by exploiting the neural pathways connected to motor imagination. Therefore, these systems open the possibility of developing applications that could span from the medical field to the entertainment industry. In this context, Artificial Intelligence (AI) approaches become of fundamental importance especially when wanting to provide a correct and coherent feedback to BCI users. Moreover, publicly available datasets in the field of MI EEG-based BCIs have been widely exploited to test new techniques from the AI domain. In this work, AI approaches applied to datasets collected in different years and with different devices but with coherent experimental paradigms are investigated with the aim of providing a concise yet sufficiently comprehensive survey on the evolution and influence of AI techniques on MI EEG-based BCI data.
