Recently, practical brain-computer interface is actively carried out, especially, in an ambulatory environment. However, the electroencephalography (EEG) signals are distorted by movement artifacts and electromyography signals when users are moving, which make hard to recognize human intention. In addition, as hardware issues are also challenging, ear-EEG has been developed for practical brain-computer interface and has been widely used. In this paper, we proposed ensemble-based convolutional neural networks in ambulatory environment and analyzed the visual event-related potential responses in scalp- and ear-EEG in terms of statistical analysis and brain-computer interface performance. The brain-computer interface performance deteriorated as 3-14% when walking fast at 1.6 m/s. The proposed methods showed 0.728 in average of the area under the curve. The proposed method shows robust to the ambulatory environment and imbalanced data as well.

中文翻译：

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动态环境下基于卷积神经网络的人耳心电信号与事件相关电位的解码

最近，特别是在非卧床环境中，积极地进行了实用的脑机接口。但是，当用户移动时，脑电图（EEG）信号会因运动伪影和肌电图信号而失真，从而难以识别人的意图。另外，由于硬件问题也具有挑战性，因此耳脑电图已经开发用于实用的脑机接口并已被广泛使用。在本文中，我们提出了在动态环境中基于集成的卷积神经网络，并从统计分析和脑机接口性能方面分析了头皮和耳朵EEG中与视觉事件相关的潜在响应。当以1.6 m / s的速度快速行走时，脑机接口性能下降了3-14％。提议的方法显示为0。曲线下面积的平均值为728。所提出的方法显示出对动态环境的鲁棒性以及数据不平衡的情况。