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An EEG-based brain-computer interface for gait training

机译：基于脑电图的步态训练脑机接口

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This work presents an electroencephalography (EEG)-based Brain-computer Interface (BCI) that decodes cerebral activities to control a lower-limb gait training exoskeleton. Motor imagery (MI) of flexion and extension of both legs was distinguished from the EEG correlates. We executed experiments with 5 able-bodied individuals under a realistic rehabilitation scenario. The Power Spectral Density (PSD) of the signals was extracted with sliding windows to train a linear discriminate analysis (LDA) classifier. An average classification accuracy of 0.67±0.07 and AUC of 0.77±0.06 were obtained in online recordings, which confirmed the feasibility of decoding these signals to control the gait trainer. In addition, discriminative feature analysis was conducted to show the modulations during the mental tasks. This study can be further implemented with different feedback modalities to enhance the user performance.
机译：这项工作提出了一个基于脑电图（EEG）的脑机接口（BCI），该接口对大脑活动进行解码以控制下肢步态训练外骨骼。腿部弯曲和伸展的运动图像（MI）与EEG相关性有所区别。我们在现实的康复情况下对5个身体健全的人进行了实验。使用滑动窗口提取信号的功率谱密度（PSD），以训练线性判别分析（LDA）分类器。在线记录中获得的平均分类准确度为0.67±0.07，AUC为0.77±0.06，这证实了解码这些信号以控制步态训练器的可行性。此外，进行了辨别性特征分析以显示在心理任务期间的调节。可以使用不同的反馈方式进一步实施此研究，以提高用户性能。

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