Objective. Mobile Brain/Body Imaging (MoBI) frameworks allowed the research community to find evidence of cortical involvement at walking initiation and during locomotion. However, the decoding of gait patterns from brain signals remains an open challenge. The aim of this work is to propose and validate a deep learning model to decode gait phases from Electroenchephalography (EEG). Approach. A Long-Short Term Memory (LSTM) deep neural network has been trained to deal with time-dependent information within brain signals during locomotion. The EEG signals have been preprocessed by means of Artifacts Subspace Reconstruction (ASR) and Reliable Independent Component Analysis (RELICA) to ensure that classification performance was not affected by movement-related artifacts. Main results. The network was evaluated on the dataset of 11 healthy subjects walking on a treadmill. The proposed decoding approach shows a robust reconstruction (AUC > 90%) of gait pattern...

中文翻译：

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基于深度学习的BCI，可使用LSTM递归神经网络从EEG进行步态解码。

目的。移动脑部/身体成像（MoBI）框架使研究团体能够找到行走开始和运动过程中皮质参与的证据。然而，根据大脑信号对步态模式进行解码仍然是一个挑战。这项工作的目的是提出并验证一种深度学习模型，以解码脑电图（EEG）的步态阶段。方法。长期记忆（LSTM）深度神经网络已经过训练，可以在运动过程中处理脑信号中与时间有关的信息。EEG信号已通过伪影子空间重构（ASR）和可靠独立分量分析（RELICA）进行了预处理，以确保分类性能不受与运动有关的伪影的影响。主要结果。该网络是对11名健康人在跑步机上行走的数据集进行评估的。拟议的解码方法显示了步态模式的鲁棒重建（AUC> 90％）...