Center for Bioelectric Interfaces, Higher School of Economics, Moscow 101000, Russia.
Moscow State University of Medicine and Dentistry, Moscow 101000, Russia.
J Neural Eng. 2021 Mar 2;18(2). doi: 10.1088/1741-2552/abe20e.
Brain-computer interfaces (BCIs) decode information from neural activity and send it to external devices. The use of Deep Learning approaches for decoding allows for automatic feature engineering within the specific decoding task. Physiologically plausible interpretation of the network parameters ensures the robustness of the learned decision rules and opens the exciting opportunity for automatic knowledge discovery.We describe a compact convolutional network-based architecture for adaptive decoding of electrocorticographic (ECoG) data into finger kinematics. We also propose a novel theoretically justified approach to interpreting the spatial and temporal weights in the architectures that combine adaptation in both space and time. The obtained spatial and frequency patterns characterizing the neuronal populations pivotal to the specific decoding task can then be interpreted by fitting appropriate spatial and dynamical models.We first tested our solution using realistic Monte-Carlo simulations. Then, when applied to the ECoG data from Berlin BCI competition IV dataset, our architecture performed comparably to the competition winners without requiring explicit feature engineering. Using the proposed approach to the network weights interpretation we could unravel the spatial and the spectral patterns of the neuronal processes underlying the successful decoding of finger kinematics from an ECoG dataset. Finally we have also applied the entire pipeline to the analysis of a 32-channel EEG motor-imagery dataset and observed physiologically plausible patterns specific to the task.We described a compact and interpretable CNN architecture derived from the basic principles and encompassing the knowledge in the field of neural electrophysiology. For the first time in the context of such multibranch architectures with factorized spatial and temporal processing we presented theoretically justified weights interpretation rules. We verified our recipes using simulations and real data and demonstrated that the proposed solution offers a good decoder and a tool for investigating motor control neural mechanisms.
脑机接口 (BCI) 从神经活动中解码信息并将其发送到外部设备。使用深度学习方法进行解码可以在特定的解码任务中自动进行特征工程。对网络参数进行生理上合理的解释可以确保所学习的决策规则的稳健性，并为自动知识发现开辟令人兴奋的机会。我们描述了一种基于紧凑卷积网络的架构，用于自适应解码脑电 (ECoG) 数据到手指运动学。我们还提出了一种新颖的理论上合理的方法来解释空间和时间权重在同时进行空间和时间自适应的架构中的作用。然后可以通过拟合适当的空间和动态模型来解释获得的特征图，这些特征图描述了对特定解码任务至关重要的神经元群体。我们首先使用现实的蒙特卡罗模拟测试了我们的解决方案。然后，当应用于柏林 BCI 竞赛 IV 数据集的 ECoG 数据时，我们的架构与竞赛获胜者的表现相当，而无需进行显式的特征工程。使用网络权重解释的建议方法，我们可以解开成功从 ECoG 数据中解码手指运动学的神经元过程的空间和频谱模式。最后，我们还将整个管道应用于分析 32 通道 EEG 运动想象数据集，并观察到与任务相关的生理上合理的模式。我们描述了一种紧凑且可解释的 CNN 架构，该架构源自基本原理，并包含神经电生理学领域的知识。我们首次在具有因子化空间和时间处理的多分支架构的背景下提出了理论上合理的权重解释规则。我们使用模拟和真实数据验证了我们的方法，并证明了所提出的解决方案提供了良好的解码器和用于研究运动控制神经机制的工具。
