A convolutional neural network for steady state visual evoked potential classification under ambulatory environment.
A convolutional neural network for steady state visual evoked potential classification under ambulatory environment.
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DOI：
10.1371/journal.pone.0172578
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Lee SW
中科院分区：
文献类型：
--
作者：
Kwak NS;Müller KR;Lee SW
神经信号的稳健分析是一个具有挑战性的问题。在这里，我们贡献了一个卷积神经网络（CNN）来对稳态视觉诱发电位（SSVEP）范式进行鲁棒分类。我们在流动条件下测量脑控外骨骼的基于脑电图 (EEG) 的 SSVEP，在这种条件下，大量伪影可能会影响解码。事实证明，所提出的 CNN 在这些具有挑战性的条件下能够实现可靠的性能。为了验证所提出的方法，我们在两种条件下获取了 SSVEP 数据集：1）静态环境，处于站立位置，同时固定在下肢外骨骼中；2）动态环境，穿着外骨骼沿着测试路线行走（此处，伪影最具挑战性）。在离线分析中，将所提出的 CNN 与标准神经网络和其他最先进的 SSVEP 解码方法（即基于规范相关分析 (CCA) 的分类器、多元同步索引 (MSI)、结合 k 最近邻的 CCA (CCA-KNN) 分类器）进行比较。我们发现 CNN 架构的 SSVEP 解码结果非常令人鼓舞，超过了其他方法，在静态和动态条件下的分类率分别为 99.28% 和 94.03%。随后的分析会检查 CNN 在每一层找到的表示，从而有助于更好地理解 CNN 稳健、准确的解码能力。
The robust analysis of neural signals is a challenging problem. Here, we contribute a convolutional neural network (CNN) for the robust classification of a steady-state visual evoked potentials (SSVEPs) paradigm. We measure electroencephalogram (EEG)-based SSVEPs for a brain-controlled exoskeleton under ambulatory conditions in which numerous artifacts may deteriorate decoding. The proposed CNN is shown to achieve reliable performance under these challenging conditions. To validate the proposed method, we have acquired an SSVEP dataset under two conditions: 1) a static environment, in a standing position while fixated into a lower-limb exoskeleton and 2) an ambulatory environment, walking along a test course wearing the exoskeleton (here, artifacts are most challenging). The proposed CNN is compared to a standard neural network and other state-of-the-art methods for SSVEP decoding (i.e., a canonical correlation analysis (CCA)-based classifier, a multivariate synchronization index (MSI), a CCA combined with k-nearest neighbors (CCA-KNN) classifier) in an offline analysis. We found highly encouraging SSVEP decoding results for the CNN architecture, surpassing those of other methods with classification rates of 99.28% and 94.03% in the static and ambulatory conditions, respectively. A subsequent analysis inspects the representation found by the CNN at each layer and can thus contribute to a better understanding of the CNN’s robust, accurate decoding abilities.