Roy Sujit, Chowdhury Anirban, McCreadie Karl, Prasad Girijesh
School of Computing, Engineering & Intelligent Systems, Ulster University, Derry-Londonderry, United Kingdom.
School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom.
Front Neurosci. 2020 Sep 30;14:918. doi: 10.3389/fnins.2020.00918. eCollection 2020.
Inter-subject transfer learning is a long-standing problem in brain-computer interfaces (BCIs) and has not yet been fully realized due to high inter-subject variability in the brain signals related to motor imagery (MI). The recent success of deep learning-based algorithms in classifying different brain signals warrants further exploration to determine whether it is feasible for the inter-subject continuous decoding of MI signals to provide contingent neurofeedback which is important for neurorehabilitative BCI designs. In this paper, we have shown how a convolutional neural network (CNN) based deep learning framework can be used for inter-subject continuous decoding of MI related electroencephalographic (EEG) signals using the novel concept of Mega Blocks for adapting the network against inter-subject variabilities. These Mega Blocks have the capacity to repeat a specific architectural block several times such as one or more convolutional layers in a single Mega Block. The parameters of such Mega Blocks can be optimized using Bayesian hyperparameter optimization. The results, obtained on the publicly available BCI competition IV-2b dataset, yields an average inter-subject continuous decoding accuracy of 71.49% (κ = 0.42) and 70.84% (κ = 0.42) for two different training methods such as adaptive moment estimation (Adam) and stochastic gradient descent (SGDM), respectively, in 7 out of 9 subjects. Our results show for the first time that it is feasible to use CNN based architectures for inter-subject continuous decoding with a sufficient level of accuracy for developing calibration-free MI-BCIs for practical purposes.
个体间迁移学习是脑机接口（BCI）中一个长期存在的问题，由于与运动想象（MI）相关的脑信号存在高度个体间变异性，该问题尚未得到充分解决。基于深度学习的算法最近在对不同脑信号进行分类方面取得的成功，值得进一步探索，以确定个体间连续解码MI信号以提供偶然神经反馈是否可行，这对于神经康复BCI设计非常重要。在本文中，我们展示了基于卷积神经网络（CNN）的深度学习框架如何通过使用“超级块”这一新颖概念，用于个体间连续解码与MI相关的脑电图（EEG）信号，从而使网络能够适应个体间的变异性。这些超级块能够多次重复特定的架构块，例如在单个超级块中包含一个或多个卷积层。此类超级块的参数可以使用贝叶斯超参数优化进行优化。在公开可用的BCI竞赛IV-2b数据集上获得的结果表明，对于9名受试者中的7名，分别采用自适应矩估计（Adam）和随机梯度下降（SGDM）这两种不同训练方法时，个体间连续解码的平均准确率分别为71.49%（κ = 0.42）和70.84%（κ = 0.42）。我们的结果首次表明，使用基于CNN的架构进行个体间连续解码并达到足够的准确率，从而开发出用于实际目的的免校准MI-BCI是可行的。
