Recently, advanced technologies have unlimited potential in solving various problems with a large amount of data. However, these technologies have yet to show competitive performance in brain-computer interfaces (BCIs) which deal with brain signals. Basically, brain signals are difficult to collect in large quantities, in particular, the amount of information would be sparse in spontaneous BCIs. In addition, we conjecture that high spatial and temporal similarities between tasks increase the prediction difficulty. We define this problem as sparse condition. To solve this, a factorization approach is introduced to allow the model to obtain distinct representations from latent space. To this end, we propose two feature extractors: A class-common module is trained through adversarial learning acting as a generator; Class-specific module utilizes loss function generated from classification so that features are extracted with traditional methods. To minimize the latent space shared by the class-common and class-specific features, the model is trained under orthogonal constraint. As a result, EEG signals are factorized into two separate latent spaces. Evaluations were conducted on a single-arm motor imagery dataset. From the results, we demonstrated that factorizing the EEG signal allows the model to extract rich and decisive features under sparse condition.
翻译:最近,先进技术在用大量数据解决各种问题方面有着无限的潜力,然而,这些技术尚未在涉及大脑信号的大脑-计算机界面(BCIS)中表现出有竞争力的性能。基本上,大脑信号很难收集大量信息,特别是自发BCI中的信息量会稀少。此外,我们推测任务之间在空间和时间上的高度相似性会增加预测困难。我们将此问题定义为稀少的条件。为了解决这个问题,我们采用了一种因子化方法,使模型能够从潜在空间获得不同的显示。为此,我们提议两个特征提取器:一个类通用模块通过对抗性学习作为发电机进行培训;一个特定类别模块利用分类产生的损失功能,以便利用传统方法提取特征。为了最大限度地减少班级常识和班级特有特征所共享的潜在空间,该模型是在或单层制约下训练的。因此,EEEG信号被分解成两个单独的隐蔽空间。我们从结果中看出,对单臂发动机图像数据集进行了评价。我们证明,EEG信号的因因因素而使模型能够根据不稳定条件提取丰富的决定因素。