Abstract：
Brain Computer Interface (BCI) is a highly promising human-computer interaction method that can utilize brain signals to control external devices. BCI based on functional near-infrared spectroscopy (fNIRS) is considered a relatively new and promising paradigm. fNIRS is a technique of measuring functional changes in cerebral hemodynamics. It detects changes in the hemodynamic activity of the cerebral cortex by measuring oxyhemoglobin and deoxyhemoglobin (HbR) concentrations and inversely predicts the neural activity of the brain. At the present time, Deep learning (DL) methods have not been widely used in fNIRS decoding, and there are fewer studies considering both spatial and temporal dimensions for fNIRS classification. To solve these problems, we proposed an end-to-end hybrid neural network for feature extraction of fNIRS. The method utilizes a spatial-temporal convolutional layer for automatic extraction of temporally valid information and uses a spatial attention mechanism to extract spatially localized information. A temporal convolutional network (TCN) is used to further utilize the temporal information of fNIRS before the fully connected layer. We validated our approach on a publicly available dataset including 29 subjects, including left-hand and right-hand motor imagery (MI), mental arithmetic (MA), and a baseline task. The results show that the method has few training parameters and high accuracy, providing a meaningful reference for BCI development.
摘要：
脑机接口（BCI）是一种非常有前途的人机交互方法，可以利用大脑信号来控制外部设备。基于功能近红外光谱（fNIRS）的BCI被认为是一种相对较新且有前途的范例。fNIRS是一种测量脑血流动力学功能变化的技术。它通过测量氧合血红蛋白和脱氧血红蛋白（HbR）浓度来检测大脑皮层血液动力学活动的变化，并反向预测大脑的神经活动。目前,深度学习(DL)方法尚未广泛用于fNIRS解码,考虑fNIRS分类的空间和时间维度的研究较少。为了解决这些问题,提出了一种用于fNIRS特征提取的端到端混合神经网络。该方法利用时空卷积层自动提取时间上的有效信息，并利用空间注意力机制提取空间定位信息。时间卷积网络(TCN)用于在全连接层之前进一步利用fNIRS的时间信息。我们在包括29名受试者的公开数据集上验证了我们的方法，包括左手和右手运动图像(MI)，心算(MA)，和基线任务。结果表明,该方法训练参数少,准确度高,为BCI的发展提供有意义的参考。
