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Classification of fNIRS Data Under Uncertainty: A Bayesian Neural Network Approach

机译：不确定性下FNIR数据的分类：贝叶斯神经网络方法

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Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive form of Brain-Computer Interface (BCI). It is used for the imaging of brain hemodynamics and has gained popularity due to the certain pros it poses over other similar technologies. The overall functionalities encompass the capture, processing and classification of brain signals. Since hemodynamic responses are contaminated by physiological noises, several methods have been implemented in the past literature to classify the responses in focus from the unwanted ones. However, the methods, thus far does not take into consideration the uncertainty in the data or model parameters. In this paper, we use a Bayesian Neural Network (BNN) to carry out a binary classification on an open-access dataset, consisting of unilateral finger tapping (left- and right-hand finger tapping). A BNN uses Bayesian statistics to assign a probability distribution to the network weights instead of a point estimate. In this way, it takes data and model uncertainty into consideration while carrying out the classification. We used Variational Inference (VI) to train our model. Our model produced an overall classification accuracy of 86.44% over 30 volunteers. We illustrated how the evidence lower bound (ELBO) function of the model converges over iterations. We further illustrated the uncertainty that is inherent during the sampling of the posterior distribution of the weights. We also generated a ROC curve for our BNN classifier using test data from a single volunteer and our model has an AUC score of 0.855.
机译：功能近红外光谱（FNIR）是一种非侵入性的脑电脑界面（BCI）。它用于脑血流动力学的成像，并且由于某些优势而越来越受到它的普及。整体功能包括捕获，加工和脑信号的分类。由于血流动力学反应被生理噪音污染，因此过去的文献中已经实施了几种方法，以分类来自不需要的焦点的响应。但是，该方法，迄今为止没有考虑数据或模型参数中的不确定性。在本文中，我们使用贝叶斯神经网络（BNN）在开放访问数据集上进行二进制分类，包括单侧手指攻丝（左手和右手指攻丝）。 BNN使用贝叶斯统计来为网络权重分配概率分布而不是点估计。通过这种方式，考虑到分类，考虑到数据和模型不确定性。我们使用变分推理（VI）培训我们的模型。我们的型号生产的整体分类准确性为86.44％以上超过30名志愿者。我们说明了如何在迭代中收敛模型的证据下限（Elbo）函数。我们进一步说明了在对重量的后部分布的采样过程中固有的不确定性。我们还使用来自单个志愿者的测试数据生成了我们的BNN分类器的ROC曲线，我们的模型具有0.855的AUC分数。

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