UNCER: A framework for uncertainty estimation and reduction in neural decoding of EEG signals

Duan, Tiehang; Wang, Zhenyi; Liu, Sheng; Yin, Yiyi; Srihari, Sargur N.

doi:10.1016/j.neucom.2023.03.071

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…In an OOD data detection problem, one states if the sample is from a known data distribution or not, whereas in classification with a rejection problem, one needs to assign a class label to a sample if it is from the data distribution. Duan et al [11] also studied uncertainty estimation, but they focused on its reduction. Moreover, they used a dropout for making predictions on a test set as a Bayesian feature of architecture, which is a very restricted case of the Bayesian deep learning framework.…”

Section: Discussionmentioning

confidence: 99%

“…Given all the mentioned applications in other areas, it is surprising that OOD detection has been so far very little explored for BCIs. A related problem, uncertainty estimation, was addressed in two BCI studies regarding the uncertainty reduction [11] and classification with rejection option [12] problems but without application for the OOD detection.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Opportunities for Brain–Computer Interfaces: Enhancement of the Existing Classification Algorithms and Out-of-Domain Detection

Chetkin,

Shishkin,

Kozyrskiy

2023

Algorithms

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Bayesian neural networks (BNNs) are effective tools for a variety of tasks that allow for the estimation of the uncertainty of the model. As BNNs use prior constraints on parameters, they are better regularized and less prone to overfitting, which is a serious issue for brain–computer interfaces (BCIs), where typically only small training datasets are available. Here, we tested, on the BCI Competition IV 2a motor imagery dataset, if the performance of the widely used, effective neural network classifiers EEGNet and Shallow ConvNet can be improved by turning them into BNNs. Accuracy indeed was higher, at least for a BNN based on Shallow ConvNet with two of three tested prior distributions. We also assessed if BNN-based uncertainty estimation could be used as a tool for out-of-domain (OOD) data detection. The OOD detection worked well only in certain participants; however, we expect that further development of the method may make it work sufficiently well for practical applications.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%