Sparse multiple instance learning with non-convex penalty

Zhang, Yuqi; Zhang, Haibin; Tian, Yingjie

doi:10.1016/j.neucom.2020.01.100

Cited by 9 publications

(1 citation statement)

References 21 publications

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“…M and U, instead of M and Σ, are used to represent LoFC and HiFC, respectively, because the size of Σ is too large [47]. M and Σ are calculated based on a maximum likelihood estimation [49,50]. Therefore, we obtained ten FC matrices in a one time-crop because LoFC and HiFC were calculated in five frequency ranges.…”

Section: Estimation Of Low-and High-order Functional Connectivity Valuesmentioning

confidence: 99%

Explainable Convolutional Neural Network to Investigate Age-Related Changes in Multi-Order Functional Connectivity

et al. 2021

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Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by the current machine learning techniques because of a lack of physiological understanding. To investigate the suitability of FC in BCIs for the elderly, we propose the decoding of lower- and higher-order FC using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. A total of 17 young adults 24.5±2.7 years and 12 older 72.5±3.2 years adults were recruited to perform tasks related to hand-force control with or without mental calculation. The CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increased the classification accuracy by 88.3% compared to the filter-bank common spatial pattern. The LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe, depending on the task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly.

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Section: Estimation Of Low-and High-order Functional Connectivity Valuesmentioning

confidence: 99%