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| 1 | Neural Mechanisms of Mental Fatigue Revisited: New Insights from the Brain Connectome显示文摘Maintaining sustained attention during a prolonged cognitive task often comes at a cost: high levels of mental fatigue. Heuristically, mental fatigue refers to a feeling of tiredness or exhaustion, and a disengagement from the task at hand;it manifests as impaired cognitive and behavioral performance. In order to effectively reduce the undesirable yet preventable consequences of mental fatigue in many real-world workspaces, a better understanding of the underlying neural mechanisms is needed, and continuous efforts have been devoted to this topic. In comparison with conventional univariate approaches, which are widely utilized in fatigue studies, convergent evidence has shown that multivariate functional connectivity analysis may lead to richer information about mental fatigue. In fact, mental fatigue is increasingly thought to be related to the deviated reorganization of functional connectivity among brain regions in recent studies. In addition, graph theoretical analysis has shed new light on quantitatively assessing the reorganization of the brain functional networks that are modulated by mental fatigue. This review article begins with a brief introduction to neuroimaging studies on mental fatigue and the brain connectome, followed by a thorough overview of connectome studies on mental fatigue. Although only a limited number of studies have been published thus far, it is believed that the brain connectome can be a useful approach not only for the elucidation of underlying neural mechanisms in the nascent field of neuroergonomics, but also for the automatic detection and classification of mental fatigue in order to address the prevention of fatigue-related human error in the near future. | Peng Qi Hua Ru Lingyun Gao Xiaobing Zhang Tianshu Zhou Yu Tian Nitish Thakor Anastasios Bezerianos Jinsong Li Yu Sun | 2019 | Engineering2019，5，2： | 5 |
| 2 | 基于脑电信号的驾驶疲劳检测综述显示文摘围绕基于脑电信号的驾驶疲劳检测,通过大量文献检索,总结了脑电信号采集设备、脑电信号特征提取方法和脑电信号分类方法三个方面现状.分析了采集设备的便携性与舒适度问题、与疲劳相关特征的稳定性问题及疲劳检测模型的鲁棒性问题,进而梳理并总结出基于脑电信号驾驶疲劳检测的三个发展趋势:从湿式电极到干式电极;从通道内特征到通道间特征;从浅层机器学习到深度学习. | 王洪涛 殷浩钧 陈创泉 Anastasios Bezerianos | 2022 | 华中科技大学学报(自然科学版)2022，50，11： | 3 |
| 3 | Novel Bayesian multiscale method for speckle removal in medical ultrasound Images显示文摘 | Achim A Bezerianos A Tsakalides P | 2001 | IEEE Transactions on medical image2001，20，8： | 1 |
| 4 | 基于单试验脑电图的n-back任务中的脑力负荷分类(英文)显示文摘近年来,脑力负荷估计已经经历了广泛的研究,因为监测认知负荷的能力能够防止认知超负荷并且改善工作场所安全。脑电图(EEG)信号已经被发现是一种客观和非侵入性的脑力负荷的测量方式。然而,作为实时脑力负荷监测和脑机接口研究的重要一步,基于单试验EEG数据的认知负荷的评估一直是一个重大的挑战。最近,许多高级的特征提取方法和机器学习算法已经被采用于基于EEG的脑力负荷评估中。在本研究中,使用在具有2个难度水平的n-back任务的执行期间记录的EEG数据进行了单试验脑力负荷分类,测试了3种类型的特征提取的有效性(谱功率、离散小波变换和公共空间滤波),并评估了4种分类算法的性能(支持向量机、K-近邻、随机森林和梯度推进分类器)。研究结果表明,公共空间滤波是性能最好的基于单试验的脑力负荷分类的特征提取方法,而且最佳性能可以通过将来自谱功率或离散小波变换的特征与来自公共空间滤波的特征相结合,并采用随机森林分类器来实现。这项研究可能对基于单试验脑电图数据的脑力负荷评估中的特征提取方法以及机器学习算法的选择提供一些有用的指导。 | 代忠祥 Bezerianos Anastasios Chen Shen-Hsing Annabel 孙煜 | 2017 | 仪器仪表学报2017，38，6： | 1 |
| 5 | SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling 显示文摘 | Achim A Tsakalides P Bezerianos A | 2003 | IEEE Transactions on Geoscience and Remote Sensing2003，41，8： | 1 |
| 6 | SAR image denoising via Bayesian wavelet shrinkage based on heavytailed modeling 显示文摘 | A Achim P Tsakalides A Bezerianos | 2003 | IEEE Transactions on Geoseience and Remote Sensing2003，41，8： | 1 |
| 7 | SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling 显示文摘 | Achim A Tsakalides P Bezerianos A | 2003 | IEEE Transaction on Geoscience and Remote Sensing2003，41，8： | 1 |
| 8 | Novel Bayesian Multiscale Method for Speckle Removal in Medical Ultrasound Images显示文摘 | Achim A Bezerianos A Tsakalides P | 2001 | IEEE Trans Med Imaging2001，20，8： | 1 |
| 9 | SAR Image Denoising Via Bayesian Wavelet Shrinkage Based on Heavy - tailed Modeling显示文摘 | Alin Achim Panagiontis Tsakalides Anastasios Bezerianos | 2003 | IEEE Transctions on Geoscience and Remote Sensing2003，41，8： | 1 |
| 10 | SAR Image DenoisingVia Bayesian Wavelet Shrinkage Based on Heavy-tailedModeling 显示文摘 | Achim A Tsakalides P Bezerianos A | 2003 | IEEE Transactions on Geoscience and RemoteSensing2003，41，3： | 1 |
| 11 | Semi Supervised Fuzzy Clustering Networks forConstrained Analysis of Time-Series 6ene ExpressionData显示文摘 | Maraziotis I A Dragomir A Bezerianos A | 2006 | artificial neural networks ICANN20062006，4132，： | 1 |
| 12 | Time- dependent entropy estimation of EEG rhythm changes following brain ischemia显示文摘 | BEZERIANOS A TONG S THANKOR N | 2003 | Annals of Biomedical Engineering2003，31，2： | 1 |
| 13 | Novel Bayesian multiscale method for speckle removal in medical ultrasound images显示文摘 | Achim A Bezerianos A Tsakalides P | 2001 | IEEE Transactionson medical image2001，20，8： | 1 |
| 14 | Nonextensive entropy measure of EEG following brain injury from cardiac arrest 显示文摘 | Tong SB Bezerianos A Paul J el al | 2002 | Physical A: Statistical Mechanics and its Applications2002，305，34： | 1 |
| 15 | Parameterized entropy analysis of LEG following hypoxic-ischemic brain injury 显示文摘 | Tong SB Bezerianos A Malhotra A | 2003 | Physical Letters A2003，314，55： | 1 |
| 16 | Parameterized entropy analysis of EEG following hypoxic-ischemic brain injury显示文摘 | TONG Shanbao BEZERIANOS A MALHOTRA A | 2003 | Physics Letters A2003，314，56： | 1 |
| 17 | Novel bayesian muhiscale method for speckle removal in medical ultrasound images 显示文摘 | ACHIM A BEZERIANOS A TSAKALIDES P | 2001 | IEEE Transactions on Medical Imaging2001，20，8： | 1 |
| 18 | SAR Image Denoising via Bayesian Wavelet Shrinkage Based on Heavy-Tailed Modeling 显示文摘 | Alin Achim Panagiotis Tsakalides Anastasios Bezerianos | 2003 | IEEE Transactions on Geoscience and Remote Sensing2003，41，8： | 1 |
| 19 | The Supervised Network Self-Organizing Map for Classification of Large Data Sets显示文摘 | Stergios Papadimitriou Seferina Mavroudi Liviu Vladutu G. Pavlides Anastasios Bezerianos | 2002 | Applied Intelligence2002，，3： | 1 |
| 20 | Nonextensive entropy measure of EEG following brain injury from cardiac arrest source显示文摘 | Tong S Bezerianos A Paul J | 2002 | Physica A:Statistical Mechanics and Its Applications2002，305，34： | 1 |
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