Browsing by Author "Wei, Hua-Liang"

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    Brain functional and effective connectivity based on electroencephalography recordings: a review
    (Wiley, 2021-10-20) Cao, Jun; Zhao, Yifan; Shan, Xiaocai; Wei, Hua-Liang; Guo, Yuzhu; Chen, Liangyu; Erkoyuncu, John Ahmet; Sarrigiannis, Ptolemaios Georgios
    Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG-based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools. This article reviews EEG-based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time-based, and frequency-based or time-frequency-based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.
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    The cortical focus in childhood absence epilepsy; evidence from nonlinear analysis of scalp EEG recordings
    (Elsevier, 2018-01-08) Sarrigiannis, Ptolemaios G.; Zhao, Yifan; He, Fei; Billings, Stephen A.; Baster, Kathleen; Rittey, Chris; Yianni, John; Zis, Panagiotis; Wei, Hua-Liang; Hadjivassiliou, Marios; Grünewald, Richard
    Objective To determine the origin and dynamic characteristics of the generalised hyper-synchronous spike and wave (SW) discharges in childhood absence epilepsy (CAE). Methods We applied nonlinear methods, the error reduction ratio (ERR) causality test and cross-frequency analysis, with a nonlinear autoregressive exogenous (NARX) model, to electroencephalograms (EEGs) from CAE, selected with stringent electro-clinical criteria (17 cases, 42 absences). We analysed the pre-ictal and ictal strength of association between homologous and heterologous EEG derivations and estimated the direction of synchronisation and corresponding time lags. Results A frontal/fronto-central onset of the absences is detected in 13 of the 17 cases with the highest ictal strength of association between homologous frontal followed by centro-temporal and fronto-central areas. Delays consistently in excess of 4 ms occur at the very onset between these regions, swiftly followed by the emergence of “isochronous” (0-2ms) synchronisation but dynamic time lag changes occur during SW discharges. Conclusions In absences an initial cortico-cortical spread leads to dynamic lag changes to include periods of isochronous interhemispheric synchronisation, which we hypothesize is mediated by the thalamus. Significance Absences from CAE show ictal epileptic network dynamics remarkably similar to those observed in WAG/Rij rats which guided the formulation of the cortical focus theory.
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    Distractor-aware deep regression for visual tracking
    (MDPI, 2019-01-18) Du, Ming; Ding, Yang; Meng, Xiuyun; Wei, Hua-Liang; Zhao, Yifan
    In recent years, regression trackers have drawn increasing attention in the visual-object tracking community due to their favorable performance and easy implementation. The tracker algorithms directly learn mapping from dense samples around the target object to Gaussian-like soft labels. However, in many real applications, when applied to test data, the extreme imbalanced distribution of training samples usually hinders the robustness and accuracy of regression trackers. In this paper, we propose a novel effective distractor-aware loss function to balance this issue by highlighting the significant domain and by severely penalizing the pure background. In addition, we introduce a full differentiable hierarchy-normalized concatenation connection to exploit abstractions across multiple convolutional layers. Extensive experiments were conducted on five challenging benchmark-tracking datasets, that is, OTB-13, OTB-15, TC-128, UAV-123, and VOT17. The experimental results are promising and show that the proposed tracker performs much better than nearly all the compared state-of-the-art approaches.
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    Editorial: New theories, models, and AI methods of brain dynamics, brain decoding and neuromodulation
    (Frontiers, 2023-12-12) Guo, Yuzhu; Li, Yang; Wei, Hua-Liang; Zhao, Yifan
    The human brain is highly dynamic and complex, supporting a remarkable range of functions by dynamically integrating and coordinating different brain regions and networks across multiple spatial and temporal scales. Research on the human brain has become truly interdisciplinary involving medicine, neurobiology, engineering, and related fields. A thorough understanding of the mechanisms of neuromodulation actions is urgently needed for stimulation parameters optimization, response prediction, and consistent therapy. This Research Topic aims to combine top-down and bottom-up methods to produce robust results that allow for a meaningful interpretation in terms of the underlying brain dynamics with an emphasis on brain decoding and neuromodulation.
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    Fractional power NARX model identification using a harmony search algorithm
    (2012-07-04T00:00:00Z) Wei, Hua-Liang; Zhao, Yifan; Billings, Stephen A.; Zhao, Jia
    A novel type of discrete-time fractional-powernonlinear autoregressive with exogenous input (FPNARX) modelis introduced for system identification, modeling and prediction.Parameter estimation of such a model is a nonlinear optimizationproblem. A harmony search algorithm is then applied to solvesuch fractional models. Examples of both simulated and real dataare provided.
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    Inferring the variation of climatic and glaciological contributions to West Greenland iceberg discharge in the twentieth century
    (Elsevier, 2016-08-17) Zhao, Yifan; Bigg, Grant R.; Billings, Steve A.; Hanna, Edward; Sole, Andrew J.; Wei, Hua-Liang; Kadirkamanathan, Visakan; Wilton, David J.
    Iceberg discharge is a major component of the mass balance of the Greenland Ice Sheet (GrIS). While bulk estimates of discharge variation over time exist, inferred remotely from measurements of grounding line ice velocities or surface mass balance calculations, few detailed measurements of discharge itself from individual marine-terminating glaciers existed until recent years. Recently, it has been shown, through a combination of ocean–iceberg modelling and non-linear system identification, that the century-long record of iceberg numbers crossing 48oN in the West Atlantic is a good first-order proxy for discharge from at least south and west Greenland. Here, we explore the varying relative importance of ice sheet, oceanic and climatic forcing of iceberg discharge from these areas over the twentieth century, by carrying out sensitivity studies of a non-linear auto-regressive mathematical model of the 48oN time series. We find that the relationships are mainly non-linear, with the contribution of the GrIS surface mass balance to iceberg discharge likely to be dominant in the first half of the century. This period is followed by several decades where oceanic temperature effects are most important in determining the model variation in iceberg discharge. In recent decades, all physical processes play a non-negligible part in explaining the iceberg discharge and the model suggests that the glacial response time to environmental changes may have decreased
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    A pilot study investigating a novel non-linear measure of eyes open versus eyes closed EEG synchronization in people with Alzheimer's disease and healthy controls
    (MDPI, 2018-07-17) Blackburn, Daniel J.; Zhao, Yifan; De Marco, Matteo; Bell, Simon M.; He, Fei; Wei, Hua-Liang; Lawrence, Sarah; Unwin, Zoe C.; Blyth, Michelle; Angel, Jenna; Baster, Kathleen; Farrow, Thomas F. D.; Wilkinson, Iain D.; Billings, Stephen A.; Venneri, Annalena; Sarrigiannis, Ptolemaios G.
    Background: The incidence of Alzheimer disease (AD) is increasing with the ageing population. The development of low cost non-invasive diagnostic aids for AD is a research priority. This pilot study investigated whether an approach based on a novel dynamic quantitative parametric EEG method could detect abnormalities in people with AD. Methods: 20 patients with probable AD, 20 matched healthy controls (HC) and 4 patients with probable fronto temporal dementia (FTD) were included. All had detailed neuropsychology along with structural, resting state fMRI and EEG. EEG data were analyzed using the Error Reduction Ratio-causality (ERR-causality) test that can capture both linear and nonlinear interactions between different EEG recording areas. The 95% confidence intervals of EEG levels of bi-centroparietal synchronization were estimated for eyes open (EO) and eyes closed (EC) states. Results: In the EC state, AD patients and HC had very similar levels of bi-centro parietal synchronization; but in the EO resting state, patients with AD had significantly higher levels of synchronization (AD = 0.44; interquartile range (IQR) 0.41 vs. HC = 0.15; IQR 0.17, p < 0.0001). The EO/EC synchronization ratio, a measure of the dynamic changes between the two states, also showed significant differences between these two groups (AD ratio 0.78 versus HC ratio 0.37 p < 0.0001). EO synchronization was also significantly different between AD and FTD (FTD = 0.075; IQR 0.03, p < 0.0001). However, the EO/EC ratio was not informative in the FTD group due to very low levels of synchronization in both states (EO and EC). Conclusion: In this pilot work, resting state quantitative EEG shows significant differences between healthy controls and patients with AD. This approach has the potential to develop into a useful non-invasive and economical diagnostic aid in AD.
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    A wavelet neural network model for spatio-temporal image processing and modeling
    (IEEE, 2015) Wei, Hua-Liang; Zhao, Yifan; Jiang, R.
    Spatio-temporal images are a class of complex dynamical systems that evolve over both space and time. Compared with pure temporal processes, the identification of spatio-temporal models from observed images is much more difficult and quite challenging. Starting with an assumption that there is no a priori information about the true model but only observed data are available, this work introduces a new type of wavelet network that utilizes the easy tractability and exploits the good properties of multiscale wavelet decompositions to represent the rules of the associated spatio-temporal evolutionary system. An application to a chemical reaction exhibiting a spatio-temporal evolutionary behaviour, is investigated to demonstrate the application of the proposed modeling and learning approaches.