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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Long, Jinyi; Wang, Jue; Yu, Tianyou;

    The hybrid brain computer interface (BCI) based on motor imagery (MI) and P300 has been a preferred strategy aiming to improve the detection performance through combining the features of each. However, current methods used for combining these two modalities optimize them separately, which does not result in optimal performance. Here, we present an efficient framework to optimize them together by concatenating the features of MI and P300 in a block diagonal form. Then a linear classifier under a dual spectral norm regularizer is applied to the combined features. Under this framework, the hybrid features of MI and P300 can be learned, selected, and combined together directly. Experimental results on the data set of hybrid BCI based on MI and P300 are provided to illustrate competitive performance of the proposed method against other conventional methods. This provides an evidence that the method used here contributes to the discrimination performance of the brain state in hybrid BCI.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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    Europe PubMed Central
    Article . 2017
    Data sources: PubMed Central
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Europe PubMed Central
      Article . 2017
      Data sources: PubMed Central
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Jing Xiang; Qian Luo; Rupesh Kotecha; Abraham M. Korman; +5 Authors

    Recent studies have revealed the importance of high-frequency brain signals (>70 Hz). One challenge of high-frequency signal analysis is that the size of time-frequency representation of high-frequency brain signals could be larger than 1 terabytes (TB), which is beyond the upper limits of a typical computer workstation’s memory (<196 GB). The aim of the present study is to develop a new method to provide greater sensitivity in detecting high-frequency magnetoencephalography (MEG) signals in a single automated and versatile interface, rather than the more traditional, time-intensive visual inspection methods, which may take up to several days. To address the aim, we developed a new method, accumulated source imaging, defined as the volumetric summation of source activity over a period of time. This method analyzes signals in both low- (1~70 Hz) and high-frequency (70~200 Hz) ranges at source levels. To extract meaningful information from MEG signals at sensor space, the signals were decomposed to channel-cross-channel matrix (CxC) representing the spatiotemporal patterns of every possible sensor-pair. A new algorithm was developed and tested by calculating the optimal CxC and source location-orientation weights for volumetric source imaging, thereby minimizing multi-source interference and reducing computational cost. The new method was implemented in C/C++ and tested with MEG data recorded from clinical epilepsy patients. The results of experimental data demonstrated that accumulated source imaging could effectively summarize and visualize MEG recordings within 12.7 hours by using approximately 10 GB of computer memory. In contrast to the conventional method of visually identifying multi-frequency epileptic activities that traditionally took 2-3 days and used 1-2 TB storage, the new approach can quantify epileptic abnormalities in both low- and high-frequency ranges at source levels, using much less time and computer memory.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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    Europe PubMed Central
    Article . 2014
    Data sources: PubMed Central
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    Frontiers in Neuroinformatics
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    Frontiers in Neuroinformatics
    Article . 2014 . Peer-reviewed
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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      Europe PubMed Central
      Article . 2014
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      Frontiers in Neuroinformatics
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      Frontiers in Neuroinformatics
      Article . 2014 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Yijing Zhang; Jinfei Ma; Chi Zhang; Ruosong Chang;

    Abstract With the continuous improvement of automated vehicles, researchers have found that automated driving is more likely to cause insufficient mental workload for the driver, which induces passive fatigue and endangers traffic safety. To explore the impact of automation and scenario complexity on the passive fatigue of the driver, we developed a three-factor, 2 (automated driving, manual driving) × 2 (monotonic condition, engaging condition) × 6 (measurement stage: 1–6) mixed experiment. We collected electroencephalography (EEG), detection-response task (DRT) performance, and the subjective report scores of 48 drivers. We found that in automated driving under monotonic conditions, the topographic map’s activation range of the drivers brain was the smallest in the six stages, and the mental workload of this group continued to maintain the lowest state at each stage; however, the subjectively reported fatigue level was significantly increased; thus, the driver experienced passive fatigue. After simulating a low-load scenario for 40 min, the power of the alpha of the driver’s EEG indicators increased significantly, the accuracy of the detection reaction task decreased, and the reaction time became slower. The EEG sample’s entropy value of the driver’s passive fatigue was 0.243, and the judgement accuracy rate was 0.71. We proved that in automated driving under monotonic conditions, the driver is more prone to passive fatigue owing to insufficient mental workload.

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    https://doi.org/10.21203/rs.3....
    Preprint . 2021
    License: CC BY
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      https://doi.org/10.21203/rs.3....
      Preprint . 2021
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    Authors: Tao Xu; Yun Zhou; Zekai Hou; Wenlan Zhang;

    The brain is a complex and dynamic system, consisting of interacting sets and the temporal evolution of these sets. Electroencephalogram (EEG) recordings of brain activity play a vital role to decode the cognitive process of human beings in learning research and application areas. In the real world, people react to stimuli differently, and the duration of brain activities varies between individuals. Therefore, the length of EEG recordings in trials gathered in the experiment is variable. However, current approaches either fix the length of EEG recordings in each trial which would lose information hidden in the data or use the sliding window which would consume large computation on overlapped parts of slices. In this paper, we propose TOO (Traverse Only Once), a new approach for processing variable-length EEG trial data. TOO is a convolutional quorum voting approach that breaks the fixed structure of the model through convolutional implementation of sliding windows and the replacement of the fully connected layer by the 1 × 1 convolutional layer. Each output cell generated from 1 × 1 convolutional layer corresponds to each slice created by a sliding time window, which reflects changes in cognitive states. Then, TOO employs quorum voting on output cells and determines the cognitive state representing the entire single trial. Our approach provides an adaptive model for trials of different lengths with traversing EEG data of each trial only once to recognize cognitive states. We design and implement a cognitive experiment and obtain EEG data. Using the data collecting from this experiment, we conducted an evaluation to compare TOO with a state-of-art sliding window end-to-end approach. The results show that TOO yields a good accuracy (83.58%) at the trial level with a much lower computation (11.16%). It also has the potential to be used in variable signal processing in other application areas.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Complexityarrow_drop_down
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    Complexity
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    Complexity
    Article . 2020
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    Complexity
    Article . 2020 . Peer-reviewed
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      Complexity
      Article . 2020
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      Complexity
      Article . 2020 . Peer-reviewed
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    Authors: Liang Wang; Yanfang Li; Paul D. Metzak; Yong He; +1 Authors

    In this study we used functional magnetic resonance imaging to investigate age-related changes in large-scale brain functional networks during memory encoding and recognition in 12 younger and 16 older adults. For each participant, functional brain networks were constructed by computing temporal correlation matrices of 90 brain regions and analyzed using graph theoretical approaches. We found the age-related changes mainly in the long-range connections with widespread reductions associated with aging in the fronto-temporal and temporo-parietal regions, and a few age-related increases in the posterior parietal regions. Graph theoretical analysis revealed that the older adults had longer path lengths linking different regions in the functional brain networks as compared to the younger adults. Further analysis indicated that the increases in shortest path length in the networks were combined with the loss of long-range connections. Finally, we showed that for older adults, frontal areas played reduced roles in the network (reduced regional centrality), whereas several default-mode regions played increased roles relative to younger subjects (increased regional centrality). Together, our results suggest that normal aging is associated with disruption of large-scale brain systems during the performance of memory tasks, which provides novel insights into the understanding of age-related decline in multiple cognitive functions.

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    NeuroImage
    Article . 2010 . Peer-reviewed
    License: Elsevier TDM
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    NeuroImage
    Article . 2009
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      NeuroImage
      Article . 2010 . Peer-reviewed
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      NeuroImage
      Article . 2009
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    Authors: Liying Peng; Liying Peng; Lanfen Lin; Yusen Lin; +26 Authors

    The infant brain undergoes a remarkable period of neural development that is crucial for the development of cognitive and behavioral capacities (Hasegawa et al., 2018). Longitudinal magnetic resonance imaging (MRI) is able to characterize the developmental trajectories and is critical in neuroimaging studies of early brain development. However, missing data at different time points is an unavoidable occurrence in longitudinal studies owing to participant attrition and scan failure. Compared to dropping incomplete data, data imputation is considered a better solution to address such missing data in order to preserve all available samples. In this paper, we adapt generative adversarial networks (GAN) to a new application: longitudinal image prediction of structural MRI in the first year of life. In contrast to existing medical image-to-image translation applications of GANs, where inputs and outputs share a very close anatomical structure, our task is more challenging as brain size, shape and tissue contrast vary significantly between the input data and the predicted data. Several improvements over existing GAN approaches are proposed to address these challenges in our task. To enhance the realism, crispness, and accuracy of the predicted images, we incorporate both a traditional voxel-wise reconstruction loss as well as a perceptual loss term into the adversarial learning scheme. As the differing contrast changes in T1w and T2w MR images in the first year of life, we incorporate multi-contrast images leading to our proposed 3D multi-contrast perceptual adversarial network (MPGAN). Extensive evaluations are performed to assess the qualityand fidelity of the predicted images, including qualitative and quantitative assessments of the image appearance, as well as quantitative assessment on two segmentation tasks. Our experimental results show that our MPGAN is an effective solution for longitudinal MR image data imputation in the infant brain. We further apply our predicted/imputed images to two practical tasks, a regression task and a classification task, in order to highlight the enhanced task-related performance following image imputation. The results show that the model performance in both tasks is improved by including the additional imputed data, demonstrating the usability of the predicted images generated from our approach.

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    Frontiers in Neuroscience
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    Frontiers in Neuroscience
    Article . 2021 . Peer-reviewed
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    DOAJ-Articles
    Article . 2021
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      Frontiers in Neuroscience
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    Authors: Prado, Jérôme; Lu, Jiayan; Liu, Li; Dong, Qi; +2 Authors

    International audience; Multiplication problems involving large numbers (e.g., 9 × 8) are more difficult to solve than problems involving small numbers (e.g., 2 × 3). Behavioral research indicates that this problem-size effect might be due to different factors across countries and educational systems. However, there is no neuroimaging evidence supporting this hypothesis. Here, we compared the neural correlates of the multiplication problem-size effect in adults educated in China and the United States. We found a greater neural problem-size effect in Chinese than American participants in bilateral superior temporal regions associated with phonological processing. However, we found a greater neural problem-size effect in American than Chinese participants in right intra-parietal sulcus (IPS) associated with calculation procedures. Therefore, while the multiplication problem-size effect might be a verbal retrieval effect in Chinese as compared to American participants, it may instead stem from the use of calculation procedures in American as compared to Chinese participants. Our results indicate that differences in educational practices might affect the neural bases of symbolic arithmetic.

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    Europe PubMed Central
    Article . 2013
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    Frontiers in Human Neuroscience
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    Frontiers in Human Neuroscience
    Article . 2013 . Peer-reviewed
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      Frontiers in Human Neuroscience
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      Frontiers in Human Neuroscience
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    Authors: Siqi Bao; Pei Wang; Tony C. W. Mok; Albert C. S. Chung;

    In this paper, a novel 3D deep learning network is proposed for brain MR image segmentation with randomized connection, which can decrease the dependency between layers and increase the network capacity. The convolutional LSTM and 3D convolution are employed as network units to capture the long-term and short-term 3D properties respectively. To assemble these two kinds of spatial-temporal information and refine the deep learning outcomes, we further introduce an efficient graph-based node selection and label inference method. Experiments have been carried out on two publicly available databases and results demonstrate that the proposed method can obtain competitive performances as compared with other state-of-the-art methods.

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    IEEE Transactions on Image Processing
    Article . 2018 . Peer-reviewed
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    Article . 2017
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      IEEE Transactions on Image Processing
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    Authors: Ping He; Xiaohua Xu; Han Zhang; Gang Li; +4 Authors

    Recent advances in MRI have made it easier to collect data for studying human structural and functional connectivity networks. Computational methods can reveal complex spatiotemporal dynamics of the human developing brain. In this paper, we propose a Developmental Meta-network Decomposition (DMD) method to decompose a series of longitudinal networks into a set of Developmental Meta-networks (DMs), which reveal the underlying changes in connectivity over development. DMD circumvents the limitations of traditional static network decomposition methods by providing a novel exploratory approach to capture the spatiotemporal dynamics of longitudinal networks. We apply this method to longitudinal structural correlation networks of cortical thickness across subjects at 3-20 years of age, and identify four DMs that smoothly evolve over three stages, i.e., 3-6, 7-12, and 13-20 years of age. We analyze and highlight the characteristic connections of each DM in relation to brain development.

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    Europe PubMed Central
    Article . 2018
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    Frontiers in Neuroinformatics
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    Frontiers in Neuroinformatics
    Article . 2018 . Peer-reviewed
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      Frontiers in Neuroinformatics
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    Authors: Jiewei Li; Shing-Chow Chan; Zhong Liu; Chunqi Chang;

    This paper proposes a novel adaptive fading Kalman filter (AF-KF)-based approach to time-varying brain spectral and functional connectivity analyses of event-related multi-channel electroencephalogram (EEG) signals. By modeling the EEG signals as a time-varying (TV) multivariate autoregressive (MVAR) process, a new AF-KF with variable number of measurements (AF-KF-VNM) is proposed for estimating the spectra of the EEG signals and identifying their functional connectivity. The proposed AF-KF-VNM algorithm uses a new adaptive fading method to adaptively update the model parameters of the KF for improved state estimation and utilizes multiple measurements for better adaptation to the nonstationary signal observations. Experimental results on a simulated data for modeling the TV directed interactions in multivariate neural data show that the proposed AF-KF-VNM method yields better tracking performance than other approaches tested. The proposed algorithm is then integrated into a novel methodology for combined functional Magnetic Resonance Imaging (fMRI) activation maps and EEG spectrum analyses for quantifying the differences in spectrum contents and information flows between the target and standard conditions in a visual oddball paradigm. The results and findings show that the proposed methodology agrees well with the literature and is capable of revealing significant frequency components and information flow involved as well as their time variations.

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    IEEE Access
    Article . 2020 . Peer-reviewed
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    IEEE Access
    Article . 2020
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      IEEE Access
      Article . 2020 . Peer-reviewed
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      IEEE Access
      Article . 2020
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Long, Jinyi; Wang, Jue; Yu, Tianyou;

    The hybrid brain computer interface (BCI) based on motor imagery (MI) and P300 has been a preferred strategy aiming to improve the detection performance through combining the features of each. However, current methods used for combining these two modalities optimize them separately, which does not result in optimal performance. Here, we present an efficient framework to optimize them together by concatenating the features of MI and P300 in a block diagonal form. Then a linear classifier under a dual spectral norm regularizer is applied to the combined features. Under this framework, the hybrid features of MI and P300 can be learned, selected, and combined together directly. Experimental results on the data set of hybrid BCI based on MI and P300 are provided to illustrate competitive performance of the proposed method against other conventional methods. This provides an evidence that the method used here contributes to the discrimination performance of the brain state in hybrid BCI.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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    Europe PubMed Central
    Article . 2017
    Data sources: PubMed Central
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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      Europe PubMed Central
      Article . 2017
      Data sources: PubMed Central
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Jing Xiang; Qian Luo; Rupesh Kotecha; Abraham M. Korman; +5 Authors

    Recent studies have revealed the importance of high-frequency brain signals (>70 Hz). One challenge of high-frequency signal analysis is that the size of time-frequency representation of high-frequency brain signals could be larger than 1 terabytes (TB), which is beyond the upper limits of a typical computer workstation’s memory (<196 GB). The aim of the present study is to develop a new method to provide greater sensitivity in detecting high-frequency magnetoencephalography (MEG) signals in a single automated and versatile interface, rather than the more traditional, time-intensive visual inspection methods, which may take up to several days. To address the aim, we developed a new method, accumulated source imaging, defined as the volumetric summation of source activity over a period of time. This method analyzes signals in both low- (1~70 Hz) and high-frequency (70~200 Hz) ranges at source levels. To extract meaningful information from MEG signals at sensor space, the signals were decomposed to channel-cross-channel matrix (CxC) representing the spatiotemporal patterns of every possible sensor-pair. A new algorithm was developed and tested by calculating the optimal CxC and source location-orientation weights for volumetric source imaging, thereby minimizing multi-source interference and reducing computational cost. The new method was implemented in C/C++ and tested with MEG data recorded from clinical epilepsy patients. The results of experimental data demonstrated that accumulated source imaging could effectively summarize and visualize MEG recordings within 12.7 hours by using approximately 10 GB of computer memory. In contrast to the conventional method of visually identifying multi-frequency epileptic activities that traditionally took 2-3 days and used 1-2 TB storage, the new approach can quantify epileptic abnormalities in both low- and high-frequency ranges at source levels, using much less time and computer memory.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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    Europe PubMed Central
    Article . 2014
    Data sources: PubMed Central
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Frontiers in Neuroinformatics
    Article
    License: CC BY
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    Frontiers in Neuroinformatics
    Article . 2014 . Peer-reviewed
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      Europe PubMed Central
      Article . 2014
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      Frontiers in Neuroinformatics
      Article
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      Frontiers in Neuroinformatics
      Article . 2014 . Peer-reviewed