Search Results - "Journal of neuroscience methods"

Data augmentation for deep-learning-based electroencephalography by Lashgari, Elnaz, Liang, Dehua, Maoz, Uri

“…•Data augmentation (DA) is increasingly used with deep learning (DL) on EEG.•It enhances decoding accuracy left unexplained by 29 % on average on the datasets…”
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NoRMCorre: An online algorithm for piecewise rigid motion correction of calcium imaging data by Pnevmatikakis, Eftychios A., Giovannucci, Andrea

“…•A method for non-rigid motion registration of calcium imaging data is proposed.•The method is fast and can be run online on large volumes of streaming…”
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The first step for neuroimaging data analysis: DICOM to NIfTI conversion by Li, Xiangrui, Morgan, Paul S., Ashburner, John, Smith, Jolinda, Rorden, Christopher

“…•Introduce conversion tools for different vendors.•Explain conversion basics.•Present methods to detect and correctproblems. Clinical imaging data are…”
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A practical guide to the selection of independent components of the electroencephalogram for artifact correction by Chaumon, Maximilien, Bishop, Dorothy V M, Busch, Niko A

“…Electroencephalographic data are easily contaminated by signals of non-neural origin. Independent component analysis (ICA) can help correct EEG data for such…”
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Interpretable deep neural networks for single-trial EEG classification by Sturm, Irene, Lapuschkin, Sebastian, Samek, Wojciech, Müller, Klaus-Robert

“…In cognitive neuroscience the potential of deep neural networks (DNNs) for solving complex classification tasks is yet to be fully exploited. The most limiting…”
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Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition by Brunton, Bingni W., Johnson, Lise A., Ojemann, Jeffrey G., Kutz, J. Nathan

“…•Dynamic mode decomposition (DMD) extracts dynamically coherent patterns from large-scale neuronal recordings.•Multiple, distinct sleep spindle networks are…”
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NODDI in clinical research by Kamiya, Kouhei, Hori, Masaaki, Aoki, Shigeki

“…•We summarized rationale to apply NODDI for clinical research.•We surveyed applications of NODDI in the studies of diseases and aging/development.•Most studies…”
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Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy by Combrisson, Etienne, Jerbi, Karim

“…Machine learning techniques are increasingly used in neuroscience to classify brain signals. Decoding performance is reflected by how much the classification…”
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Digital filter design for electrophysiological data – a practical approach by Widmann, Andreas, Schröger, Erich, Maess, Burkhard

“…•Filtering may introduce significant distortions and bias results.•Filter responses, types, and parameters are introduced and explained.•Implementations in…”
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The MVGC multivariate Granger causality toolbox: A new approach to Granger-causal inference by Barnett, Lionel, Seth, Anil K.

“…•Matlab© Toolbox for accurate and efficient calculation of Granger causality.•Calculate Granger causalities (conditional and unconditional) in both time and…”
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DeepVOG: Open-source pupil segmentation and gaze estimation in neuroscience using deep learning by Yiu, Yuk-Hoi, Aboulatta, Moustafa, Raiser, Theresa, Ophey, Leoni, Flanagin, Virginia L., zu Eulenburg, Peter, Ahmadi, Seyed-Ahmad

“…•We propose novel tools for video-oculography powered by deep-learning.•Robust pupil segmentation using fully convolutional neural networks (FCNN).•Gaze…”
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The Psychology Experiment Building Language (PEBL) and PEBL Test Battery by Mueller, Shane T., Piper, Brian J.

“…We briefly describe the Psychology Experiment Building Language (PEBL), an open source software system for designing and running psychological experiments. We…”
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Deep residual learning for neuroimaging: An application to predict progression to Alzheimer’s disease by Abrol, Anees, Bhattarai, Manish, Fedorov, Alex, Du, Yuhui, Plis, Sergey, Calhoun, Vince

“…•Progression to AD was predicted from deep residual learning on baseline MRI scans.•Prediction accuracy of 83 % was achieved using domain transfer…”
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The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data by Kasper, Lars, Bollmann, Steffen, Diaconescu, Andreea O., Hutton, Chloe, Heinzle, Jakob, Iglesias, Sandra, Hauser, Tobias U., Sebold, Miriam, Manjaly, Zina-Mary, Pruessmann, Klaas P., Stephan, Klaas E.

“…[Display omitted] •A Toolbox to integrate preprocessing of physiological data and fMRI noise modeling.•Robust preprocessing via iterative peak detection, shown…”
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A hybrid Convolutional and Recurrent Neural Network for Hippocampus Analysis in Alzheimer's Disease by Li, Fan, Liu, Manhua

“…•Propose a hybrid convolutional and recurrent neural network for hippocampus analysis•3D image patch from hippocampus is divided into internal and external…”
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Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines by Lajnef, Tarek, Chaibi, Sahbi, Ruby, Perrine, Aguera, Pierre-Emmanuel, Eichenlaub, Jean-Baptiste, Samet, Mounir, Kachouri, Abdennaceur, Jerbi, Karim

“…Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although…”
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A systematic review of EEG source localization techniques and their applications on diagnosis of brain abnormalities by Asadzadeh, Shiva, Yousefi Rezaii, Tohid, Beheshti, Soosan, Delpak, Azra, Meshgini, Saeed

“…[Display omitted] •It is revealed that more than 42 different statistical method are proposed to localize brain activity sources using EEG signals.•Sparse…”
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DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI by Riaz, Atif, Asad, Muhammad, Alonso, Eduardo, Slabaugh, Greg

“…•A novel deep learning-based method for the classification of the ADHD.•The importance of functional connectivity for classification of ADHD is evaluated.•The…”
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Neuroinflammation in animal models of traumatic brain injury by Chiu, Chong-Chi, Liao, Yi-En, Yang, Ling-Yu, Wang, Jing-Ya, Tweedie, David, Karnati, Hanuma K., Greig, Nigel H., Wang, Jia-Yi

“…Traumatic brain injury (TBI) is a leading cause of mortality and morbidity worldwide. Neuroinflammation is prominent in the short and long-term consequences of…”
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Tensor decomposition of EEG signals: A brief review by Cong, Fengyu, Lin, Qiu-Hua, Kuang, Li-Dan, Gong, Xiao-Feng, Astikainen, Piia, Ristaniemi, Tapani

“…•EEG signals are naturally born with multi modes.•EEG signals can be represented by the high-order multi-way array, tensor.•Tensor of EEG can be exploited by…”
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