EEGLAB

EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. EEGLAB is an interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. First developed on Matlab 5.3 under Linux, EEGLAB runs on Matlab v5 and higher under Linux, Unix, Windows, and Mac OS X (Matlab 7+ recommended).


References in zbMATH (referenced in 51 articles )

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  1. Bénar, Christian G.; Grova, C.; Jirsa, V. K.; Lina, J. M.: Differences in MEG and EEG power-law scaling explained by a coupling between spatial coherence and frequency: a simulation study (2019)
  2. Daniel McDuff, Ethan Blackford: iPhys: An Open Non-Contact Imaging-Based Physiological Measurement Toolbox (2019) arXiv
  3. Mobarezpour, Jahangir; Khosrowabadi, Reza; Ghaderi, Reza; Navi, Keivan: Classification of EEG-based motor imagery BCI by using ECOC (2019)
  4. Pfister, Niklas; Weichwald, Sebastian; Bühlmann, Peter; Schölkopf, Bernhard: Robustifying independent component analysis by adjusting for group-wise stationary noise (2019)
  5. Fanny Grosselin; Xavier Navarro-Sune; Mathieu Raux; Thomas Similowski; Mario Chavez: CARE-rCortex: a Matlab toolbox for the analysis of CArdio-REspiratory-related activity in the Cortex (2018) arXiv
  6. Liu, Hao; Zhang, Puming: Phase synchronization dynamics of neural network during seizures (2018)
  7. Li, Weifeng; Shen, Yuxiaotong; Zhang, Jie; Huang, Xiaolin; Chen, Ying; Ge, Yun: Common interferences removal from dense multichannel EEG using independent component decomposition (2018)
  8. Sweeney-Reed, Catherine M.; Nasuto, Slawomir J.; Vieira, Marcus F.; Andrade, Adriano O.: Empirical mode decomposition and its extensions applied to EEG analysis: a review (2018)
  9. Veretennikova, Maria A.; Sikorskii, Alla; Boivin, Michael J.: Parameters of stochastic models for electroencephalogram data as biomarkers for child’s neurodevelopment after cerebral malaria (2018)
  10. Frady, E. Paxon; Kapoor, Ashish; Horvitz, Eric; Kristan, William B. jun.: Scalable semisupervised functional neurocartography reveals canonical neurons in behavioral networks (2016)
  11. Hamedi, Mahyar; Salleh, Sh-Hussain; Noor, Alias Mohd: Electroencephalographic motor imagery brain connectivity analysis for BCI: a review (2016)
  12. Schillinger, Frieder L.; De Smedt, Bert; Grabner, Roland H.: When errors count: an EEG study on numerical error monitoring under performance pressure (2016) MathEduc
  13. Lainscsek, Claudia; Hernandez, Manuel E.; Poizner, Howard; Sejnowski, Terrence J.: Delay differential analysis of electroencephalographic data (2015)
  14. Selvan, S. Easter; George, S. Thomas; Balakrishnan, R.: Range-based ICA using a nonsmooth quasi-Newton optimizer for electroencephalographic source localization in focal epilepsy (2015)
  15. Hinault, Thomas; Dufau, Stéphane; Lemaire, Patrick: Sequential modulations of poorer-strategy effects during strategy execution: an event-related potential study in arithmetic (2014) MathEduc
  16. Kang, Hyohyeong; Choi, Seungjin: Bayesian common spatial patterns for multi-subject EEG classification (2014)
  17. Li, Zhaohui; Ouyang, Gaoxiang; Yao, Li; Li, Xiaoli: Estimating the correlation between bursty spike trains and local field potentials (2014) ioport
  18. Fiori, Marina; Lintas, Alessandra; Mesrobian, Sarah; Villa, Alessandro E. P.: Effect of emotion and personality on deviation from purely rational decision-making (2013) ioport
  19. Held, Pascal; Moewes, Christian; Braune, Christian; Kruse, Rudolf; Sabel, Bernhard A.: Advanced analysis of dynamic graphs in social and neural networks (2013) ioport
  20. Moewes, Christian; Kruse, Rudolf; Sabel, Bernhard A.: Analysis of dynamic brain networks using VAR models (2013) ioport

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Further publications can be found at: http://sccn.ucsd.edu/eeglab/refs.html