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Alexander von Lühmann

Showing results (1-10 of 24) with videos related to

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Imaging Neuroscience (Cambridge, Mass.)|November 10, 2025
Multimodal fNIRS-EEG sensor fusion: Review of data-driven methods and perspective for naturalistic brain imagingTomás Codina, Benjamin Blankertz, Alexander von Lühmann
Imaging Neuroscience (Cambridge, Mass.)|January 12, 2026
Data-driven head model individualization from digitized electrode positions or photogrammetry improves M/EEG source localization accuracyNils Harmening, Alexander von Lühmann, Benjamin Blankertz
Journal of Neural Engineering|June 5, 2026
fNIRS Single-trial decoding improves systematically with higher optode density, model-based noise regression, and image reconstructionThomas Fischer, Eike Middell, Shakiba Moradi, et al.
Frontiers in Human Neuroscience|December 1, 2015
Toward a Wireless Open Source Instrument: Functional Near-infrared Spectroscopy in Mobile Neuroergonomics and BCI ApplicationsAlexander von Lühmann, Christian Herff, Dominic Heger, et al.
Neuroimage|June 17, 2019
A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared SpectroscopyAlexander von Lühmann, Zois Boukouvalas, Klaus-Robert Müller, et al.
Neuroimage. Clinical|February 5, 2021
Optical brain imaging and its application to neurofeedbackSurjo R Soekadar, Simon H Kohl, Masahito Mihara, et al.
Frontiers in Human Neuroscience|March 6, 2020
Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A PerspectiveAlexander von Lühmann, Antonio Ortega-Martinez, David A Boas, et al.
Neurophotonics|May 19, 2023
Wearable, high-density fNIRS and diffuse optical tomography technologies: a perspectiveErnesto E Vidal-Rosas, Alexander von Lühmann, Paola Pinti, et al.
Neuroimage|December 25, 2019
Improved physiological noise regression in fNIRS: A multimodal extension of the General Linear Model using temporally embedded Canonical Correlation AnalysisAlexander von Lühmann, Xinge Li, Klaus-Robert Müller, et al.
Biorxiv : the Preprint Server for Biology|February 8, 2024
fNIRS Dataset During Complex Scene AnalysisMatthew Ning, Sudan Duwadi, Meryem A Yücel, et al.
Pageof 3

Showing results (1-10 of 24) with videos related to

Sort By:
Pageof 3
Imaging Neuroscience (Cambridge, Mass.)|November 10, 2025
Multimodal fNIRS-EEG sensor fusion: Review of data-driven methods and perspective for naturalistic brain imagingTomás Codina, Benjamin Blankertz, Alexander von Lühmann
Imaging Neuroscience (Cambridge, Mass.)|January 12, 2026
Data-driven head model individualization from digitized electrode positions or photogrammetry improves M/EEG source localization accuracyNils Harmening, Alexander von Lühmann, Benjamin Blankertz
Journal of Neural Engineering|June 5, 2026
fNIRS Single-trial decoding improves systematically with higher optode density, model-based noise regression, and image reconstructionThomas Fischer, Eike Middell, Shakiba Moradi, et al.
Frontiers in Human Neuroscience|December 1, 2015
Toward a Wireless Open Source Instrument: Functional Near-infrared Spectroscopy in Mobile Neuroergonomics and BCI ApplicationsAlexander von Lühmann, Christian Herff, Dominic Heger, et al.
Neuroimage|June 17, 2019
A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared SpectroscopyAlexander von Lühmann, Zois Boukouvalas, Klaus-Robert Müller, et al.
Neuroimage. Clinical|February 5, 2021
Optical brain imaging and its application to neurofeedbackSurjo R Soekadar, Simon H Kohl, Masahito Mihara, et al.
Frontiers in Human Neuroscience|March 6, 2020
Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A PerspectiveAlexander von Lühmann, Antonio Ortega-Martinez, David A Boas, et al.
Neurophotonics|May 19, 2023
Wearable, high-density fNIRS and diffuse optical tomography technologies: a perspectiveErnesto E Vidal-Rosas, Alexander von Lühmann, Paola Pinti, et al.
Neuroimage|December 25, 2019
Improved physiological noise regression in fNIRS: A multimodal extension of the General Linear Model using temporally embedded Canonical Correlation AnalysisAlexander von Lühmann, Xinge Li, Klaus-Robert Müller, et al.
Biorxiv : the Preprint Server for Biology|February 8, 2024
fNIRS Dataset During Complex Scene AnalysisMatthew Ning, Sudan Duwadi, Meryem A Yücel, et al.
Pageof 3