M. Tangermann

发表

Towards Noninvasive Hybrid Brain–Computer Interfaces: Framework, Practice, Clinical Application, and Beyond

Febo Cincotti, José del R. Millán, Donatella Mattia, 2015, Proceedings of the IEEE.

A New Auditory Multi-Class Brain-Computer Interface Paradigm: Spatial Hearing as an Informative Cue

B. Blankertz, M. Tangermann, M. Schreuder, 2010, PloS one.

Voluntary brain regulation and communication with electrocorticogram signals

B. Schölkopf, T. N. Lal, C. Elger, 2008, Epilepsy & Behavior.

Facing the challenge: Bringing brain-computer interfaces to end-users

Donatella Mattia, Michael Tangermann, Rüdiger Rupp, 2013, Artif. Intell. Medicine.

Natural stimuli improve auditory BCIs with respect to ergonomics and performance.

Michael Tangermann, Sven Dähne, Johannes Höhne, 2012, Journal of neural engineering.

Initial results of a high-speed spatial auditory BCI

B. Blankertz, M. Tangermann, E. M. Schreuder, 2009 .

Deep learning with convolutional neural networks for brain mapping and decoding of movement-related information from the human EEG pdf

Wolfram Burgard, Tonio Ball, Frank Hutter, 2017, ArXiv.

Pre-Trial EEG-Based Single-Trial Motor Performance Prediction to Enhance Neuroergonomics for a Hand Force Task

J. Reis, M. Tangermann, A. Meinel, 2016, Front. Hum. Neurosci..

The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials

Klaus-Robert Müller, Christa Neuper, Gert Pfurtscheller, 2004, IEEE Transactions on Biomedical Engineering.

Practicing fast-decision BCI using a "goalkeeper" paradigm

B. Blankertz, M. Tangermann, L. Ramsey, 2009, BMC Neuroscience.

Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach

Wolfgang Rosenstiel, Bernhard Schölkopf, Michael Bensch, 2007 .

Machine learning for real-time single-trial EEG-analysis: From brain–computer interfacing to mental state monitoring

Klaus-Robert Müller, Benjamin Blankertz, Michael Tangermann, 2008, Journal of Neuroscience Methods.

On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP

Klaus-Robert Müller, Michael Tangermann, Stefan Debener, 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals

M. Tangermann, S. Haufe, I. Winkler, 2011, Behavioral and Brain Functions.

Jan B.F. van Erp, The Netherlands Organization for Applied Scientific Research (TNO)

M. Tangermann, F. Lotte, Bordeaux Sud-Ouest, 2012 .

Estimating Noise and Dimensionality in BCI Data Sets : Towards Illiteracy Comprehension

Klaus-Robert Müller, Michael Tangermann, Claudia Sannelli, 2008 .

Oscillatory EEG Components and their Persistence in Time and Frequency

M. Tangermann, A. Meinel, 2017 .

Learning from label proportions in brain-computer interfaces

Klaus-Robert Müller, Michael Tangermann, Konstantin Schmid, 2017 .

BCI Applications for the General Population

Klaus-Robert Müller, Benjamin Blankertz, Michael Tangermann, 2012 .

True Zero-Training Brain-Computer Interfacing – An Online Study

Klaus-Robert Müller, Benjamin Schrauwen, Michael Tangermann, 2014, PloS one.

Real-time detection of neural oscillation bursts allows behaviourally relevant neurofeedback

M. Tangermann, I. Diester, Artur Schneider, 2020, Communications Biology.

A Pilot Study on Data-Driven Adaptive Deep Brain Stimulation in Chronically Implanted Essential Tremor Patients

Jeffrey A. Herron, H. Chizeck, J. Herron, 2020, Frontiers in Human Neuroscience.

Making Brain-Computer Interfaces robust , reliable and adaptive with Learning from Label Proportions

D. Hübner, M. Tangermann, T. Verhoeven, 2016 .

Decoding cognitive brain states

Stefan Haufe, Gabriel Curio, Benjamin Blankertz, 2013, 2013 International Winter Workshop on Brain-Computer Interface (BCI).

Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges

C. Neuper, D. Mattia, R. Murray-Smith, 2010, Front. Neurosci..

Mining Within-Trial Oscillatory Brain Dynamics to Address the Variability of Optimized Spatial Filters

Henrich Kolkhorst, Michael Tangermann, Andreas Meinel, 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Method to assess the functional role of noisy brain signals by mining envelope dynamics pdf

Michael Tangermann, Henrich Kolkhorst, Andreas Meinel, 2018, ArXiv.

Post-hoc Labeling of Arbitrary M/EEG Recordings for Data-Efficient Evaluation of Neural Decoding Methods

Michael Tangermann, Sebastián Castaño-Candamil, Andreas Meinel, 2019, Front. Neuroinform..

Introducing block-Toeplitz covariance matrices to remaster linear discriminant analysis for event-related potential brain–computer interfaces

M. Tangermann, Jan Sosulski, 2022, Journal of neural engineering.

The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology

Stefan Haufe, Klaus-Robert Müller, Gabriel Curio, 2010, Front. Neurosci..

Review of the BCI Competition IV

Ad Aertsen, Hubert Preissl, Clemens Brunner, 2012, Front. Neurosci..

Detecting Mental States by Machine Learning Techniques: The Berlin Brain-Computer Interface

Klaus-Robert Müller, Gabriel Curio, Benjamin Blankertz, 2009 .

The Berlin Brain-Computer Interface

Klaus-Robert Müller, Gabriel Curio, Benjamin Blankertz, 2008, WCCI.

Classifying Event-Related Desynchronization in EEG, ECoG and MEG Signals

Bernhard Schölkopf, Thilo Hinterberger, Niels Birbaumer, 2006, DAGM-Symposium.

Robust EEG Channel Selection across Subjects for Brain-Computer Interfaces

Bernhard Schölkopf, Wolfgang Rosenstiel, Thilo Hinterberger, 2005, EURASIP J. Adv. Signal Process..

Methods Towards Invasive Human Brain Computer Interfaces

Bernhard Schölkopf, Wolfgang Rosenstiel, Thilo Hinterberger, 2004, NIPS.

Support vector channel selection in BCI

Bernhard Schölkopf, Jason Weston, Thilo Hinterberger, 2004, IEEE Transactions on Biomedical Engineering.

An Easy-to-Use and Fast Assessment of Patient-Specific DBS-Induced Changes in Hand Motor Control in Parkinson’s Disease

Volker A. Coenen, Sebastián Castaño-Candamil, Tobias Piroth, 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Real-time detection of neural oscillation bursts allows behaviourally relevant neurofeedback

M. Tangermann, I. Diester, Artur Schneider, 2020, Communications Biology.

Deep learning with convolutional neural networks for EEG decoding and visualization

Wolfram Burgard, Tonio Ball, Frank Hutter, 2017, Human brain mapping.

Workshops of the Sixth International Brain–Computer Interface Meeting: brain–computer interfaces past, present, and future

Ricardo Chavarriaga, Anton Nijholt, Reinhold Scherer, 2017, Brain computer interfaces.

Brain-Computer Interfaces: Beyond Medical Applications

Fabien Lotte, Michael Tangermann, Jan B. F. van Erp, 2012, Computer.

Eyes-Closed Increases the Usability of Brain-Computer Interfaces Based on Auditory Event-Related Potentials

Michael Tangermann, David Hübner, Albrecht Schall, 2018, Front. Hum. Neurosci..

Robust artifactual independent component classification for BCI practitioners

Stephanie Brandl, Franziska Horn, Michael Tangermann, 2014, Journal of neural engineering.

Artifact-Insensitivity of CSP in Motor Imagery BCI

Michael Tangermann, Irene Winkler, M. Tangermann, 2011 .

Listen, You are Writing! Speeding up Online Spelling with a Dynamic Auditory BCI

M. Tangermann, M. Schreuder, T. Rost, 2011, Front. Neurosci..

Designing Future BCIs: Beyond the Bit Rate

Roderick Murray-Smith, Michael Tangermann, Johannes Höhne, 2012 .

A P300-based image browser with automatic stimulation optimization

Roderick Murray-Smith, Michael Tangermann, Martijn Schreuder, 2010 .

Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future.

Ricardo Chavarriaga, Clemens Brunner, Fabien Lotte, 2014, Brain computer interfaces.

A simulated environment for early development stages of reinforcement learning algorithms for closed-loop deep brain stimulation

Michael Tangermann, Sebastián Castaño-Candamil, Mara Vaihinger, 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

A Simulated Environment for Studying Partial observability in Novel adaptive Deep brain stimulation

Michael Tangermann, Sebastián Castaño-Candamil, Mara Vaihinger, 2019, GBCIC.

SPoC: A novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters

Stefan Haufe, Klaus-Robert Müller, Michael Tangermann, 2014, NeuroImage.

Guess What I Attend: Interface-Free Object Selection Using Brain Signals

Wolfram Burgard, Michael Tangermann, Henrich Kolkhorst, 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Identifying controllable cortical neural markers with machine learning for adaptive deep brain stimulation in Parkinson’s disease

M. Tangermann, V. Coenen, S. Castaño-Candamil, 2020, NeuroImage: Clinical.

Workshops of the Seventh International Brain-Computer Interface Meeting: Not Getting Lost in Translation.

Ricardo Chavarriaga, Anton Nijholt, Matthias Hohmann, 2019, Brain computer interfaces.

Aphasia recovery by language training using a brain–computer interface: a proof-of-concept study

C. Weiller, D. Hübner, M. Tangermann, 2022, Brain communications.

Challenging the Assumption that auditory Event-Related potentials are Independent and identically Distributed

Michael Tangermann, David Hübner, D. Hübner, 2017, GBCIC.

Rethinking BCI Paradigm and Machine Learning Algorithm as a Symbiosis: Zero Calibration, Guaranteed Convergence and High Decoding Performance

Klaus-Robert Müller, Michael Tangermann, David Hübner, 2019, Brain-Computer Interface Research.

Towards Zero Training for Brain-Computer Interfacing

Klaus-Robert Müller, Benjamin Blankertz, Michael Tangermann, 2008, PloS one.

On-line Differentiation Of Neuroelectric Activities: Algorithms And applications

B. Blankertz, G. Curio, G. Dornhege, 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

Playing Pinball with non-invasive BCI

Klaus-Robert Müller, Benjamin Blankertz, Michael Tangermann, 2008, NIPS.

Brain-computer interface controlled gaming: Evaluation of usability by severely motor restricted end-users

Michael Tangermann, Johannes Höhne, Andrea Kübler, 2013, Artif. Intell. Medicine.

Reducing Calibration Time For Brain-Computer Interfaces: A Clustering Approach

Klaus-Robert Müller, Benjamin Blankertz, Michael Tangermann, 2006, NIPS.

Exploring the use of tactile feedback in an ERP-based auditory BCI

Michael Tangermann, Martijn Schreuder, Marieke E. Thurlings, 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Feature selection for brain-computer interfaces

Michael Tangermann, M. Tangermann, 2007 .

Data driven neuroergonomic optimization of BCI stimuli

Benjamin Blankertz, Roderick Murray-Smith, Michael Tangermann, 2011 .

Towards User-Friendly Spelling with an Auditory Brain-Computer Interface: The CharStreamer Paradigm

Michael Tangermann, Johannes Höhne, M. Tangermann, 2014, PloS one.

A Novel 9-Class Auditory ERP Paradigm Driving a Predictive Text Entry System

Benjamin Blankertz, Michael Tangermann, Martijn Schreuder, 2011, Front. Neurosci..

Improving zero-training brain-computer interfaces by mixing model estimators

T Verhoeven, D Hübner, M Tangermann, 2017, Journal of neural engineering.

Unsupervised learning in a BCI chess application using label proportions and expectation-maximization

Michael Tangermann, David Hübner, Albrecht Schall, 2020 .

A novel BCI based rehabilitation approach for aphasia rehabilitation

D. Hübner, M. Tangermann, M. Musso, 2016 .

Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller

Klaus-Robert Müller, Benjamin Schrauwen, Michael Tangermann, 2014, Journal of neural engineering.

Motor Imagery for Severely Motor-Impaired Patients: Evidence for Brain-Computer Interfacing as Superior Control Solution

Klaus-Robert Müller, Michael Tangermann, Johannes Höhne, 2014, PloS one.

Nessi: An EEG-Controlled Web Browser for Severely Paralyzed Patients

Wolfgang Rosenstiel, Michael Bensch, Thilo Hinterberger, 2007, Comput. Intell. Neurosci..

Real-time detection of neural oscillation bursts allows behaviourally relevant neurofeedback

Ilka Diester, Artur Schneider, Mansour Alyahyaey, 2019, Communications Biology.

User-centered design in brain-computer interfaces - A case study

John Williamson, Donatella Mattia, Michael Tangermann, 2013, Artif. Intell. Medicine.

UMM: Unsupervised Mean-difference Maximization

M. Tangermann, Jan Sosulski, 2023, arXiv.org.

Unsupervised Learning for Brain-Computer Interfaces Based on Event-Related Potentials: Review and Online Comparison [Research Frontier]

Klaus-Robert Müller, Michael Tangermann, David Hübner, 2018, IEEE Computational Intelligence Magazine.

Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees

Klaus-Robert Müller, Michael Tangermann, Konstantin Schmid, 2017, PloS one.

Improving Covariance Matrices Derived from Tiny Training Datasets for the Classification of Event-Related Potentials with Linear Discriminant Analysis

Jan Sosulski, Michael Tangermann, Jan-Philipp Kemmer, 2020, Neuroinformatics.

No surprise - fixed sequence event-related potentials for brain-computer interfaces

Michael Tangermann, Martijn Schreuder, Johannes Höhne, 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

EP 65. DBS-induced alpha desynchronization in the subthalamic nucleus of PD patients

M. Tangermann, V. Coenen, S. Castaño-Candamil, 2016, Clinical Neurophysiology.

An embedding for EEG signals learned using a triplet loss

T. Papadopoulo, M. Tangermann, Pierre Guetschel, 2023, ArXiv.

Estimating person-specific neural correlates of mental rotation: A machine learning approach

M. Tangermann, S. Uslu, Claus Vögele, 2023, bioRxiv.

EEG band power predicts single-trial reaction time in a hand motor task

Michael Tangermann, Sven Dähne, Janine Reis, 2015, 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER).

Post-hoc labeling of arbitrary EEG recordings for data-efficient evaluation of neural decoding methods

M. Tangermann, A. Meinel, S. Castaño-Candamil, 2017, ArXiv.

SPoC: A novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters

K. Müller, F. Meinecke, Sven Dähne, 2014, NeuroImage.