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Martin Spüler
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- affiliation (PhD 2013): Eberhard Karls University of Tübingen, Department of Computer Science
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2020 – today
- 2020
- [j4]Sebastian Grissmann
, Martin Spüler
, Josef Faller, Tanja Krumpe
, Thorsten O. Zander, Augustin Kelava
, Christian Scharinger
, Peter Gerjets:
Context Sensitivity of EEG-Based Workload Classification Under Different Affective Valence. IEEE Trans. Affect. Comput. 11(2): 327-334 (2020)
2010 – 2019
- 2019
- [j3]Katrin Sippel
, Julia Moser
, Franziska Schleger
, Hubert Preissl
, Wolfgang Rosenstiel, Martin Spüler:
Fully Automated R-peak Detection Algorithm (FLORA) for fetal magnetoencephalographic data. Comput. Methods Programs Biomed. 173: 35-41 (2019) - [c17]Tanja Krumpe, Wolfgang Rosenstiel, Martin Spüler:
Prediction of item familiarity based on ERPs. BCI 2019: 1-6 - [c16]Katrin Sippel
, Julia Moser
, Franziska Schleger, Diana I. Escalona-Vargas
, Hubert Preissl
, Wolfgang Rosenstiel, Martin Spüler:
Fully Automated Subtraction of Heart Activity for Fetal Magnetoencephalography Data. EMBC 2019: 5685-5689 - 2017
- [c15]Tanja Krumpe, Katrin Baumgaertner, Wolfgang Rosenstiel, Martin Spüler:
Non-stationarity and Inter-subject variability of EEG characteristics in the Context of BCI Development. GBCIC 2017 - [c14]Sebastian Nagel, Wolfgang Rosenstiel, Martin Spüler:
Random Visual evoked potentials (Rvep) for brain-Computer Interface (BCI) control. GBCIC 2017 - [c13]Martin Spüler:
Spatial filtering of EEG as a Regression Problem. GBCIC 2017 - [c12]Gustavo Vergani Dambros, Judith Ungewiss, Thomas C. Kübler, Enkelejda Kasneci, Martin Spüler:
Monitoring Response Quality During Campimetry Via Eye-Tracking. IUI Companion 2017: 61-64 - 2016
- [c11]Martin Spüler, Nerea Irastorza-Landa
, Andrea Sarasola-Sanz
, Ander Ramos-Murguialday
:
Extracting Muscle Synergy Patterns from EMG Data Using Autoencoders. ICANN (2) 2016: 47-54 - 2015
- [c10]Martin Spüler, Andrea Sarasola-Sanz
, Niels Birbaumer
, Wolfgang Rosenstiel, Ander Ramos-Murguialday
:
Comparing metrics to evaluate performance of regression methods for decoding of neural signals. EMBC 2015: 1083-1086 - [c9]Martin Spüler:
A Brain-Computer Interface (BCI) system to use arbitrary Windows applications by directly controlling mouse and keyboard. EMBC 2015: 1087-1090 - [c8]Farid Shiman, Nerea Irastorza-Landa
, Andrea Sarasola-Sanz
, Martin Spüler, Niels Birbaumer
, Ander Ramos-Murguialday
:
Towards decoding of functional movements from the same limb using EEG. EMBC 2015: 1922-1925 - [c7]Martin Spüler, Sebastian Nagel, Wolfgang Rosenstiel:
A spiking neuronal model learning a motor control task by reinforcement learning and structural synaptic plasticity. IJCNN 2015: 1-8 - 2014
- [j2]Armin Walter, Georgios Naros
, Martin Spüler, Alireza Gharabaghi
, Wolfgang Rosenstiel, Martin Bogdan:
Decoding stimulation intensity from evoked ECoG activity. Neurocomputing 141: 46-53 (2014) - [c6]Martin Spüler, Wolfgang Rosenstiel, Martin Bogdan:
Predicting Wrist Movement Trajectory from Ipsilesional ECoG in Chronic Stroke Patients. NEUROTECHNIX 2014: 38-45 - 2013
- [b1]Martin Spüler:
Assessing the Benefit of Adaptive Brain-Computer Interfacing. Eberhard Karls University of Tübingen, Verlag Dr. Hut 2013 - [c5]Armin Walter, Georgios Naros, Martin Spüler, Alireza Gharabaghi, Wolfgang Rosenstiel, Martin Bogdan:
Decoding stimulation intensity from evoked ECoG activity using support vector regression. ESANN 2013 - [c4]Martin Spüler, Wolfgang Rosenstiel, Martin Bogdan:
Unsupervised Online Calibration of a c-VEP Brain-Computer Interface (BCI). ICANN 2013: 224-231 - [c3]Armin Walter, Georgios Naros
, Martin Spüler, Wolfgang Rosenstiel, Alireza Gharabaghi, Martin Bogdan:
Dynamics of a Stimulation-evoked ECoG Potential During Stroke Rehabilitation - A Case Study. NEUROTECHNIX 2013: 241-248 - 2012
- [j1]Martin Spüler, Wolfgang Rosenstiel, Martin Bogdan:
Principal component based covariate shift adaption to reduce non-stationarity in a MEG-based brain-computer interface. EURASIP J. Adv. Signal Process. 2012: 129 (2012) - [c2]Martin Spüler, Wolfgang Rosenstiel, Martin Bogdan:
One Class SVM and Canonical Correlation Analysis increase performance in a c-VEP based Brain-Computer Interface (BCI). ESANN 2012 - [c1]Martin Spüler, Wolfgang Rosenstiel, Martin Bogdan:
Adaptive SVM-Based Classification Increases Performance of a MEG-Based Brain-Computer Interface (BCI). ICANN (1) 2012: 669-676
Coauthor Index
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