Dr. Martin Spüler

Content

Research
Teaching
Publications

Dr. Martin Spüler

Research projects

Context-sensitive neural-controlled hand-exoskeleton for restoration of everyday-capability and autonomy after brain and spinal cord injuries

CONSENS-NHE Logo

The development of robotic systems that interacts with the human nervous system, promise to improve the autonomy, quality of life, and capability of people with disabilities. Brain-Computer Interfaces (BCI) can be used to translate the electrical brain activity into control signals of a robotic exoskeleton. Therefore, it is possible to restore grasping movements of a paralyzed hand by interpreting the neural correlates of the movement. For lack of signal quality, BCI systems based on non-invasive methods, e.g. electroencephalography (EEG), can only be used limited in everyday situations.

In the project CONSENS-NHE we develope a non-invasive and everyday suitable neural-controlled hand-exoskeleton, targeting the compensation of a paralyzed hand, as it can occur after strokes or spinal cord injuries. Within the project the latest methods of machine learning, optical object-recognition, movement analysis, and biological inspired design for robotic systems are combined with neurorehabilitative research. The system will allow people with hand paralysis to grasp and manipulate different objects of everyday life. For direct control of the hand-exoskeleton, the grasping intention is identified based on neural signals measured on the scalp using EEG.

Teaching

Neuronal Computing	Summer 2015 Summer 2016 Summer 2017
Seminar: Machine Learning and Artificial Neural Networks in Biomedical Applications	Summer 2015 Winter 2015 Summer 2016 Winter 2016 Summer 2017 Winter 2017 Summer 2018 Winter 2018

Publications

2020

Decision confidence: EEG correlates of confidence in different phases of an old/ new recognition task

by Tanja Krumpe, Peter Gerjets, Wolfgang Rosenstiel, and Martin Spüler
In Brain-Computer Interfaces, pages 1–16, 2020. [BIB] [DOI] [ABSTRACT]

Abstract: We use an old-new recognition memory task to investigate the correlates of high and low decision confidence throughout all stages of the memory process. Group-level ERP analysis and single-trial and single-subject classification are performed on four stages of the task (information encoding, retrieval, old/new decision formation, and evaluative feedback processing). The study shows that decision confidence is significantly reflected on a group, as well as on a single-trial basis, in all investigated stages at the neural level, except during encoding. The most pronounced differences between high and low confidence can be found in the ERPs during feedback presentation after a correct answer, whereas almost no differences can be found following a wrong answers. In the feedback stage, the two levels of confidence can be separated with classification accuracies of up to 70 % on average, therefore showing potential to be used as a control state in a BCI application.

@article{krumpe2020decision,
  author = {Krumpe, Tanja and Gerjets, Peter and Rosenstiel, Wolfgang and Spüler, Martin},
  year = {2020},
  month = {jan},
  pages = {1–16},
  title = {Decision confidence: EEG correlates of confidence in different phases of an old/ new recognition task},
  journal = {Brain-Computer Interfaces},
  doi = {10.1080/2326263X.2019.1708539},
  month_numeric = {1}
}

2019

World’s fastest brain-computer interface: Combining EEG2Code with deep learning

by Sebastian Nagel and Martin Spüler
In PLOS ONE 14(9): e0221909, 2019. [BIB] [DOI] [ABSTRACT]

Abstract: We present a novel approach based on deep learning for decoding sensory information from non-invasively recorded Electroencephalograms (EEG). It can either be used in a passive Brain-Computer Interface (BCI) to predict properties of a visual stimulus the person is viewing, or it can be used to actively control a BCI application. Both scenarios were tested, whereby an average information transfer rate (ITR) of 701 bit/min was achieved for the passive BCI approach with the best subject achieving an online ITR of 1237 bit/min. Further, it allowed the discrimination of 500,000 different visual stimuli based on only 2 seconds of EEG data with an accuracy of up to 100%. When using the method for an asynchronous self-paced BCI for spelling, an average utility rate of 175 bit/min was achieved, which corresponds to an average of 35 error-free letters per minute. As the presented method extracts more than three times more information than the previously fastest approach, we suggest that EEG signals carry more information than generally assumed. Finally, we observed a ceiling effect such that information content in the EEG exceeds that required for BCI control, and therefore we discuss if BCI research has reached a point where the performance of non-invasive visual BCI control cannot be substantially improved anymore.

@article{SM092019,
  author = {Nagel, Sebastian and Spüler, Martin},
  title = {World’s fastest brain-computer interface: Combining EEG2Code with deep learning},
  journal = {PLOS ONE},
  year = {2019},
  month = {sep},
  volume = {14},
  number = {9},
  pages = {e0221909},
  doi = {10.1371/journal.pone.0221909},
  month_numeric = {9}
}

Asynchronous non-invasive high-speed BCI speller with robust non-control state detection

by Sebastian Nagel and Martin Spüler
In Scientific Reports 9(1): 8269, 2019. [BIB] [DOI] [ABSTRACT]

Abstract: Brain-Computer Interfaces (BCIs) enable users to control a computer by using pure brain activity. Recent BCIs based on visual evoked potentials (VEPs) have shown to be suitable for high-speed communication. However, all recent high-speed BCIs are synchronous, which means that the system works with fixed time slots so that the user is not able to select a command at his own convenience, which poses a problem in real-world applications. In this paper, we present the first asynchronous high-speed BCI with robust distinction between intentional control (IC) and non-control (NC), with a nearly perfect NC state detection of only 0.075 erroneous classifications per minute. The resulting asynchronous speller achieved an average information transfer rate (ITR) of 122.7 bit/min using a 32 target matrix-keyboard. Since the method is based on random stimulation patterns it allows to use an arbitrary number of targets for any application purpose, which was shown by using an 55 target German QWERTZ-keyboard layout which allowed the participants to write an average of 16.1 (up to 30.7) correct case-sensitive letters per minute. As the presented system is the first asynchronous high-speed BCI speller with a robust non-control state detection, it is an important step for moving BCI applications out of the lab and into real-life.

@article{SM062019,
  author = {Nagel, Sebastian and Spüler, Martin},
  title = {Asynchronous non-invasive high-speed BCI speller with robust non-control state detection},
  journal = {Scientific Reports},
  year = {2019},
  month = {jun},
  volume = {9},
  number = {1},
  pages = {8269},
  doi = {10.1038/s41598-019-44645-x},
  month_numeric = {6}
}

Prediction of item familiarity based on ERPs

by Tanja Krumpe, Wolfgang Rosenstiel, and Martin Spüler
In 7th International Conference on Brain-Computer Interface (BCI), South Korea, 2019. [BIB] [DOI] [ABSTRACT]

Abstract: A simple recognition task was used to investigate if the item familiarity of pictures can be predicted based on single trial ERPs during item presentation, to explore the possibility of using this property in a BCI application. Two experimental parts with equal learning phases but different ratios of old and new stimuli in a forced choice memory recognition test have been performed. We were able to predict item familiarity with accuracies above 70 % based on the ERPs elicited during item representation in both parts of the experiment. In some cases, the classification accuracy even exceeds the behavioral accuracy of the subjects. Usage of this property, for example in an education-oriented scenario, seems feasible in a BCI application.

@inproceedings{TWM022019,
  author = {Krumpe, Tanja and Rosenstiel, Wolfgang and Spüler, Martin},
  title = {Prediction of item familiarity based on ERPs },
  booktitle = {7th International Conference on Brain-Computer Interface (BCI), South Korea},
  year = {2019},
  month = {feb},
  organization = {IEEE},
  doi = {10.1109/IWW-BCI.2019.8737330},
  month_numeric = {2}
}

Fully Automated Subtraction of Heart Activity for Fetal Magnetoencephalography Data

by Katrin Sippel, Julia Moser, Franziska Schleger, Diana Escalona-Vargas, Hubert Preissl, Wolfgang Rosenstiel, and Martin Spüler
In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 5685–5689, 2019. [BIB] [DOI] [ABSTRACT]

Abstract: Fetal magnetoencephalography (fMEG) is a method to record human fetal brain signals in pregnant mothers. Nevertheless the amplitude of the fetal brain signal is very small and the fetal brain signal is overlaid by interfering signals mainly caused by maternal and fetal heart activity. Several methods are used to attenuate the interfering signals for the extraction of the fetal brain signal. However currently used methods are often affected by a reduction of the fetal brain signal or redistribution of the fetal brain signal. To overcome this limitation we developed a new fully automated procedure for removal of heart activity (FAUNA) based on Principal Component Analysis (PCA) and Ridge Regression. We compared the results with an orthogonal projection (OP) algorithm which is widely used in fetal research. The analysis was performed on simulated data sets containing spontaneous and averaged brain activity. The new analysis was able to extract fetal brain signals with an increased signal to noise ratio and without redistribution of activity across sensors compared to OP. The attenuation of interfering heart signals in fMEG data was significantly improved by FAUNA and supports fully automated evaluation of fetal brain signal.

@inproceedings{sippel2019fullz,
  title = {Fully Automated Subtraction of Heart Activity for Fetal Magnetoencephalography Data},
  author = {Sippel, Katrin and Moser, Julia and Schleger, Franziska and Escalona-Vargas, Diana and Preissl, Hubert and Rosenstiel, Wolfgang and Spüler, Martin},
  booktitle = {2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
  pages = {5685–5689},
  year = {2019},
  doi = {10.1109/EMBC.2019.8856603},
  organization = {IEEE}
}

Fully automated r-peak detection algorithm (flora) for fetal magnetoencephalographic data

by Katrin Sippel, Julia Moser, Franziska Schleger, Hubert Preissl, Wolfgang Rosenstiel, and Martin Spüler
In Computer methods and programs in biomedicine 173: 35–41. Elsevier, 2019. [BIB] [DOI] [ABSTRACT]

Abstract: Background and objective: Fetal magnetoencephalography (fMEG) is a method for recording fetal brain signals, fetal and maternal heart activity simultaneously. The identification of the R-peaks of the heartbeats forms the basis for later heart rate (HR) and heart rate variability (HRV) analysis. The current procedure for the evaluation of fetal magnetocardiograms (fMCG) is either semi-automated evaluation using template matching (SATM) or Hilbert transformation algorithm (HTA). However, none of the methods available at present works reliable for all datasets. Methods: Our aim was to develop a unitary, responsive and fully automated R-peak detection algorithm (FLORA) that combines and enhances both of the methods used up to now. Results: The evaluation of all methods on 55 datasets verifies that FLORA outperforms both of these methods as well as a combination of the two, which applies in particular to data of fetuses at earlier gestational age. Conclusion: The combined analysis shows that FLORA is capable of providing good, stable and reproducible results without manual intervention.

@article{sippel2019fully,
  title = {Fully automated r-peak detection algorithm (flora) for fetal magnetoencephalographic data},
  author = {Sippel, Katrin and Moser, Julia and Schleger, Franziska and Preissl, Hubert and Rosenstiel, Wolfgang and Spüler, Martin},
  journal = {Computer methods and programs in biomedicine},
  volume = {173},
  pages = {35–41},
  year = {2019},
  doi = {10.1016/j.cmpb.2019.02.016},
  publisher = {Elsevier}
}

2018

Modelling the brain response to arbitrary visual stimulation patterns for a flexible high-speed Brain-Computer Interface

by Sebastian Nagel and Martin Spüler
In PLoS one 13(10): e0206107, 2018. [BIB] [DOI] [ABSTRACT]

Abstract: Visual evoked potentials (VEPs) can be measured in the EEG as response to a visual stimulus. Commonly, VEPs are displayed by averaging multiple responses to a certain stimulus or a classifier is trained to identify the response to a certain stimulus. While the traditional approach is limited to a set of predefined stimulation patterns, we present a method that models the general process of VEP generation and thereby can be used to predict arbitrary visual stimulation patterns from EEG and predict how the brain responds to arbitrary stimulation patterns. We demonstrate how this method can be used to model single-flash VEPs, steady state VEPs (SSVEPs) or VEPs to complex stimulation patterns. It is further shown that this method can also be used for a high-speed BCI in an online scenario where it achieved an average information transfer rate (ITR) of 108.1 bit/min. Furthermore, in an offline analysis, we show the flexibility of the method allowing to modulate a virtually unlimited amount of targets with any desired trial duration resulting in a theoretically possible ITR of more than 470 bit/min.

@article{SM102018,
  author = {Nagel, Sebastian and Spüler, Martin},
  title = {Modelling the brain response to arbitrary visual stimulation patterns for a flexible high-speed Brain-Computer Interface},
  journal = {PLoS one},
  year = {2018},
  month = {oct},
  volume = {13},
  number = {10},
  pages = {e0206107},
  doi = {10.1371/journal.pone.0206107},
  month_numeric = {10}
}

Finding optimal stimulation patterns for BCIs based on visual evoked potentials

by Sebastian Nagel, Wolfgang Rosenstiel, and Martin Spüler
In Proceedings of the 7th International BCI Meeting 2018, pages 164-165, 2018. [BIB] [PDF]

@inproceedings{SWM052018,
  author = {Nagel, Sebastian and Rosenstiel, Wolfgang and Spüler, Martin},
  title = {Finding optimal stimulation patterns for BCIs based on visual evoked potentials},
  booktitle = {Proceedings of the 7th International BCI Meeting 2018},
  year = {2018},
  month = {may},
  pages = {164-165},
  address = {Asilomar, CA},
  month_numeric = {5}
}

Decision confidence: EEG correlates of confidence in different phases of an old/new recognition task

by Tanja Krumpe, Peter Gerjets, Wolfgang Rosenstiel, and Martin Spüler
In Proceedings of the 7th International BCI Meeting, pages 180-181, 2018. [BIB] [DOI] [ABSTRACT]

@inproceedings{TPWM052018,
  author = {Krumpe, Tanja and Gerjets, Peter and Rosenstiel, Wolfgang and Spüler, Martin},
  title = {Decision confidence: EEG correlates of confidence in different phases of an old/new recognition task},
  booktitle = {Proceedings of the 7th International BCI Meeting},
  year = {2018},
  month = {may},
  pages = {180-181},
  address = {Asilomar, CA},
  doi = {10.1080/2326263X.2019.1708539},
  month_numeric = {5}
}

Robustness of single-hand classification against other-hand activity in EEG

by Christian Niethammer, Wolfgang Rosenstiel, and Martin Spüler
In Proceedings of the 7th International BCI Meeting 2018, pages 28-29, 2018. [BIB] [PDF]

@inproceedings{CWM052018,
  author = {Niethammer, Christian and Rosenstiel, Wolfgang and Spüler, Martin},
  title = {Robustness of single-hand classification against other-hand activity in EEG},
  booktitle = {Proceedings of the 7th International BCI Meeting 2018},
  year = {2018},
  month = {may},
  pages = {28-29},
  address = {Asilomar, CA},
  month_numeric = {5}
}

The effect of monitor raster latency on VEPs, ERPs and Brain-Computer Interface performance

by Sebastian Nagel, Werner Dreher, Wolfgang Rosenstiel, and Martin Spüler
In Journal of Neuroscience Methods 295: 45-50, 2018. [BIB] [DOI] [ABSTRACT]

Abstract: BACKGROUND: Visual neuroscience experiments and Brain–Computer Interface (BCI) control often require strict timings in a millisecond scale. As most experiments are performed using a personal computer (PC), the latencies that are introduced by the setup should be taken into account and be corrected. As a standard computer monitor uses a rastering to update each line of the image sequentially, this causes a monitor raster latency which depends on the position, on the monitor and the refresh rate. NEW METHOD: We technically measured the raster latencies of different monitors and present the effects on visual evoked potentials (VEPs) and event-related potentials (ERPs). Additionally we present a method for correcting the monitor raster latency and analyzed the performance difference of a code-modulated VEP BCI speller by correcting the latency. COMPARISON WITH EXISTING METHODS: There are currently no other methods validating the effects of monitor raster latency on VEPs and ERPs. RESULTS: The timings of VEPs and ERPs are directly affected by the raster latency. Furthermore, correcting the raster latency resulted in a significant reduction of the target prediction error from 7.98% to 4.61% and also in a more reliable classification of targets by significantly increasing the distance between the most probable and the second most probable target by 18.23%.

@article{SWWM012018,
  author = {Nagel, Sebastian and Dreher, Werner and Rosenstiel, Wolfgang and Spüler, Martin},
  title = {The effect of monitor raster latency on VEPs, ERPs and Brain-Computer Interface performance},
  journal = {Journal of Neuroscience Methods},
  year = {2018},
  month = {jan},
  volume = {295},
  pages = {45-50},
  doi = {10.1016/j.jneumeth.2017.11.018},
  month_numeric = {1}
}

2017

Random Visual Evoked Pontentials (RVEP) for Brain-Computer Interface (BCI) Control

by Sebastian Nagel, Wolfgang Rosenstiel, and Martin Spüler
In Proceedings of the 7th Graz Brain-Computer Interface Conference 2017, pages 349-354. Verlag der TU Graz, 2017. [BIB] [DOI] [ABSTRACT]

Abstract: Brain-Computer Interfaces (BCIs) enable users to control devices or communicate by using brain activity only. While BCIs based on visual evoked potentials (VEPs) have been shown to achieve high performance, we present a different paradigm for BCI control: random VEP (rVEP). We designed a regression model, trained on VEPs of fully random bit codes. Afterwards, the model is able to perform a bit-wise prediction of a previously unseen stimulation sequence, which in turn can be used for BCI control. In an offline study, the model predicts unknown stimulation sequences with an average ITR of 94.5 bits per minute (bpm) and up to 281 bpm on a single-trial level. In a copy-spelling task, the model achieved an average ITR of 64.3 bpm and up to 115.5 bpm.

@inproceedings{SWM092017,
  author = {Nagel, Sebastian and Rosenstiel, Wolfgang and Spüler, Martin},
  title = {Random Visual Evoked Pontentials (RVEP) for Brain-Computer Interface (BCI) Control},
  booktitle = {Proceedings of the 7th Graz Brain-Computer Interface Conference 2017},
  publisher = {Verlag der TU Graz},
  year = {2017},
  month = {sep},
  pages = {349-354},
  doi = {10.3217/978-3-85125-533-1-64},
  month_numeric = {9}
}

Non-stationarity and inter-subject variability of EEG characteristics in the context of BCI development

by Tanja Krumpe, K. Baumgärtner, Wolfgang Rosenstiel, and Martin Spüler
In Proceedings of the 7th Graz Brain-Computer Interface Conference, pages 260-265, 2017. [BIB]

@inproceedings{TKWM092017,
  author = {Krumpe, Tanja and Baumgärtner, K. and Rosenstiel, Wolfgang and Spüler, Martin},
  title = {Non-stationarity and inter-subject variability of EEG characteristics in the context of BCI development},
  booktitle = {Proceedings of the 7th Graz Brain-Computer Interface Conference},
  year = {2017},
  month = {sep},
  pages = {260-265},
  month_numeric = {9}
}

Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment

by Carina Walter, Wolfgang Rosenstiel, Martin Bogdan, Peter Gerjets, and Martin Spüler
In Frontiers in Human Neuroscience 11: 286, 2017. [BIB]

@article{CWMPM052017,
  author = {Walter, Carina and Rosenstiel, Wolfgang and Bogdan, Martin and Gerjets, Peter and Spüler, Martin},
  title = {Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment},
  journal = {Frontiers in Human Neuroscience},
  year = {2017},
  month = {may},
  volume = {11},
  pages = {286},
  month_numeric = {5}
}

Automated Therapeutic Anticoagulation: A Closed-Loop Approach Using a Modified Measurement Device

by Jörg Peter, Wilfried Klingert, Martin Spüler, Alfred Königsrainer, Wolfgang Rosenstiel, and Martin Schenk
In Proceeding of the International Conference on Biomedical Engineering (BIOMED) . ACTA Press, 2017. [BIB]

@inproceedings{JWMAWM022017,
  author = {Peter, Jörg and Klingert, Wilfried and Spüler, Martin and Königsrainer, Alfred and Rosenstiel, Wolfgang and Schenk, Martin},
  title = {Automated Therapeutic Anticoagulation: A Closed-Loop Approach Using a Modified Measurement Device},
  booktitle = {Proceeding of the International Conference on Biomedical Engineering (BIOMED) },
  publisher = {ACTA Press},
  year = {2017},
  month = {feb},
  organization = {IASTED},
  month_numeric = {2}
}

Brain-computer interfaces for educational applications

by Martin Spüler, Tanja Krumpe, Carina Walter, Christian Scharinger, Wolfgang Rosenstiel, and Peter Gerjets
In Informational Environments : Effects of Use, Effective Designs, pages 177-201, 2017. [BIB]

@article{MTCCWP2017,
  author = {Spüler, Martin and Krumpe, Tanja and Walter, Carina and Scharinger, Christian and Rosenstiel, Wolfgang and Gerjets, Peter},
  title = {Brain-computer interfaces for educational applications},
  journal = {Informational Environments : Effects of Use, Effective Designs},
  year = {2017},
  pages = {177-201}
}

2016

Hybrid neuroprosthesis for the upper limb: combining brain-controlled neuromuscular stimulation with a multi-joint arm exoskeleton

by F. Grimm, Armin Walter, Martin Spüler, G. Naros, Wolfgang Rosenstiel, and A. Gharbaghi
In Frontiers in Neuroscience 10(367), 2016. [BIB]

@article{FAMGWA072016,
  author = {Grimm, F. and Walter, Armin and Spüler, Martin and Naros, G. and Rosenstiel, Wolfgang and Gharbaghi, A.},
  title = {Hybrid neuroprosthesis for the upper limb: combining brain-controlled neuromuscular stimulation with a multi-joint arm exoskeleton},
  journal = {Frontiers in Neuroscience},
  year = {2016},
  month = {jul},
  volume = {10},
  number = {367},
  month_numeric = {7}
}

EEG-based prediction of cognitive workload induced by arithmetic: a step towards online adaptation in numerical learning

by Martin Spüler, Carina Walter, Wolfgang Rosenstiel, Peter Gerjets, K. Moeller, and E. Klein
In ZDM Mathematics Education 48(3): 267-278, 2016. [BIB]

@article{MCWPKE062016,
  author = {Spüler, Martin and Walter, Carina and Rosenstiel, Wolfgang and Gerjets, Peter and Moeller, K. and Klein, E.},
  title = {EEG-based prediction of cognitive workload induced by arithmetic: a step towards online adaptation in numerical learning},
  journal = {ZDM Mathematics Education},
  year = {2016},
  month = {jun},
  volume = {48},
  number = {3},
  pages = {267-278},
  month_numeric = {6}
}

Disentangeling working memory load - finding inhibition and updating components in EEG data

by Tanja Krumpe, Christian Scharinger, Peter Gerjets, Wolfgang Rosenstiel, and Martin Spüler
In Proceedings of the 6th International Brain-Computer Interface Meeting, pages 174, 2016. [BIB]

@inproceedings{TCPWM062016,
  author = {Krumpe, Tanja and Scharinger, Christian and Gerjets, Peter and Rosenstiel, Wolfgang and Spüler, Martin},
  title = {Disentangeling working memory load - finding inhibition and updating components in EEG data},
  booktitle = {Proceedings of the 6th International Brain-Computer Interface Meeting},
  year = {2016},
  month = {jun},
  pages = {174},
  month_numeric = {6}
}

Asynchronous P300 classification in a reactive brain-computer interface during an outlier detection task

by Tanja Krumpe, Carina Walter, Wolfgang Rosenstiel, and Martin Spüler
In Journal of Neural Engineering 13(4): 046015, 2016. [BIB]

@article{TCWM062016,
  author = {Krumpe, Tanja and Walter, Carina and Rosenstiel, Wolfgang and Spüler, Martin},
  title = {Asynchronous P300 classification in a reactive brain-computer interface during an outlier detection task},
  journal = {Journal of Neural Engineering},
  year = {2016},
  month = {jun},
  volume = {13},
  number = {4},
  pages = {046015},
  month_numeric = {6}
}

EEG Responses to Auditory Stimuli for Automatic Affect Recognition

by Dirk Hettich, E. Bolinger, T. Matuz, Niels Birbaumer, Wolfgang Rosenstiel, and Martin Spüler
In Frontiers in Neuroscience 10(244), 2016. [BIB]

@article{DETNWM052016,
  author = {Hettich, Dirk and Bolinger, E. and Matuz, T. and Birbaumer, Niels and Rosenstiel, Wolfgang and Spüler, Martin},
  title = {EEG Responses to Auditory Stimuli for Automatic Affect Recognition },
  journal = {Frontiers in Neuroscience},
  year = {2016},
  month = {may},
  volume = {10},
  number = {244},
  month_numeric = {5}
}

Comparing Methods for Decoding Movement Trajectory from ECoG in Chronic Stroke Patients

by Martin Spüler, F. Grimm, A. Gharabaghi, Martin Bogdan, and Wolfgang Rosenstiel
In Advances in Neurotechnology, Electronics and Informatics 12: 125-139, 2016. [BIB]

@article{MFAMW2016,
  author = {Spüler, Martin and Grimm, F. and Gharabaghi, A. and Bogdan, Martin and Rosenstiel, Wolfgang},
  title = {Comparing Methods for Decoding Movement Trajectory from ECoG in Chronic Stroke Patients},
  journal = {Advances in Neurotechnology, Electronics and Informatics},
  year = {2016},
  volume = {12},
  pages = {125-139}
}

2015

Comparing Metrics to Evaluate Performance of Regression Methods for Decoding of Neural Signals

by Martin Spüler, A. Sarasola-Sanz, Niels Birbaumer, Wolfgang Rosenstiel, and A. Ramos-Murguialday
In Proceedings of 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’15), pages 1083-1086, 2015. [BIB]

@inproceedings{MANWA082015,
  author = {Spüler, Martin and Sarasola-Sanz, A. and Birbaumer, Niels and Rosenstiel, Wolfgang and Ramos-Murguialday, A.},
  title = {Comparing Metrics to Evaluate Performance of Regression Methods for Decoding of Neural Signals},
  booktitle = {Proceedings of 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’15)},
  year = {2015},
  month = {aug},
  pages = {1083-1086},
  month_numeric = {8}
}

A Spiking Neuronal Model Learning a Motor Control Task by Reinforcement Learning and Structural Synaptic Plasticity

by Martin Spüler, Sebastian Nagel, and Wolfgang Rosenstiel
In Proceedings of the 2015 Internation Joint Conference on Neuronal Networks, pages 1652-1659, 2015. [BIB] [DOI] [ABSTRACT]

Abstract: In this paper, we present a spiking neuronal model that learns to perform a motor control task. Since the long-term goal of this project is the application of such a neuronal model to study the mutual adaptation between a Brain-Computer Interface (BCI) and its user, neurobiological plausibility of the model is a key aspect. Therefore, the model was trained using reinforcement learning similar to that of the dopamine system, in which a global reward and punishment signal controlled spike-timing dependent plasticity (STDP). Based on this method, the majority of the randomly generated models were able to learn the motor control task. Although the models were only trained on two targets, they were able to reach arbitrary targets after learning. By introducing structural synaptic plasticity (SSP), which dynamically restructures the connections between neurons, the number of models that successfully learned the task could be significantly improved.

@inproceedings{MSW072015,
  author = {Spüler, Martin and Nagel, Sebastian and Rosenstiel, Wolfgang},
  title = {A Spiking Neuronal Model Learning a Motor Control Task by Reinforcement Learning and Structural Synaptic Plasticity},
  booktitle = {Proceedings of the 2015 Internation Joint Conference on Neuronal Networks},
  year = {2015},
  month = {jul},
  pages = {1652-1659},
  doi = {10.1109/IJCNN.2015.7280521},
  month_numeric = {7}
}

Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity

by Martin Spüler and Christian Niethammer
In Frontiers in Human Neuroscience 9(): 155, 2015. [BIB] [DOI]

@article{MC032015,
  author = {Spüler, Martin and Niethammer, Christian},
  title = {Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity},
  journal = {Frontiers in Human Neuroscience},
  year = {2015},
  month = {mar},
  volume = {9},
  number = {},
  pages = {155},
  doi = {10.3389/fnhum.2015.00155},
  month_numeric = {3}
}

2014

Learned self-regulation of the lesioned brain with epidural electrocorticography

by A. Gharabaghi, G. Naros, F. Khademi, J. Jesser, Martin Spüler, Armin Walter, Martin Bogdan, Wolfgang Rosenstiel, and Niels Birbaumer
In Frontiers in Behavioral Neuroscience 8: 429, 2014. [BIB]

@article{AGFJMAMWN122014,
  author = {Gharabaghi, A. and Naros, G. and Khademi, F. and Jesser, J. and Spüler, Martin and Walter, Armin and Bogdan, Martin and Rosenstiel, Wolfgang and Birbaumer, Niels},
  title = {Learned self-regulation of the lesioned brain with epidural electrocorticography},
  journal = {Frontiers in Behavioral Neuroscience},
  year = {2014},
  month = {dec},
  volume = {8},
  pages = {429},
  month_numeric = {12}
}

Decoding of motor intentions from epidural ECoG recordings in severely paralyzed chronic stroke patients

by Martin Spüler, Armin Walter, A. Ramos-Murguialday, G. Naros, Niels Birbaumer, A. Gharabaghi, Wolfgang Rosenstiel, and Martin Bogdan
In Journal of Neural Engineering 11(6), 2014. [BIB]

@article{MAAGNAWM122014,
  author = {Spüler, Martin and Walter, Armin and Ramos-Murguialday, A. and Naros, G. and Birbaumer, Niels and Gharabaghi, A. and Rosenstiel, Wolfgang and Bogdan, Martin},
  title = {Decoding of motor intentions from epidural ECoG recordings in severely paralyzed chronic stroke patients },
  journal = {Journal of Neural Engineering},
  year = {2014},
  month = {dec},
  volume = {11},
  number = {6},
  month_numeric = {12}
}

Spatial Filtering Based on Canonical Correlation Analysis for Classification of Evoked or Event-Related Potentials in EEG Data

by Martin Spüler, Armin Walter, Wolfgang Rosenstiel, and Martin Bogdan
In IEEE Transactions on Neural Systems and Rehabilitation Engineering 22(6): 1097 - 1103, 2014. [BIB]

@article{MAWM112014,
  author = {Spüler, Martin and Walter, Armin and Rosenstiel, Wolfgang and Bogdan, Martin},
  title = {Spatial Filtering Based on Canonical Correlation Analysis for Classification of Evoked or Event-Related Potentials in EEG Data},
  journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
  year = {2014},
  month = {nov},
  volume = {22},
  number = {6},
  pages = {1097 - 1103},
  month_numeric = {11}
}

Predicting Wrist Movement Trajectory from Ipsilesional ECoG in Chronic Stroke Patients

by Martin Spüler, Wolfgang Rosenstiel, and Martin Bogdan
In Proceedings of 2nd International Congress on Neurotechnology, Electronics and Informatics (NEUROTECHNIX 2014) , pages 38-45, 2014. [BIB]

@inproceedings{MWM102014,
  author = {Spüler, Martin and Rosenstiel, Wolfgang and Bogdan, Martin},
  title = {Predicting Wrist Movement Trajectory from Ipsilesional ECoG in Chronic Stroke Patients},
  booktitle = {Proceedings of 2nd International Congress on Neurotechnology, Electronics and Informatics (NEUROTECHNIX 2014) },
  year = {2014},
  month = {oct},
  pages = {38-45},
  month_numeric = {10}
}

Towards Cross-Subject Workload Prediction.

by Carina Walter, P. Wolter, Wolfgang Rosenstiel, Martin Bogdan, and Martin Spüler
In Proceedings of the 6th International Brain-Computer Interface Conference , 2014. [BIB]

@inproceedings{CPWMM092014,
  author = {Walter, Carina and Wolter, P. and Rosenstiel, Wolfgang and Bogdan, Martin and Spüler, Martin},
  title = {Towards Cross-Subject Workload Prediction.},
  booktitle = {Proceedings of the 6th International Brain-Computer Interface Conference },
  year = {2014},
  month = {sep},
  address = {Graz, Austria},
  month_numeric = {9}
}

Classification of error-related potentials in EEG during continuous feedback

by Martin Spüler, Christian Niethammer, Wolfgang Rosenstiel, and Martin Bogdan
In Proceedings of the 6th International Brain-Computer Interface Conference, 2014. [BIB]

@inproceedings{MCWM092014,
  author = {Spüler, Martin and Niethammer, Christian and Rosenstiel, Wolfgang and Bogdan, Martin},
  title = {Classification of error-related potentials in EEG during continuous feedback},
  booktitle = {Proceedings of the 6th International Brain-Computer Interface Conference},
  year = {2014},
  month = {sep},
  month_numeric = {9}
}

Using Coherence for Robust Online Brain-Computer Interface (BCI) Control

by Martin Spüler, Wolfgang Rosenstiel, and Martin Bogdan
In Nonlinear Dynamics of Electronic Systems, pages 363-370, 2014. [BIB]

@inproceedings{MWM072014,
  author = {Spüler, Martin and Rosenstiel, Wolfgang and Bogdan, Martin},
  title = {Using Coherence for Robust Online Brain-Computer Interface (BCI) Control},
  booktitle = {Nonlinear Dynamics of Electronic Systems},
  year = {2014},
  month = {jul},
  pages = {363-370},
  month_numeric = {7}
}

Decoding stimulation intensity from evoked ECoG activity

by Armin Walter, G. Naros, Martin Spüler, A. Gharabaghi, Wolfgang Rosenstiel, and Martin Bogdan
In Neurocomputing 141(1): 46-53, 2014. [BIB]

@inproceedings{AGMAWM2014,
  author = {Walter, Armin and Naros, G. and Spüler, Martin and Gharabaghi, A. and Rosenstiel, Wolfgang and Bogdan, Martin},
  title = {Decoding stimulation intensity from evoked ECoG activity},
  booktitle = {Neurocomputing},
  year = {2014},
  volume = {141},
  number = {1},
  pages = {46-53}
}

Coupling brain-machine interfaces with cortical stimulation for brain-state dependent stimulation: enhancing motor cortex excitability for neurorehabilitation

by A. Gharabaghi, D. Kraus, M. Leao, Martin Spüler, Armin Walter, Martin Bogdan, Wolfgang Rosenstiel, G. Naros, and U. Ziemann
In Frontiers in Human Neuroscience 8(122), 2014. [BIB]

@article{ADMMAMWGU2014,
  author = {Gharabaghi, A. and Kraus, D. and Leao, M. and Spüler, Martin and Walter, Armin and Bogdan, Martin and Rosenstiel, Wolfgang and Naros, G. and Ziemann, U.},
  title = {Coupling brain-machine interfaces with cortical stimulation for brain-state dependent stimulation: enhancing motor cortex excitability for neurorehabilitation},
  journal = {Frontiers in Human Neuroscience},
  year = {2014},
  volume = {8},
  number = {122}
}

2013

Unsupervised Online Calibration of a c-VEP Brain-Computer Interface (BCI)

by Martin Spüler, Wolfgang Rosenstiel, and Martin Bogdan
In Artificial Neural Networks and Machine Learning-ICANN 2013, Lecture Notes in Computer Science Vol. 8131, pages 224-231. Springer, 2013. [BIB]

@inproceedings{MWM092013,
  author = {Spüler, Martin and Rosenstiel, Wolfgang and Bogdan, Martin},
  title = {Unsupervised Online Calibration of a c-VEP Brain-Computer Interface (BCI)},
  booktitle = {Artificial Neural Networks and Machine Learning-ICANN 2013, Lecture Notes in Computer Science Vol. 8131},
  publisher = {Springer},
  year = {2013},
  month = {sep},
  pages = {224-231},
  month_numeric = {9}
}

Dynamics of a Stimulation-evoked ECoG Potential During Stroke Rehabilitation - A Case Study

by Armin Walter, G. Naros, Martin Spüler, Wolfgang Rosenstiel, A. Gharabaghi, and Martin Bogdan
In NEUROTECHNIX 2013 - International Congress on Neurotechnology, Electronics and Informatics, pages 241-243, 2013. [BIB]

@inproceedings{A092013,
  author = {Walter, Armin and Naros, G. and Spüler, Martin and Rosenstiel, Wolfgang and Gharabaghi, A. and Bogdan, Martin},
  title = {Dynamics of a Stimulation-evoked ECoG Potential During Stroke Rehabilitation - A Case Study},
  booktitle = {NEUROTECHNIX 2013 - International Congress on Neurotechnology, Electronics and Informatics},
  year = {2013},
  month = {sep},
  pages = {241-243},
  month_numeric = {9}
}

Unsupervised BCI calibration as possibility for communication in CLIS patients?

by Martin Spüler, Wolfgang Rosenstiel, and Martin Bogdan
In Proceedings of the Fifth International Brain-Computer Interface Meeting 2013, DOI:10.3217/978-3-85125-260-6-122, 2013. [BIB]

@inproceedings{MWM062013,
  author = {Spüler, Martin and Rosenstiel, Wolfgang and Bogdan, Martin},
  title = {Unsupervised BCI calibration as possibility for communication in CLIS patients?},
  booktitle = {Proceedings of the Fifth International Brain-Computer Interface Meeting 2013, DOI:10.3217/978-3-85125-260-6-122},
  year = {2013},
  month = {jun},
  month_numeric = {6}
}

Decoding stimulation intensity from evoked ECoG activity using support vector regression

by Armin Walter, G. Naros, Martin Spüler, A. Gharabaghi, Wolfgang Rosenstiel, and Martin Bogdan
In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning , 2013. [BIB]

@inproceedings{AGMAWM2013,
  author = {Walter, Armin and Naros, G. and Spüler, Martin and Gharabaghi, A. and Rosenstiel, Wolfgang and Bogdan, Martin},
  title = {Decoding stimulation intensity from evoked ECoG activity using support vector regression},
  booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning },
  year = {2013}
}

2012

Online Adaptation of a c-VEP Brain-Computer Interface(BCI) Based on Error-Related Potentials and Unsupervised Learning

by Martin Spüler, Wolfgang Rosenstiel, and Martin Bogdan
In PLoS ONE 7(12): e51077, 2012. [BIB]

@article{MWM122012,
  author = {Spüler, Martin and Rosenstiel, Wolfgang and Bogdan, Martin},
  title = {Online Adaptation of a c-VEP Brain-Computer Interface(BCI) Based on Error-Related Potentials and Unsupervised Learning},
  journal = {PLoS ONE},
  year = {2012},
  month = {dec},
  volume = {7},
  number = {12},
  pages = {e51077},
  month_numeric = {12}
}

Coupling BCI and cortical stimulation for brain-state-dependent stimulation: Methods for spectral estimation in the presence of stimulation after-effects

by Armin Walter, A. Ramos Murguialday, Martin Spüler, G. Naros, M. T. Leao, A. Gharabaghi, Wolfgang Rosenstiel, Niels Birbaumer, and Martin Bogdan
In Frontiers in Neural Circuits, 2012. [BIB]

@inproceedings{AAMGMTAWNM102012,
  author = {Walter, Armin and Murguialday, A. Ramos and Spüler, Martin and Naros, G. and Leao, M. T. and Gharabaghi, A. and Rosenstiel, Wolfgang and Birbaumer, Niels and Bogdan, Martin},
  title = {Coupling BCI and cortical stimulation for brain-state-dependent stimulation: Methods for spectral estimation in the presence of stimulation after-effects},
  booktitle = {Frontiers in Neural Circuits},
  year = {2012},
  month = {oct},
  address = {doi: 10.3389/fncir.2012.00087},
  month_numeric = {10}
}

Adaptive SVM-based classification increases performance of a MEG-based Brain-Computer Interface (BCI)

by Martin Spüler, Wolfgang Rosenstiel, and Martin Bogdan
In ICANN 2012, Part I, LNCS 7552, pages 669-676, 2012. [BIB]

@inproceedings{MWM092012,
  author = {Spüler, Martin and Rosenstiel, Wolfgang and Bogdan, Martin},
  title = {Adaptive SVM-based classification increases performance of a MEG-based Brain-Computer Interface (BCI)},
  booktitle = {ICANN 2012, Part I, LNCS 7552},
  year = {2012},
  month = {sep},
  pages = {669-676},
  month_numeric = {9}
}

Online use of error-related potentials in healthy users and people with severe motor impairment increases performance of a P300-BCI

by Martin Spüler, Michael Bensch, S. Kleih, Wolfgang Rosenstiel, Martin Bogdan, and A. Kübler
In Clinical Neurophysiology 123(7): 1328-1337, 2012. [BIB]

@article{MMSWMA072012,
  author = {Spüler, Martin and Bensch, Michael and Kleih, S. and Rosenstiel, Wolfgang and Bogdan, Martin and Kübler, A.},
  title = {Online use of error-related potentials in healthy users and people with severe motor impairment increases performance of a P300-BCI},
  journal = {Clinical Neurophysiology},
  year = {2012},
  month = {jul},
  volume = {123},
  number = {7},
  pages = {1328-1337},
  month_numeric = {7}
}

Co-adaptivity in Unsupervised Adaptive Brain-Computer Interfacing: a Simulation Approach

by Martin Spüler, Wolfgang Rosenstiel, and Martin Bogdan
In The Fourth International Conference on Advanced Cognitive Technologies and Applications, pages 115-121, 2012. [BIB]

@inproceedings{MWM072012,
  author = {Spüler, Martin and Rosenstiel, Wolfgang and Bogdan, Martin},
  title = {Co-adaptivity in Unsupervised Adaptive Brain-Computer Interfacing: a Simulation Approach},
  booktitle = {The Fourth International Conference on Advanced Cognitive Technologies and Applications},
  year = {2012},
  month = {jul},
  pages = {115-121},
  month_numeric = {7}
}

Principal component based covariate shift adaption to reduce non-stationarity in a MEG-based brain-computer interface

by Martin Spüler, Wolfgang Rosenstiel, and Martin Bogdan
In EURASIP Journal on Advances in Signal Processing 2012:129, 2012. [BIB]

@article{MWM72012,
  author = {Spüler, Martin and Rosenstiel, Wolfgang and Bogdan, Martin},
  title = {Principal component based covariate shift adaption to reduce non-stationarity in a MEG-based brain-computer interface },
  journal = {EURASIP Journal on Advances in Signal Processing},
  year = {2012},
  month = {jul},
  number = {2012:129},
  month_numeric = {7}
}

One Class SVM and Canonical Correlation Analysis increase performance in a c-VEP based Brain-Computer Interface (BCI)

by Martin Spüler, Wolfgang Rosenstiel, and Martin Bogdan
In Proceedings of 20th European Symposium on Artificial Neural Networks (ESANN 2012), pages 103-108, 2012. [BIB]

@inproceedings{MWM042012,
  author = {Spüler, Martin and Rosenstiel, Wolfgang and Bogdan, Martin},
  title = {One Class SVM and Canonical Correlation Analysis increase performance in a c-VEP based Brain-Computer Interface (BCI)},
  booktitle = {Proceedings of 20th European Symposium on Artificial Neural Networks (ESANN 2012)},
  year = {2012},
  month = {apr},
  pages = {103-108},
  address = {Bruges, Belgium},
  month_numeric = {4}
}

2011

Prediction of Visual P300 BCI Aptitude Using Spectral Features

by S. Halder, Martin Spüler, E. Hammer, S. Kleih, Martin Bogdan, Wolfgang Rosenstiel, A. Kübler, and Niels Birbaumer
In Proceedings of the 5th International Brain-Computer Interface Conference, pages 144-147, 2011. [BIB]

@inproceedings{SMESMWAN092011,
  author = {Halder, S. and Spüler, Martin and Hammer, E. and Kleih, S. and Bogdan, Martin and Rosenstiel, Wolfgang and Kübler, A. and Birbaumer, Niels},
  title = {Prediction of Visual P300 BCI Aptitude Using Spectral Features},
  booktitle = {Proceedings of the 5th International Brain-Computer Interface Conference},
  year = {2011},
  month = {sep},
  pages = {144-147},
  address = {Graz},
  month_numeric = {9}
}

A Fast Feature Selection Method for High-Dimensional MEG BCI Data

by Martin Spüler, Wolfgang Rosenstiel, and Martin Bogdan
In Proceedings of the 5th International Brain-Computer Interface Conference, pages 24-27, 2011. [BIB]

@inproceedings{MWM092011,
  author = {Spüler, Martin and Rosenstiel, Wolfgang and Bogdan, Martin},
  title = {A Fast Feature Selection Method for High-Dimensional MEG BCI Data},
  booktitle = {Proceedings of the 5th International Brain-Computer Interface Conference},
  year = {2011},
  month = {sep},
  pages = {24-27},
  address = {Graz},
  month_numeric = {9}
}