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Michael Tangermann

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7 papers
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7

YNICL Journal 2020 Journal Article

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

  • Sebastián Castaño-Candamil
  • Tobias Piroth
  • Peter Reinacher
  • Bastian Sajonz
  • Volker A. Coenen
  • Michael Tangermann

The identification of oscillatory neural markers of Parkinson's disease (PD) can contribute not only to the understanding of functional mechanisms of the disorder, but may also serve in adaptive deep brain stimulation (DBS) systems. These systems seek online adaptation of stimulation parameters in closed-loop as a function of neural markers, aiming at improving treatment's efficacy and reducing side effects. Typically, the identification of PD neural markers is based on group-level studies. Due to the heterogeneity of symptoms across patients, however, such group-level neural markers, like the beta band power of the subthalamic nucleus, are not present in every patient or not informative about every patient's motor state. Instead, individual neural markers may be preferable for providing a personalized solution for the adaptation of stimulation parameters. Fortunately, data-driven bottom-up approaches based on machine learning may be utilized. These approaches have been developed and applied successfully in the field of brain-computer interfaces with the goal of providing individuals with means of communication and control. In our contribution, we present results obtained with a novel supervised data-driven identification of neural markers of hand motor performance based on a supervised machine learning model. Data of 16 experimental sessions obtained from seven PD patients undergoing DBS therapy show that the supervised patient-specific neural markers provide improved decoding accuracy of hand motor performance, compared to group-level neural markers reported in the literature. We observed that the individual markers are sensitive to DBS therapy and thus, may represent controllable variables in an adaptive DBS system.

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IROS Conference 2018 Conference Paper

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

  • Henrich Kolkhorst
  • Michael Tangermann
  • Wolfram Burgard

Interpreting the brain activity to identify user goals or to ground a robot's hypotheses about them is a promising direction for non-intrusive and intuitive communication. Such a capability can be of particular relevance in the context of human-robot cooperation scenarios. This paper proposes a novel approach to utilize the natural brain responses to highlighted objects in the scene for object selection. By this, it circumvents the need for additional interfaces or user training. Our approach uses methods from information geometry to classify the target/non-target response of these event-related potentials. Online experiments carried out with a real robot demonstrate an accurate detection of target objects solely based on the user's attention.

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YNIMG Journal 2014 Journal Article

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

  • Sven Dähne
  • Frank C. Meinecke
  • Stefan Haufe
  • Johannes Höhne
  • Michael Tangermann
  • Klaus-Robert Müller
  • Vadim V. Nikulin

Previously, modulations in power of neuronal oscillations have been functionally linked to sensory, motor and cognitive operations. Such links are commonly established by relating the power modulations to specific target variables such as reaction times or task ratings. Consequently, the resulting spatio-spectral representation is subjected to neurophysiological interpretation. As an alternative, independent component analysis (ICA) or alternative decomposition methods can be applied and the power of the components may be related to the target variable. In this paper we show that these standard approaches are suboptimal as the first does not take into account the superposition of many sources due to volume conduction, while the second is unable to exploit available information about the target variable. To improve upon these approaches we introduce a novel (supervised) source separation framework called Source Power Comodulation (SPoC). SPoC makes use of the target variable in the decomposition process in order to give preference to components whose power comodulates with the target variable. We present two algorithms that implement the SPoC approach. Using simulations with a realistic head model, we show that the SPoC algorithms are able extract neuronal components exhibiting high correlation of power with the target variable. In this task, the SPoC algorithms outperform other commonly used techniques that are based on the sensor data or ICA approaches. Furthermore, using real electroencephalography (EEG) recordings during an auditory steady state paradigm, we demonstrate the utility of the SPoC algorithms by extracting neuronal components exhibiting high correlation of power with the intensity of the auditory input. Taking into account the results of the simulations and real EEG recordings, we conclude that SPoC represents an adequate approach for the optimal extraction of neuronal components showing coupling of power with continuously changing behaviorally relevant parameters.

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AIIM Journal 2013 Journal Article

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

  • Elisa Mira Holz
  • Johannes Höhne
  • Pit Staiger-Sälzer
  • Michael Tangermann
  • Andrea Kübler

Objective Connect-Four, a new sensorimotor rhythm (SMR) based brain–computer interface (BCI) gaming application, was evaluated by four severely motor restricted end-users; two were in the locked-in state and had unreliable eye-movement. Methods Following the user-centred approach, usability of the BCI prototype was evaluated in terms of effectiveness (accuracy), efficiency (information transfer rate (ITR) and subjective workload) and users’ satisfaction. Results Online performance varied strongly across users and sessions (median accuracy (%) of end-users: A=. 65; B=. 60; C=. 47; D=. 77). Our results thus yielded low to medium effectiveness in three end-users and high effectiveness in one end-user. Consequently, ITR was low (0. 05–1. 44bits/min). Only two end-users were able to play the game in free-mode. Total workload was moderate but varied strongly across sessions. Main sources of workload were mental and temporal demand. Furthermore, frustration contributed to the subjective workload of two end-users. Nevertheless, most end-users accepted the BCI application well and rated satisfaction medium to high. Sources for dissatisfaction were (1) electrode gel and cap, (2) low effectiveness, (3) time-consuming adjustment and (4) not easy-to-use BCI equipment. All four end-users indicated ease of use as being one of the most important aspect of BCI. Conclusion Effectiveness and efficiency are lower as compared to applications using the event-related potential as input channel. Nevertheless, the SMR-BCI application was satisfactorily accepted by the end-users and two of four could imagine using the BCI application in their daily life. Thus, despite moderate effectiveness and efficiency BCIs might be an option when controlling an application for entertainment.

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AIIM Journal 2013 Journal Article

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

  • Martijn Schreuder
  • Angela Riccio
  • Monica Risetti
  • Sven Dähne
  • Andrew Ramsay
  • John Williamson
  • Donatella Mattia
  • Michael Tangermann

Objective The array of available brain–computer interface (BCI) paradigms has continued to grow, and so has the corresponding set of machine learning methods which are at the core of BCI systems. The latter have evolved to provide more robust data analysis solutions, and as a consequence the proportion of healthy BCI users who can use a BCI successfully is growing. With this development the chances have increased that the needs and abilities of specific patients, the end-users, can be covered by an existing BCI approach. However, most end-users who have experienced the use of a BCI system at all have encountered a single paradigm only. This paradigm is typically the one that is being tested in the study that the end-user happens to be enrolled in, along with other end-users. Though this corresponds to the preferred study arrangement for basic research, it does not ensure that the end-user experiences a working BCI. In this study, a different approach was taken; that of a user-centered design. It is the prevailing process in traditional assistive technology. Given an individual user with a particular clinical profile, several available BCI approaches are tested and – if necessary – adapted to him/her until a suitable BCI system is found. Methods Described is the case of a 48-year-old woman who suffered from an ischemic brain stem stroke, leading to a severe motor- and communication deficit. She was enrolled in studies with two different BCI systems before a suitable system was found. The first was an auditory event-related potential (ERP) paradigm and the second a visual ERP paradigm, both of which are established in literature. Results The auditory paradigm did not work successfully, despite favorable preconditions. The visual paradigm worked flawlessly, as found over several sessions. This discrepancy in performance can possibly be explained by the user's clinical deficit in several key neuropsychological indicators, such as attention and working memory. While the auditory paradigm relies on both categories, the visual paradigm could be used with lower cognitive workload. Besides attention and working memory, several other neurophysiological and -psychological indicators – and the role they play in the BCIs at hand – are discussed. Conclusion The user's performance on the first BCI paradigm would typically have excluded her from further ERP-based BCI studies. However, this study clearly shows that, with the numerous paradigms now at our disposal, the pursuit for a functioning BCI system should not be stopped after an initial failed attempt.

Details DOI
v2026.09.13