Predicting Motor Intentions with Closed-Loop Brain-Computer Interfaces

  • Matthias Schultze-Kraft
  • Mario Neumann
  • Martin Lundfall
  • Patrick Wagner
  • Daniel Birman
  • John-Dylan Haynes
  • Benjamin Blankertz
Chapter
Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Abstract

We present two studies in which brain-computer interfaces (BCIs) used two related EEG signals, the readiness potential (RP) and the lateralized readiness potential (LRP), in order to predict and feed back motor intentions in real-time. In each of the studies, the experimental task was designed as a game that the subjects played against the BCI. In one of the experiments, subjects played a “duel” game against the BCI. They were challenged to perform spontaneous button presses but to withhold any movement when interrupted by a stop signal. This stop signal was controlled by the BCI that had been trained to predict movements by detecting the occurrence of RPs in the ongoing EEG. In the other experiment, participants played a “matching pennies” game. They won a point if they raised a different hand than the opponent at the end of a countdown and lost a point otherwise. The opponent was played by the BCI, who had been trained to predict from the LRP in the ongoing EEG which hand the subject would move at the end of the countdown. Hence, in both experiments a key feature of the BCI was its closed-loop nature, that is the ability to predict the motor intention in real-time and provide an immediate feedback of the prediction to the subject. In both experiments, prediction accuracies of the BCI were substantially higher compared to random predictions, thereby demonstrating the success of this approach. This allows researchers to use BCIs as research tools to address questions from cognitive neuroscience and provide new insights into the coupling of motor preparatory signals and the corresponding actions.

Notes

Acknowledgements

This work was supported by the Bernstein Focus: Neurotechnology from the German Federal Ministry of Education and Research (BMBF grant 01GQ0850), by the Bernstein Computational Neuroscience Program (BMBF grant 01GQ1001C), the Research Training Group “Sensory Computation in Neural Systems” (GRK 1589/1-2), the Collaborative Research Center “Volition and Cognitive Control: Mechanisms, Modulations, Dysfunctions” (SFB 940/1) and the German Research Foundation (DFG grants EXC 257 and KFO 247).

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Copyright information

© The Author(s) 2017

Authors and Affiliations

  • Matthias Schultze-Kraft
    • 1
    • 2
    • 3
  • Mario Neumann
    • 1
  • Martin Lundfall
    • 1
  • Patrick Wagner
    • 1
  • Daniel Birman
    • 3
  • John-Dylan Haynes
    • 2
    • 3
    • 4
  • Benjamin Blankertz
    • 1
    • 2
    • 3
  1. 1.Neurotechnology GroupTechnische Universität BerlinBerlinGermany
  2. 2.Bernstein Focus: NeurotechnologyBerlinGermany
  3. 3.Bernstein Center for Computational Neuroscience BerlinBerlinGermany
  4. 4.Berlin Center for Advanced NeuroimagingBerlinGermany

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