Publications

[9] invisible-maze-task-eeg

Gehrke, L. & Gramann, K. | Eur. J. Neurosci. 1–18 (2021). https://doi.org/10.1111/ejn.15152

We showcased the capabilities of Mobile Brain/Body Imaging (MoBI) using Virtual Reality (VR), demonstrating several analyses approaches based on general linear models (GLM) to reveal behavior‐dependent brain dynamics. Confirming spatial learning via drawn sketch maps we employed motion capture to image spatial exploration behavior describing a shift from initial exploration to subsequent exploitation of the mental representation.

📄 Paper


[8] audiomaze-eeg

Miyakoshi, M., Gehrke, L., Gramann, K., Makeig, S. & Iversen, J. | Eur J Neurosci. 2021; 00: 1– 25. https://doi.org/10.1111/ejn.15131

We developed the Audiomaze, a novel paradigm in which participants freely explore a room-sized virtual maze while EEG is recorded synchronized to motion capture. Participants (n=16) were blindfolded and explored different mazes, each in three successive trials, using their right hand as a probe to ‘feel’ for virtual maze walls. We found behavioral evidence of navigational learning in a sparse-AR environment, and a neural correlate of navigational learning was found near lingual gyrus.

📄 Paper


[7] landmark-based-navigation-eeg

Alexandre Delaux, Jean-Baptiste de Saint Aubert, Stephen Ramanoël, Marcia Bécu, Lukas Gehrke, Marius Klug, Ricardo Chavarriaga, José-Alain Sahel, Klaus Gramann, Angelo Arleo | Eur J Neurosci. 2021; 00: 1– 27. https://doi.org/10.1111/ejn.15190

We focused on landmark-based navigation in actively behaving young adults solving a virtual reality Y-maze task. Our results confirm that combining mobile high-density EEG and biometric measures can help unravel the brain network and neural modulations subtending ecological landmark-based navigation.

📄 Paper


[6] prediction-error-eeg

Lukas Gehrke, Sezen Akman, Pedro Lopes, Albert Chen, Avinash Kumar Singh, Hsiang-Ting Chen, Chin-Teng Lin and Klaus Gramann | In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ‘19). ACM, New York, NY, USA, Paper 427, 11 pages. DOI: https://doi.org/10.1145/3290605.3300657

We detected conflicts in visuo-haptic integration by analyzing event-related potentials (ERP) during interaction with virtual objects. In our EEG study, participants touched virtual objects and received either no haptic feedback, vibration, or vibration and EMS (Electrical muscle stimulation). We report a sensitiviy to unrealistic VR situations of an early negativity component at electrode FCz (prediction error), indicating we successfully detected haptic conflicts.

📄 Paper | 📽 Slides | 💾 Data/Code

Together with Sezen Akman who wrote her excellent Masterthesis within the project we won Best Project Award at “VDI Mensch und Technik 2019” endowed with 3000€.

In the Media

EMS

VDI


[5] source-power-comodulation-eeg

Lukas Gehrke*, Luke Guerdan* and Klaus Gramann | * contributed equally | *9th International IEEE/EMBS Conference on Neural Engineering (NER), San Francisco, CA, USA, 2019, pp. 344-347*

We proposed the use of a supervised spatial filtering method, Source Power Co-modulation (SPoC), for extracting source components that co-modulate with body motion. We illustrate the approach to investigate the link between hand and head movement kinematics and power dynamics of EEG sources while participants explore an invisible maze in virtual reality.

📄 Paper


[4] art-brain-computer-interface-eeg

Stephanie Scott and Lukas Gehrke | Springer Series on Bio- and Neurosystems, Vol. 10, Jose L. Contreras-Vidal et al. (Eds): Mobile Brain-Body Imaging and the Neuroscience of Art, Innovation and Creativity, 978-3-030-24325-8

Using EEG power we gave a live visual feedback of white lines on a black background, borrowing from Joy Division’s famous album cover. This closed-loop neurofeedback stimulates creativity by making aware one’s own brain activity.

📄 Paper


[3]

Evelyn Jungnickel, Lukas Gehrke, Marius Klug and Klaus Gramann | Academic Press, Neuroergonomics, 59—63, 2019

Mobile brain/body imaging (MoBI) is a method to record and analyze brain dynamics and motor behavior in naturalistic conditions. In this chapter we give an overview of its suitability to investigate a wide range of scientific problems., including analyses of human brain dynamics with the aid of information derived from movement and studies with an interest in motor behavior using brain imaging as an additional source of information.

📕 Chapter


[2] invisible-maze-task-behavior

Lukas Gehrke, John R. Iversen, Scott Makeig and Klaus Gramann | In: Creem-Regehr S., Schöning J., Klippel A. (eds) Spatial Cognition XI. Spatial Cognition 2018. Lecture Notes in Computer Science, vol 11034. Springer, Cham

The neuroscientific study of human navigation has been limited by requiring participants to remain stationary during data recordings. With the Invisible Maze Task (IMT) we provide a novel VR paradigm to investigate freely moving, naturally interacting, navigators.

📄 Paper | 💾 Code

In the Media

German newspaper Der Spiegel featured the invisible maze task in Seidler’s Selbstversuch series.


[1] spot-rotation-eeg

Klaus Gramann, Friederike U. Hohlefeld, Lukas Gehrke and Marius Klug | bioRxiv, https://doi.org/10.1101/417972

We contrasted physically rotating participants with a traditional joystick setup with rotations based on visual flow only. We show that varying rotation velocities were accompanied by pronounced beta synchronization during physical rotation but not joystick control.

📄 Preprint