Cognitive neuroscientific research methods

Here you will find information on the research fields of the section.

Methods and software for EEG analysis

Electroencephalography (EEG) is a non-invasive method for measuring brain activity, which records electrical voltages on the surface of the head. Due to its high temporal resolution, EEG can be used to investigate various cognitive processes. In our research group, we are interested in the neurophysiological correlates of solving arithmetic problems, comparing two numbers and processing number sequences, among other things. A large number of parameters can be derived from the EEG that can correlate with such processes. For example, oscillatory activity in a certain frequency band (the so-called event-related desynchronization and synchronization, ERS/ERD) provides information about the use of certain solution strategies in arithmetic tasks. The connectivity between cortical sources provides information about how neural networks are involved in specific processes. The widely used technique of event-related potentials (ERP) averages over many repetitions of a task, resulting in a waveform with positive and negative spikes. These can be linked to information processing in the brain.

Our department is committed to open research, i.e. the use of open source software and the provision of fully reproducible research results via open access. We contribute to various open source projects to promote the development and application of new and established methods, such as MNE-Python, the most popular Python package for analyzing MEG/EEG data, and MNELAB, a graphical user interface for MNE-Python. Another project supported by our working group is SigViewer, a widely used program for displaying and editing biosignals in various formats. We also support the development of the XDF format, which is well suited for storing multimodal data, with import functions for Python(pyxdf) and Julia(XDF.jl). We also maintain the Python package SleepECG for classifying sleep phases based on heart rate variability. We have also contributed to projects such as Scikit-learn, MNE-BIDS, pybv, pandas, SciPy, Matplotlib and BioSig.

  1. Brunner, C., & Hofer, F. (2023). SleepECG: a Python package for sleep staging based on heart rate. Journal of Open Source Software, 8(86), 5411, https://doi.org/10.21105/joss.05411
  2. Brunner, C. (2022). MNELAB: a graphical user interface for MNE-Python. Journal of Open Source Software , 7(78), 4650, https://doi.org/10.21105/joss.04650
  3. Appelhoff, S., Sanderson, M., Brooks, T., van Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A., & Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software, 4(44), 1896, https://doi.org/10.21105/joss.01896
  4. Lin, Y., Brunner, C., Sajda, P., & Faller, J. (2017). SigViewer: visualizing multimodal signals stored in XDF (Extensible Data Format) files. arXiv, https://doi.org/10.48550/arXiv.1708.06333
  5. Brunner, C., Billinger, M., Seeber, M., Mullen, T. R., & Makeig, S. (2016). Volume conduction influences scalp-based connectivity estimates.Frontiers in Computational Neuroscience, 10, 121, https://doi.org/10.3389/fncom.2016.00121