This hands-on workshop covers essential methods for analyzing EEG and MEG data, from analyzing raw recordings and event-related potentials, applying time-frequency analysis and source separation to advanced methods for connectivity analysis and decoding of selective attention. Learn to use the MNE toolbox alongside other scientific Python libraries (NumPy, Matplotlib, Scikit-Learn, mTRFpy), to transform raw recordings into insights about neural processing. Using simulations and real-world data, you will explore the theoretical foundations of signal processing, statistics and machine learning techniques as well as their practical application to EEG and MEG data.

Learning and participation

Topics

  • From Raw Recordings to Evoked Responses: Epoching, filtering, source separation, and permutation-based statistical testing.
  • Time-Frequency and Connectivity Analysis: Decomposition with Morlet wavelets and phase- and coherence-based connectivity measures.
  • Encoding and Decoding Models: Regularized regression, predicting brain responses from speech features, and decoding selective attention.

Intended participants

  • Researchers and students from all universities are welcome.
  • Participants of all educational backgrounds are welcome.

Useful resources for preparation

Certification

Students who attend at least 75% of the course will receive a participation certificate by email at the end of the course.