Advanced EEG analysis workshop
|Kod przedmiotu:||Cog-SDS/CogNeS/AnEEG||Kod Erasmus / ISCED:||(brak danych) / (brak danych)|
|Nazwa przedmiotu:||Advanced EEG analysis workshop|
|Jednostka:||Szkoła Doktorska Nauk Społecznych|
|Punkty ECTS i inne:||
zobacz reguły punktacji
- EEG signal pre-processing, the Matlab environment, EEGLab GUI.
- Time, frequency and time-frequency analysis methods.
- Variants of time-frequency analysis.
- Bistable perception (Binocular Rivalry, Perceptual filling-in).
- Frequency-tagging (SSVEPs).
- Consciousness and attention.
|Efekty uczenia się:||
- Knows and understands the principles of EEG signal generation, and best-practice for pre-processing data.
- Knows and can evaluate the applicability of different EEG analysis methods (time, frequency, and time-frequency domain processing).
- Knows and understands current state-of-the-art methods for analysing narrow-band time-frequency data (steady-state visually evoked potentials).
- Knows relevant critical principles to evaluate the use of time-frequency analyses, in the context of peer-review.
- is able to use EEGLAB to fulfil crucial data analysis objectives, and automate the processing pipeline using custom MATLAB scripts.
- is able to pre-process, plot, and analyse EEG data using different EEGlab functions, in the MATLAB environment.
- is able to administer separate time-frequency analysis methods (single taper, multi-taper, Morlet-wavelet filter, SNR conversion).
- is able to collaborate with peers, and contribute to constructive scientific peer review.
- is able to collaborate with peers on a scientific project related to brain signal processing
- is able to create novel solutions to problems encountered during data analysis
- is updated on the latest research in specialized areas of cognitive science
|Metody i kryteria oceniania:||
- Analysis of sample EEG data (single and group work)
- Short responses: students will provide inferences in response to provided, processed data.
- Scientific revision: students will review scientific preprints as if they were actual reviewers for a scientific journal.
Within class participation
- During the workshop students will work collaboratively to develop an EEG analysis pipeline. The scripts used for this analysis will be submitted after class
Independent EEG analysis
- Using the skills developed above, students will inspect processed data and provide a short response to demonstrate their ability to infer meaning from processed time-frequency data.
- Using the knowledge gained, students will provide a short scientific peer-review of an unpublished manuscript, focused on the EEG analysis methods.
Właścicielem praw autorskich jest Uniwersytet Jagielloński w Krakowie.