Advanced EEG analysis workshop
Informacje ogólne
Kod przedmiotu: | Cog-SDS/CogNeS/AnEEG |
Kod Erasmus / ISCED: | (brak danych) / (brak danych) |
Nazwa przedmiotu: | Advanced EEG analysis workshop |
Jednostka: | Szkoła Doktorska Nauk Społecznych |
Grupy: | |
Punkty ECTS i inne: |
(brak)
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Język prowadzenia: | angielski |
Pełny opis: |
- 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: |
Knowledge The student: - 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. Skills The student: - 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. Social competences The student: - 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. Scientific review - 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.