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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: 0 LUB 1.00 (zmienne w czasie)
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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.

Zajęcia w cyklu "Semestr letni 2020/2021" (zakończony)

Okres: 2021-02-25 - 2021-06-15
Wybrany podział planu:


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Typ zajęć: Warsztat, 15 godzin więcej informacji
Koordynatorzy: Mattew Davidson, Michał Kuniecki
Prowadzący grup: Mattew Davidson, Michał Kuniecki
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Zaliczenie
Skrócony 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.

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.

Opisy przedmiotów w USOS i USOSweb są chronione prawem autorskim.
Właścicielem praw autorskich jest Uniwersytet Jagielloński w Krakowie.