StanSStanman
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1 year ago Thank you @DCP-INS for submitting your project to BrainHack Marseille 2022! :tada::tada::tada: We are sorry but at the current state of submission your project can't be added on our website. In detail, we need you to:
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You can take some example from the current projects added on our website, or from those of past editions. Your project will be published as soon as we receive these updates. Thank you! :pray:
Ruggero for the BHM team.
DCP-INS
Title
Neural encoding of acoustic and semantic features during speech and music perception: Matlab to Python code translation
Leaders
Bruno Giordano (INT) : https://github.com/brungio/bhack_td https://twitter.com/brungio https://framateam.org/blgnatsou/messages/@bruno.giordano
Giorgio Marinato (INT) : https://github.com/neurogima https://twitter.com/neurogima mattermost: @neurogima
Collaborators
Benjamin Morillon (CR INS - https://github.com/DCP-INS) Nadège Marin (IE, INS) Arnaud Zalta (PhD student, INS)
Brainhack Global 2022 Event
Brainhack Marseille
Project Description
In everyday life, humans are particularly attuned to listening to two particular types of sound: speech and music. We apply a novel analysis method to shed light on how the brain is almost effortlessly able to use acoustic features to assign meaning to sounds. To do so, we use an original cross-validated Representational Similarity Analyses (RSA) approach implemented in Matlab to estimate the similarity between acoustic or semantic features of an auditory stream (speech, music) and neural activity (here intracranial EEG recordings decomposed into frequency bands).
Link to project repository/sources
https://github.com/brungio/bhack_td
Goals for Brainhack Global
The main goal of this project is to translate the Matlab code into Python:
Good first issues
Communication channels
https://mattermost.brainhack.org/brainhack/channels/brainhack_marseille_2022_speech_music_representation
Skills
python coding Representational Similarity Analyses (RSA) Cross-validation (train-test-validate) intracranial EEG signal processing sharing data analyses ideas
Onboarding documentation
What will participants learn?
to analyse intracranial EEG data to perform cross-validation procedures to estimate the neural encoding of different acoustic features
Data to use
We will provide a sample of a dataset of iEEG recordings.
Number of collaborators
from 3 to 10
Credit to collaborators
Acknowledgment in the code and in the Github repo.
Image
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Type
pipeline_development
Development status
1_basic structure
Topic
neural_decoding
Tools
MNE, RSAtoolbox (github.com/rsagroup/rsatoolbox), Scikit Learn
Programming language
Python, Matlab
Modalities
intracranial EEG
Git skills
Basic git workflow: fork, branches, commit and pull request