EEG Data Analysis with MATLAB
Budget / Salary£20–250
TypeFreelance project
LocationRemote
Posted1 hour ago
Description
I am looking for someone experienced in EEG data analysis using MATLAB and FieldTrip to provide analysis support for a university research project investigating the effects of attentional load on multisensory integration (MSI).
I have already completed data collection and have the raw EEG data in BrainVision format (.eeg, .vhdr and .vmrk) for multiple participants.
The experiment includes auditory (A), visual (V) and audiovisual/bimodal (AV) stimuli. Participants also completed an N-back manipulation involving 0-back, 1-back and 2-back conditions, which is being used to manipulate attentional/cognitive load.
The overall aim is to investigate whether multisensory processing/integration changes depending on attentional load, using behavioural and EEG measures.
I am looking for someone who can support me with:
Preprocessing/cleaning the raw EEG data in MATLAB using FieldTrip
Filtering, re-referencing, identifying bad channels/trials and appropriate artefact removal (including ICA if appropriate)
Correctly identifying and using the experimental triggers/events
Epoching the EEG around the relevant auditory, visual and audiovisual stimuli
Separating trials according to experimental condition and N-back load
Producing participant-level and grand-average ERP data/plots
Helping quantify/test multisensory integration appropriately (e.g. audiovisual versus appropriate unimodal comparisons)
Appropriate statistical analysis of the EEG results
Extraction/analysis of behavioural measures, including reaction time and accuracy, where possible from the experimental event markers
Producing appropriate figures/tables for the analysis
Providing the MATLAB/FieldTrip scripts used for all analyses, with clear comments
Explaining the analysis decisions and outputs so that I can understand and report the methodology and results myself
Experience analysing ERP data, multisensory/audiovisual experiments, cognitive neuroscience experiments, or N-back/attention paradigms would be particularly useful.
I am looking for someone experienced in EEG data analysis using MATLAB and FieldTrip to provide analysis support for a university research project investigating the effects of attentional load on multisensory integration (MSI).
I have already completed data collection and have the raw EEG data in BrainVision format (.eeg, .vhdr and .vmrk) for multiple participants.
The experiment includes auditory (A), visual (V) and audiovisual/bimodal (AV) stimuli. Participants also completed an N-back manipulation involving 0-back, 1-back and 2-back conditions, which is being used to manipulate attentional/cognitive load.
The overall aim is to investigate whether multisensory processing/integration changes depending on attentional load, using behavioural and EEG measures.
I am looking for someone who can support me with:
Preprocessing/cleaning the raw EEG data in MATLAB using FieldTrip
Filtering, re-referencing, identifying bad channels/trials and appropriate artefact removal (including ICA if appropriate)
Correctly identifying and using the experimental triggers/events
Epoching the EEG around the relevant auditory, visual and audiovisual stimuli
Separating trials according to experimental condition and N-back load
Producing participant-level and grand-average ERP data/plots
Helping quantify/test multisensory integration appropriately (e.g. audiovisual versus appropriate unimodal comparisons)
Appropriate statistical analysis of the EEG results
Extraction/analysis of behavioural measures, including reaction time and accuracy, where possible from the experimental event markers
Producing appropriate figures/tables for the analysis
Providing the MATLAB/FieldTrip scripts used for all analyses, with clear comments
Explaining the analysis decisions and outputs so that I can understand and report the methodology and results myself
Experience analysing ERP data, multisensory/audiovisual experiments, cognitive neuroscience experiments, or N-back/attention paradigms would be particularly useful.
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