Data from: Electroencephalography Responses to Simplified Visual Signals Reveal Explain Differences in Speech-in-Noise Comprehension

Contents and Folder Structure: EEG Experiment EEG_stimuli: these are the videos that were presented to participants in the EEG experiment, and the code that generates them from the original corpus (link) data_2020>split_trials: contains the raw EEG data starting 1.995s before each trial and ending 1.995s after each trial with naming convention subXx_VV_YY_N.fif where X or Xx is the subject number, YY is the modality condition (AV for audiovisual and V0 for video only), N is the trial number (between 0 and 4 inclusive), and VV is the video condition (1e for the envelope dot, 1m is the mismatched dot, 4v is the cartoon, bw is the edge detection and nh is the natural condition). unprocessed>raw: contains the unprocessed raw EEG data processed>Fs-200>BP-1-80-ASR-INTP-AVR: contains the pre-processed raw EEG data: the output of run_preprocessing.m processed>Fs-200>BP-1-80-ASR-INTP-AVR-ICr: contains the pre-processed raw EEG data after ICA cleaning: the output of run_reject_ICs.m stim>stim_dwnspl: contains the aligned 200Hz envelopes of the presented speech used as features for the time-lagged models EEG_analysis_code [note: please extract the contents of this folder to match paths] 2_ICA_filt: this folder contains the MATLAB code that performs the pre-processing of the EEG data, including filtering, downsampling, ICA cleaning etc. The main functions are: run_preprocessing.m: downsampling, filtering, ASR cleaning run_reject_ICs.m: ICLabel ICA cleaning 3_analysis: this is the Python code that performs the TRF and backward modelling on the EEG data. The main functions are: multisensory_bw.py: backwards model multisensory_fw.py: forwards model Behavioural Experiment behavioural 0_dataset: these are the videos that were presented to participants in the behavioural experiment, and the code that generates them from the original corpus (AV GRID corpus) 3_analysis: behavioural data analysis script main function: data_grid_v3.py behavioural_data>data_grid: behavioural result