jatshi/CAS_EEG

# CAS_EEG Dataset Documentation

1. Dataset Overview

CAS_EEG is an EEG-audio paired dataset for auditory target detection under continuous background noise. Participants listened to 120-second stimulus streams containing target events, non-target events, overlapping events, and pure background segments. The dataset covers the full pipeline from source audio and stimulus-generation scripts to participant-level raw/preprocessed EEG and final aligned EEG-audio samples.

1.1 Core Statistics (from unified_metadata_cleaned.csv)

• Number of participants: 16 (S01-S16)
• Total aligned samples: 49,313
• Target samples: 8,875
• Button-press samples: 8,758
• Overlap-event samples: 11,053
• Background samples (category_1=background): 16,056

Notes:

• These statistics are computed from 04_EEG_Audio_Paired_Dataset/eeg_sound_pairs_aligned/unified_metadata_cleaned.csv.
• Sample counts are not identical across participants; see per-subject files in split_metadata.

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2. Directory Structure

CAS_EEG/
|- 01_Resources/
|  |- paradigm.py / paradigm.psyexp
|  |- generate_stimulus.py
|  |- create_trigger_schedule.py
|  |- trigger_map.py / trigger_map.csv
|  \- preprocess_audio.py
|- 02_Source_Audio/
|  \- unified_dataset/
|     |- metadata.csv
|     |- target/...
|     |- nontarget/...
|     \- Wind_150s/
|- 03_Participants/
|  \- S01...S16/
|     |- audio_stimuli/
|     |- behavioral/
|     |- eeg_raw/
|     \- eeg_preprocessed/
\- 04_EEG_Audio_Paired_Dataset/
   |- eeg_sound_pairs_aligned/
   |  |- unified_metadata.csv
   |  |- unified_metadata_cleaned.csv
   |  |- split_metadata/
   |  \- S01...S16/
   \- sound_event_clips_aligned/
      \- S01...S16/

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3. Experiment and Stimulus Design

3.1 Per-trial Configuration

According to 01_Resources/generate_stimulus.py, each trial is configured as follows by default:

• Duration: 120 seconds
• Total events: 80
• Event duration: 1 second
• Minimum inter-event interval: 0.2 seconds
• Maximum inter-event interval (sampling constraint): 2.0 seconds
• Number of overlap pairs: 20 pairs (40 events involved in overlaps)
• SNR conditions: 0 dB (high) and -10 dB (low)
• Background: babble noise + wind noise (random crops and overlay)

Output files per trial:

• trial_X.wav: synthesized 120-second stimulus
• trial_X_log.csv: event-level category/onset/SNR/overlap relations
• trial_X_pure_noise_onsets.txt: start times of 1-second pure background segments
• trial_X_schedule.csv: final trigger schedule for experiment presentation

3.2 Target-category Notes

• Example target categories in scripts include gunshot, glassbreak, and babycry.
• Because some stages may include adjusted versions (for example adjusted log/schedule), use explicit metadata labels (especially is_target) as the source of truth during analysis.

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4. Trigger Encoding

Trigger semantics are defined in trigger_map.csv and trigger_map.py. Main rules:

• Single events: 1-115 (low/high SNR are encoded separately)
• Non-target overlaps NT+NT: 120-123
• Target+non-target overlaps T+NT: 180-183
• Special triggers:
  • 250: participant button response
  • 253: start of pure background segment
  • 254: trial start
  • 255: trial end

Recommendation:

• For training and evaluation, avoid relying only on numeric trigger codes. Prefer explicit metadata fields such as is_target, is_overlap, and pressed.

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5. Modalities and File Formats

5.1 Raw EEG

Path: 03_Participants/Sxx/eeg_raw/

• .cdt / .ceo / .dpa: acquisition-system raw files
• readme.txt: participant-level acquisition notes

5.2 Preprocessed EEG

Path: 03_Participants/Sxx/eeg_preprocessed/

• merged_120s_icaed_removed.set: preprocessed continuous EEG (EEGLAB)
• EEG_events.csv: event list (including fields such as type and latency)
• pressed/ and unpressed/:
  • condition-split .set files (target/non-target/SNR/overlap/background/button, etc.)

5.3 Participant Stimuli and Behavioral Logs

Path: 03_Participants/Sxx/audio_stimuli/ and 03_Participants/Sxx/behavioral/

• Each participant usually contains 40 trial audio files and logs
• behavioral stores PsychoPy exports (.csv/.log/.psydat)

5.4 Final Aligned Dataset

EEG Segments

Path: 04_EEG_Audio_Paired_Dataset/eeg_sound_pairs_aligned/Sxx/

Further organized by class and response:

• target/pressed, target/unpressed
• nontarget/pressed, nontarget/unpressed
• background/pressed, background/unpressed

File extension: .npy (EEG segment)

Corresponding Audio Segments

Path: 04_EEG_Audio_Paired_Dataset/sound_event_clips_aligned/Sxx/

Directory layout mirrors EEG aligned folders, with .wav files.

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6. Metadata Description

6.1 Unified Metadata Files

• 04_EEG_Audio_Paired_Dataset/eeg_sound_pairs_aligned/unified_metadata.csv
• 04_EEG_Audio_Paired_Dataset/eeg_sound_pairs_aligned/unified_metadata_cleaned.csv

Compared with unified_metadata.csv, the cleaned version adds a reject field to mark samples recommended for exclusion.

6.2 Field Dictionary

Field	Type	Description
subject_id	string	Participant ID (for example S01)
eeg_filename	string	EEG-segment filename (.npy)
eeg_path	string	Relative path of EEG segment
sound_clip_path	string	Relative path of corresponding audio clip (.wav)
pressed	bool	Whether a behavioral button response occurred in this event window
trial_id	int	Trial index
onset_s	float	Event onset within trial (seconds)
trigger	int	Trigger value
lag_seconds	float	Alignment lag (seconds)
is_target	bool	Whether this is a target event
is_overlap	bool	Whether this is an overlap event
sound_filename_1	string	Source audio filename of event 1
category_1	string	Category of event 1 (background indicates pure-background segment)
snr_db_1	float	SNR of event 1 (dB)
sound_filename_2	string/empty	Source audio filename of event 2 in overlaps
category_2	string/empty	Category of event 2 in overlaps
snr_db_2	float/empty	SNR of event 2 in overlaps
reject	bool	Available only in cleaned metadata; recommended exclusion flag

Notes:

• For non-overlap events, *_2 fields are typically empty.
• Background segments usually have category_1=background and trigger 253.

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7. Reproducible Build Workflow

Recommended minimum workflow (run scripts in the relevant folder):

1. Normalize audio format (44.1 kHz, 16-bit, mono)
2. Generate trial stimuli and trial logs
3. Generate trigger map and per-trial schedules
4. Run PsychoPy experiment (paradigm.py / paradigm.psyexp)
5. Preprocess EEG and epoch/segment data
6. Align EEG segments with audio segments and export unified metadata

Example commands (adjust paths to your environment):

python 01_Resources/preprocess_audio.py
python 01_Resources/generate_stimulus.py
python 01_Resources/trigger_map.py
python 01_Resources/create_trigger_schedule.py

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8. Quick Loading Example

import os
import numpy as np
import pandas as pd

root = r"d:\CAS_EEG"
meta_path = os.path.join(root, "04_EEG_Audio_Paired_Dataset", "eeg_sound_pairs_aligned", "unified_metadata_cleaned.csv")
meta = pd.read_csv(meta_path)

# Keep only valid samples if reject exists
if "reject" in meta.columns:
    meta = meta[meta["reject"].astype(str).str.upper() != "TRUE"]

# Load one sample
row = meta.iloc[0]
eeg = np.load(os.path.join(root, "04_EEG_Audio_Paired_Dataset", "eeg_sound_pairs_aligned", row["eeg_path"]))

print("subject:", row["subject_id"])
print("shape:", eeg.shape)
print("target:", row["is_target"], "pressed:", row["pressed"], "trigger:", row["trigger"])

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11. Citation and License

11.1 Citation

11.2 License and Ethics

• This dataset is released under the Apache License, Version 2.0. The full license text is included in the repository file LICENSE and can also be found at http://www.apache.org/licenses/LICENSE-2.0.

• The data collection procedure involved in creating this dataset was conducted in accordance with the Declaration of Helsinki. The study protocol was reviewed and approved by the Ethics Committee of Beijing Institute of Technology (Approval ID: BIT-EC-H-2024150). All participants provided informed consent prior to their involvement.