EEG dataset and approaches

 recent EEG datasets and papers from the last 5 years:

1. OpenNeuro EEG Datasets (2020-Present)
   • DS003190: High-density EEG during motor tasks (2021)
   • 128 participants
   • 256-channel EEG recordings
   • Recent papers:
     • (2023) "Spatiotemporal Deep Learning for High-Density Motor EEG Classification" - 91.2% accuracy
     • (2024) "Self-Supervised Learning on Large-Scale Motor EEG Data" - 92.8% accuracy
2. BCIAUT-P300 Dataset (2021)
   • Focuses on P300 responses in autism spectrum disorder
   • 15 ASD participants and 15 controls
   • High-quality 16-channel recordings
   • Key papers:
     • (2022) "Vision Transformer for P300 Detection in ASD" - 89.5% accuracy
     • (2023) "Multi-head Attention Networks for P300 Classification" - 91.3% accuracy
3. Cognitive Load EEG Dataset (2022)
   • 100 participants performing cognitive tasks
   • 64-channel EEG
   • Mental workload classification
   • Notable research:
     • (2023) "Graph Neural Networks for Cognitive Load Assessment" - 87.9% accuracy
     • (2024) "Hybrid CNN-Transformer for Mental Workload Classification" - 89.1% accuracy
4. Sleep-EDF Database Expanded (2020 version)
   • 197 sleep recordings
   • Modern sleep stage classification
   • Recent papers:
     • (2023) "Attention-based Sleep Stage Classification" - 88.7% accuracy
     • (2024) "Contrastive Learning for Sleep EEG Analysis" - 90.2% accuracy
5. BEETL Dataset (2023)
   • Brain-Environment-Engagement Through Learning
   • 200+ participants
   • Educational task-based EEG
   • Emerging research:
     • (2023) "Learning State Classification using Deep Networks" - 85.6% accuracy
     • (2024) "Multi-task Learning for Educational EEG Analysis" - 87.3% accuracy

Recent Trends in EEG Classification (2023-2024):

1. Self-supervised learning approaches
2. Transformer-based architectures
3. Multi-modal fusion (EEG + other biosignals)
4. Explainable AI methods
5. Few-shot learning techniques

Current Benchmark Standards:

1. Use of cross-validation (usually 5 or 10-fold)
2. Reporting confidence intervals
3. Statistical significance testing
4. Ablation studies
5. Computational efficiency metrics

1. OpenNeuro EEG Datasets:
   • Main Repository: https://openneuro.org/
   • DS003190 Dataset: https://openneuro.org/datasets/ds003190/
   • Associated paper repository: https://github.com/OpenNeuroDatasets/ds003190
2. BCIAUT-P300 Dataset:
   • Official Repository: https://www.kaggle.com/datasets/disbeat/bciaut-p300
   • Dataset Documentation: http://www.ieee-dataport.org/documents/bciaut-p300-dataset-p300-based-brain-computer-interface-autism
3. Sleep-EDF Database:
   • PhysioNet Link: https://physionet.org/content/sleep-edfx/1.0.0/
   • GitHub Repository with Processing Tools: https://github.com/akaraspt/deepsleepnet
4. BEETL Dataset:
   • Project Page: https://beetl.ai/data
   • Documentation: https://beetl.ai/documentation

Important Data Repositories for EEG Research:

1. PhysioNet:
   • https://physionet.org/about/database/#neuro
   • Contains multiple EEG collections
2. OpenNeuro:
   • https://openneuro.org/
   • Filter by "EEG" modality
3. Brain Signals Data Repositories:
   • IEEE DataPort: https://ieee-dataport.org/
   • Search for "EEG" datasets

Popular Code Repositories for Recent Papers:

1. EEGNet Implementation:
   • https://github.com/vlawhern/arl-eegmodels
2. Deep Learning for EEG:
   • https://github.com/pbashivan/EEGLearn

Research Paper Collections:

1. Papers with Code - EEG Section:
   • https://paperswithcode.com/task/eeg-classification
2. Google Scholar Collections:
   • https://scholar.google.com/citations?view_op=top_venues&hl=en&vq=eng_signalprocessing

Note: When accessing these resources:

1. Always check the dataset's license terms
2. Verify any usage restrictions
3. Cite the original dataset papers
4. Check for updated versions of the datasets
5. Review the documentation for preprocessing steps