Journal: Data in Brief

Article Title: A high-density scalp EEG dataset acquired during brief naps after a visual working memory task

doi: 10.1016/j.dib.2018.04.073

Figure Lengend Snippet: Block diagram showing the algorithm used for automated spindle detection. Python source code distributed with the dataset allows for file input/output of the native BrainVision *.eeg files, filtering and artifact rejection, and optionally automated spindle detection, comparison with manually labelled spindles occurring in different non-REM sleep stages, and model validation. Root-mean-square (RMS) is computed and used to represent the feature maps for the automated spindle detection.

Article Snippet: EEG datasets are provided in their native raw format, which have the Brainvision.eeg extension.

Techniques: Blocking Assay, Comparison, Biomarker Discovery

Journal: Data in Brief

Article Title: A high-density scalp EEG dataset acquired during brief naps after a visual working memory task

doi: 10.1016/j.dib.2018.04.073

Figure Lengend Snippet:

Article Snippet: EEG datasets are provided in their native raw format, which have the Brainvision.eeg extension.

Techniques:

Journal: Scientific Reports

Article Title: Hybrid deep models for parallel feature extraction and enhanced emotion state classification

doi: 10.1038/s41598-024-75850-y

Figure Lengend Snippet: Data usage details.

Article Snippet: The EEG Brainwave dataset, sourced from Kaggle, is designed to classify emotions into three categories: positive, negative, and neutral.

Techniques:

Journal: Scientific Reports

Article Title: Hybrid deep models for parallel feature extraction and enhanced emotion state classification

doi: 10.1038/s41598-024-75850-y

Figure Lengend Snippet: Proposed model performance analysis for EEG brainwave dataset.

Article Snippet: The EEG Brainwave dataset, sourced from Kaggle, is designed to classify emotions into three categories: positive, negative, and neutral.

Techniques:

Journal: Scientific Reports

Article Title: Hybrid deep models for parallel feature extraction and enhanced emotion state classification

doi: 10.1038/s41598-024-75850-y

Figure Lengend Snippet: Performance comparative analysis with existing methods for EEG Brainwave dataset.

Article Snippet: The EEG Brainwave dataset, sourced from Kaggle, is designed to classify emotions into three categories: positive, negative, and neutral.

Techniques:

Journal: Physical and Engineering Sciences in Medicine

Article Title: Exploring deep residual network based features for automatic schizophrenia detection from EEG

doi: 10.1007/s13246-023-01225-8

Figure Lengend Snippet: Some recent research works to identify schizophrenia with EEG data and MRI data (2019–2022)

Article Snippet: This section provides a comparative report for our proposed method with the existing methods for the same Kaggle schizophrenia EEG dataset that was used in this study.

Techniques: Plasmid Preparation, Functional Assay

Journal: Physical and Engineering Sciences in Medicine

Article Title: Exploring deep residual network based features for automatic schizophrenia detection from EEG

doi: 10.1007/s13246-023-01225-8

Figure Lengend Snippet: The overall architecture of the proposed deep ResNet based framework for detection of schizophrenia from EEG signals. * SZ Schizophrenia, * C Normal Control, * DL Deep learning, * ML Machine learning

Article Snippet: This section provides a comparative report for our proposed method with the existing methods for the same Kaggle schizophrenia EEG dataset that was used in this study.

Techniques:

Journal: Data in Brief

Article Title: Motion artifact contaminated multichannel EEG dataset

doi: 10.1016/j.dib.2024.110994

Figure Lengend Snippet:

Article Snippet: Mendeley Data Motion Artifact Contaminated Multichannel EEG Dataset (Original data) .

Techniques:

Journal: Biomedicines

Article Title: Multi-Channel Vision Transformer for Epileptic Seizure Prediction

doi: 10.3390/biomedicines10071551

Figure Lengend Snippet: Schematic pipeline of the proposed EEG pre-processing strategy for seizure prediction: ( a ) EEG-to-scalogram conversion procedure: continuous wavelet transform (CWT) is adopted to generate the EEG power spectrum from the time-series EEG data; and 3D-to-2D projection (Proj) is used to produce the 2D time-frequency representations of EEG named “scalogram”. ( b ) EEG pre-processing approach: S 1 , S 2 , ⋯, S 60 correspond to the 1st, 2nd, and 60th 10-s segments of each 10-min EEG clip ( f S = 400 Hz); N is the total number of EEG channels ( N = 23 for scalp EEG; N = 16 for invasive EEG); d is the number of data-points in each EEG segment ( d = 10-s × f S = 4000); and h and w are the height and width of the EEG scalogram images ( h × w = 100 × 4000).

Article Snippet: Kaggle/American Epilepsy Society (AES) Invasive EEG Dataset [ ]—This EEG dataset was collected from two adult human and five canine subjects.

Techniques:

Journal: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

Article Title: Comparison of Foam-Based and Spring-Loaded Dry EEG Electrodes with Wet Electrodes in Resting and Moving Conditions *

doi: 10.1109/EMBC.2015.7320036

Figure Lengend Snippet: Electrode pop artifacts recorded by (left) the wet electrode and (right) the spring-loaded dry electrode at O2 during the cycling state.

Article Snippet: We analyzed these differences using a 256-channel EEG dataset acquired with Biosemi wet electrodes (Biosemi, Amsterdam, Netherlands).

Techniques:

Journal: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

Article Title: Comparison of Foam-Based and Spring-Loaded Dry EEG Electrodes with Wet Electrodes in Resting and Moving Conditions *

doi: 10.1109/EMBC.2015.7320036

Figure Lengend Snippet: Comparison of gait-related artifacts recorded by the wet electrodes and the dry electrodes: (left) foam-based electrode at Fp2 and (right) spring-loaded electrode at O1.

Article Snippet: We analyzed these differences using a 256-channel EEG dataset acquired with Biosemi wet electrodes (Biosemi, Amsterdam, Netherlands).

Techniques: Comparison

Journal: Frontiers in Neuroscience

Article Title: A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking

doi: 10.3389/fnins.2018.00217

Figure Lengend Snippet: Topographical map of multichannel MI-EEG recordings for subject ay with the SP feature. Six rows indicate six methods, i.e., gsBLDA, MRCS, NSGA-II and our three proposed methods. Five columns indicate the five cross-validation. We only retain the channels included in the optimal channel subset. Red and blue represent the top and bottom rankings.

Article Snippet: Dataset IVa from BCI Competition III is a public EEG dataset provided by the Berlin BCI group Fraunhofer FIRST (Intelligent Data Analysis Group) and Campus Benjamin Franklin of the Charité University (Neurophysics Group).

Techniques: Biomarker Discovery

Journal: Data in Brief

Article Title: EEG dataset for energy data visualizations

doi: 10.1016/j.dib.2023.109933

Figure Lengend Snippet:

Article Snippet: EEG Dataset Collected During Energy Data Visualization Stimuli Presentation (EDAVIS) (Original data) (Mendeley Data).

Techniques: Sampling, Software, Generated