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: PLoS ONE

Article Title: Automated seizure activity tracking and onset zone localization from scalp EEG using deep neural networks

doi: 10.1371/journal.pone.0264537

Figure Lengend Snippet: Patient demographics and clinical attributes for our JHH evaluation dataset (N = 34) and UWM generalization dataset (N = 15).

Article Snippet: JHH Dataset: Our primary EEG dataset consists of 201 seizure recordings obtained from 34 focal epilepsy patients undergoing presurgical evaluation in the Johns Hopkins Hospital between 2016–2019.

Techniques:

Journal: PLoS ONE

Article Title: Automated seizure activity tracking and onset zone localization from scalp EEG using deep neural networks

doi: 10.1371/journal.pone.0264537

Figure Lengend Snippet: Performance on the JHH dataset. Seizure detection results on the JHH dataset. Window-level metrics are aggregated across one-second segments of the EEG. Seizure level results are calculated over the duration of the seizure interval.

Article Snippet: JHH Dataset: Our primary EEG dataset consists of 201 seizure recordings obtained from 34 focal epilepsy patients undergoing presurgical evaluation in the Johns Hopkins Hospital between 2016–2019.

Techniques:

Journal: Frontiers in Neuroscience

Article Title: Assigning channel weights using an attention mechanism: an EEG interpolation algorithm

doi: 10.3389/fnins.2023.1251677

Figure Lengend Snippet: Brief description of the four datasets.

Article Snippet: A test–retest resting and cognitive state EEG dataset(TRCS) , Resting (eyes-open and eyes-closed) and subject-driven cognitive states (memory, music, subtraction) , 60 , Brain Products , 500 HZ.

Techniques: Sampling

Journal: PLoS ONE

Article Title: Automated seizure activity tracking and onset zone localization from scalp EEG using deep neural networks

doi: 10.1371/journal.pone.0264537

Figure Lengend Snippet: Performance on the JHH dataset. Seizure detection results on the JHH dataset. Window-level metrics are aggregated across one-second segments of the EEG. Seizure level results are calculated over the duration of the seizure interval.

Article Snippet: We evaluate SZTrack on a clinical EEG dataset of 201 seizure recordings from 34 epilepsy patients acquired at the Johns Hopkins Hospital.

Techniques:

Journal: PLoS ONE

Article Title: Automated seizure activity tracking and onset zone localization from scalp EEG using deep neural networks

doi: 10.1371/journal.pone.0264537

Figure Lengend Snippet: Channel-wise predictions for the fronto-temporal seizure shown on the top row of are superimposed on the EEG signal. In (a) SZTrack makes a confident prediction of seizure onset in the temporal channels which spreads to the parietal and frontal areas. In (b) No-BLSTM responds to isolated seizure activity at the onset but does not provide a temporally stable prediction.

Article Snippet: We evaluate SZTrack on a clinical EEG dataset of 201 seizure recordings from 34 epilepsy patients acquired at the Johns Hopkins Hospital.

Techniques: Isolation, Activity Assay