100 results for "ECG" · page 10 of 10 · ranked by relevance
…EOG (4 ch, horizontal, vertical), ecg, respiration Participants ------------ Number of subjects: 29…
This dataset comprises EEG recordings from 13 healthy subjects performing a P300-based brain-computer interface task using a 9x8 checkerboard stimulus paradigm. Participants completed a single session of visual perception tasks designed for speller applications, with 16-channel EEG data sampled at 256 Hz. This derivative dataset is part of the BigP3BCI study, the largest public P300 BCI dataset containing recordings from approximately 267 subjects across multiple studies.
A 4-target, 2-class code-VEP brain-computer interface dataset from 12 healthy participants comparing burst c-VEP and m-sequence stimulation paradigms at two amplitude depths (100% and 40%). The study evaluates classification performance and user experience using 32-channel EEG recorded at 500 Hz. This derivative dataset is derived from the original Castillos et al. (2023) study and optimizes stimulus design for reactive BCI applications while maintaining visual comfort. CNN-based decoding achieved up to 95.6% accuracy (best-case scenario with 52.8 s of calibration data).
This dataset comprises EEG recordings from 5 healthy subjects performing a P300-based brain-computer interface (BCI) speller task using a 9x8 adaptive/checkerboard stimulus paradigm across 2 sessions. The data were acquired at 256 Hz using 16 EEG channels and represent a derivative subset of the BigP3BCI study, the largest public P300 BCI dataset containing recordings from approximately 267 subjects across 20 studies. This dataset is suitable for P300 detection, feature extraction, and within-subject BCI performance evaluation.
This dataset comprises psychometric and electroencephalographic (EEG) data collected from Mexican children with reading or math learning difficulties, aimed at evaluating the efficacy of the assistive online learning platform Smartick in improving academic skills. Data were collected from 76 subjects across two study groups (reading and math difficulties), each divided into experimental and control subgroups, with EEG and psychometric assessments performed before and after a three-month intervention period. The EEG recordings include resting-state and task-related conditions (reading comprehension or arithmetic problem solving), accompanied by psychometric measures such as reading speed, comprehension, math skills, selective attention, and IQ.
This high-density EEG dataset comprises resting-state and task-based recordings from 23 healthy volunteers performing visual naming and spelling tasks. Collected between 2012-2013 at Rennes University Hospital using a 256-electrode EEG system, the dataset enables investigation of dynamic functional brain network reorganization during language production and cognitive processing. The study provides insights into spatiotemporal patterns of brain connectivity during object naming and orthographic tasks.
This dataset comprises high-density EEG recordings from 30 healthy, right-handed, native Chinese-speaking adults performing imagined and executed vocalization and writing tasks involving four Chinese strokes and four English letters. The factorial design crossed task mode, action, script and token identity, yielding 32 event-coded conditions across 15,360 task events. The dataset supports research into motor imagery, speech and handwriting-related neural communication signals.
EmoEEG-MC is a multi-context emotional EEG dataset comprising 64-channel EEG and peripheral physiological recordings (PPG, GSR) from 60 participants exposed to video-induced and imagery-induced emotional stimuli. Seven emotion categories (joy, inspiration, tenderness, fear, disgust, sadness, neutral) were evoked and validated through subjective reports, enabling investigation of cross-context emotion decoding. The dataset supports research on neural mechanisms of emotion and generalization of affective computing models across contexts.
2417 clinical EEG recordings, 19-ch 10-20 montage, normal/abnormal labels. EDF format. Zenodo DOI:10.5281/zenodo.10909103