100 results for "human electroencephalography" · page 10 of 10 · ranked by relevance
Imported from OpenNeuro ds004022
…Open access dataset for hybrid brain-computer interfaces (BCIs) using electroencephalography (EEG…
…Signatures of electrical stimulation driven network interactions in the human limbic system…
This dataset comprises EEG recordings from a single healthy participant exposed to multisensory stimulation at gamma frequencies (40Hz). The experiment investigates brain entrainment responses to three stimulus modalities: auditory (5kHz carrier amplitude-modulated at 40Hz), visual (20Hz flickering white LED array), and combined audio-visual stimulation. Each stimulus epoch lasted 40 seconds and was recorded using 19 monopolar EEG channels at 500Hz sampling rate. The study aims to characterize synchronized brain oscillations induced by different sensory modalities, with potential implications for understanding gamma entrainment mechanisms relevant to neurodegenerative diseases such as Alzheimer's disease.
…iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial…
This dataset comprises resting-state electroencephalography (EEG) recordings from 111 healthy control subjects acquired using a BioSemi ActiveTwo system with 64 electrodes. Subjects were recorded during four minutes of continuous EEG with eyes closed, with some subjects undergoing repeat recordings at a later timepoint. The dataset includes both raw EEG data rereferenced to average reference and a derived cleaned dataset preprocessed with an automated pipeline, along with demographic and cognitive test data.
…EEG-BIDS, an extension to the brain imaging data structure for electroencephalography…
BigP3BCI Study J is a P300-based brain-computer interface dataset comprising EEG recordings from 20 healthy subjects performing a 9x8 character grid speller task. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset with ~267 subjects across 20 studies. The data were acquired at 256 Hz using 16-channel EEG with a g.USBamp amplifier and include target and non-target event classifications suitable for machine learning applications.
Imported from OpenNeuro ds004855