100 results for "scalp electroencephalography" · page 10 of 10 · ranked by relevance
…EEG-BIDS, an extension to the brain imaging data structure for electroencephalography…
…EEG-BIDS, an extension to the brain imaging data structure for electroencephalography…
Imported from OpenNeuro ds003343
…nemar.on003774) The dataset contains Electroencephalography (EEG) responses from 20 Indian participants…
This dataset comprises mobile EEG recordings from 44 healthy adults performing an auditory oddball task across three environmental conditions: laboratory, outdoor field, and campus navigation. Participants attended to or ignored complex tone stimuli (piano and horn) while 32-channel EEG was recorded using a Brain Vision LiveAmp amplifier. The study investigates how cognitive task demands, motor demands, and environmental complexity modulate attentional processing and event-related potentials during naturalistic behavior.
…contains high-density, dual-layer electroencephalography (EEG), neck electromyography (EMG), inertial measurement…
This dataset contains electroencephalogram (EEG) recordings from 19 healthy participants using a brain-computer music interface designed to enable real-time control of musical tempo through motor imagery. Participants performed kinesthetic motor imagery tasks—imagining squeezing a ball to increase tempo or relaxing to decrease tempo—across nine experimental runs including a calibration phase. The data were collected at 1 kHz sampling rate with 20-second epochs synchronized to music clips, providing a resource for investigating the neural correlates of intentional tempo modulation and music-based brain-computer interface design. This dataset accompanies the publication by Daly et al. (2018). Full methodological details are available in Daly et al. (2014a, 2014b).
…on004306) This dataset consists of electroencephalography (EEG) signals acquired with a 124…
…nemar.on004043) Experiment Details Human electroencephalography recordings from 20 participants. Participants viewed…
BCIComp2020UpperLimb is a preprocessed EEG dataset from BCI Competition 2020 Track 4 containing motor imagery recordings of three grasping tasks (cylindrical, spherical, lumbrical) from 15 healthy subjects across three sessions. The dataset comprises 60-channel EEG data sampled at 250 Hz with 450 trials per subject (150 trials per session across 3 sessions), designed to evaluate session-to-session transfer learning in brain-computer interface applications. Data were preprocessed with 60 Hz notch filtering and cue-aligned epoching, with the 4-second motor imagery window extracted for analysis.