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100 results for "sensorimotor control" · page 4 of 10 · ranked by relevance
…control and arousal (under threat of unpredictable shock). ## Event labels 100 - 5…
…In order to have control samples for the cellular outcomes and control…
This dataset contains EEG recordings from 14 healthy participants performing a covert attention task with real-time neurofeedback based on alpha power lateralization. The study investigates whether real-time EEG feedback on alpha power lateralization can lead to behavioral improvements in a covert attention task, employing a single-blinded crossover design comparing real versus sham feedback conditions across three recording sessions.
This dataset comprises EEG recordings from 15 healthy participants performing self-initiated reach-and-grasp motor tasks using three different recording systems: gel-based laboratory equipment, water-based mobile EEG, and dry-electrode mobile EEG. Data were acquired at 256 Hz from 58 EEG channels plus 6 EOG channels across three sessions with a total of 7,200 trials. Participants executed palmar and lateral grasp actions toward objects while EEG signals were recorded. The study investigates the feasibility of decoding natural reach-and-grasp neural correlates across different EEG acquisition modalities for brain-computer interface applications.
A multi-session EEG dataset acquired from 15 healthy participants performing resting state and graded cognitive tasks (MATB-II at three difficulty levels). The dataset comprises 62-channel EEG recordings at 500 Hz sampling rate designed for passive brain-computer interface applications and mental workload estimation in neuroergonomic contexts. Raw EEG data are provided with standardized event annotations using HED 8.4.0 schema and MOABB-compatible feature extraction pipelines (bandpower analysis and Riemannian covariance methods) for benchmarking purposes.
BCIComp2020UpperLimb is a preprocessed EEG dataset from BCI Competition 2020 Track 4 containing motor imagery recordings of three upper-limb 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 × 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.
…Perform a local situational awareness task while maintaining supervisory control of a…
…Evidence from a Randomized, Controlled Trial ### Introduction **Overview:** Both forward and backward…
Imported from OpenNeuro ds004362