100 results for "Eye-Tracking Technology" · page 6 of 10 · ranked by relevance
…Contains physiological recordings like eye-tracking (gaze coordinates and pupil size), and…
…Contains physiological recordings like eye-tracking (gaze coordinates and pupil size), and…
…Subjects were then outfitted and prepped for eye tracking and EEG acquisition…
…Contains physiological recordings like eye-tracking (gaze coordinates and pupil size), and…
This dataset contains EEG recordings from participants performing a novel instruction-following task and an accompanying 1-back localizer task, designed to investigate how complex task instructions are represented and implemented in the brain. Each participant completed a single session including 16 blocks of the main instruction-following task, requiring integration or selection of visual features across sequential instruction screens, and 8 blocks of a localizer task. The dataset is intended to support research on instruction-based cognitive control and rule representation.
…6 types of non-task-related data (eye blinking, eyeball movements, head…
…The Eyetracker data were collected using a portable eye tracker (Tobii Pro…
This dataset contains multimodal brain–machine interface (BMI) recordings from seven healthy adults who trained over nine longitudinal sessions to control a lower-limb exoskeleton via motor imagery. It includes 60-channel EEG, 4-channel EOG, dual IMU motion data, and exoskeleton control/feedback signals collected during open-loop calibration and closed-loop walk/stop trials. The dataset supports research on EEG-based decoding of motor imagery for neurorehabilitation and human-robot interaction applications.
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.