…reading and math skills by assistive technology ## Year that the project ran…
…nemar.on004624) An Ecosystem of Technology and Protocols for Adaptive Neuromodulation Research…
NEMAR Dataset nm000105: discrete_gestures - Discrete hand gesture detection from EMG
…Online SSVEP-based BCI using Riemannian geometry for assistive robotics with shared…
TX14 is an EEG dataset collected while participants performed a local situational awareness task and simultaneously maintained supervisory control of a semi-autonomous vehicle. It addresses the shift of the soldier's role from active controller to system monitor and manager in autonomous military systems. The dataset supports study of trade-offs between local area awareness (target/threat detection) and vehicle control, informing dynamic task allocation in crewstations.
…rehabilitation, assistive_technology, neurofeedback, user_training - **Environment**: laboratory - **Online feedback**: True ## Tags…
A longitudinal brain-machine interface dataset from seven healthy adults who controlled a Rex Bionics lower-limb exoskeleton through motor imagery (walk/stop). Each participant completed nine sessions, each with an open-loop calibration training phase followed by twelve closed-loop control trials and extended 6-minute walk and stop imagery tasks. The dataset includes 60-channel EEG, EOG, IMU motion data from the head and exoskeleton, and exoskeleton control and feedback signals.
A multimodal dataset of simultaneous 64-channel EEG, head-mounted eye-tracking, photoplethysmography (PPG), and galvanic skin response (GSR) recordings from 23 healthy adults. Participants took part in a naturalistic smartphone use condition and a standardized video viewing condition. Hardware TTL pulses synchronize the EEG and eye-tracking streams for temporal alignment across modalities.
[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on006803-blue…
EEG recordings from healthy participants performing self-initiated palmar and lateral reach-and-grasp actions, acquired simultaneously across three recording systems: gel-based laboratory EEG, water-based mobile EEG, and dry-electrode mobile EEG. The dataset supports investigation of the feasibility of decoding natural reach-and-grasp movement-related cortical potentials across EEG acquisition modalities for brain-computer interface applications. It is distributed in BIDS format as part of the MOABB benchmark collection.