Description
MEG data were recorded with a 306-channel (102 magnetometers and 204 planar gradiometers) Elekta Neuromag system (Elekta Oy, Helsinki,Finland) located at the Northern Ireland Functional Brain Mapping (NIFBM) Facility of the Intelligent Systems Research Centre, Ulster University. All the participants were screened for any metallic foreign substance e.g. jewelry, coins, keys or any other ferromagnetic material before entering the magnetically shielded room. The standard fiducial landmarks (left and right preauricular points and Nasion), five head position indicator (HPI) coils (placed over scalp), and the additional reference points over the scalp were digitized (Fastrak Polhemus system) to store information about the participant's head position, orientation, and shape. In addition, ocular and cardiac activities were recorded with two sets of bipolar electro-oculogram (EOG) electrodes (horizontal-EOG and vertical-EOG) and one set of electrocardiogram (EKG) electrodes, respectively. Before starting the data acquisition, the complete procedure and the experimental paradigm were described to the participants. All recordings were made with participants seated on a comfortable chair approximately 80 cm away from the projector screen and in upright position of MEG scanner. The MEG signals were filtered at a bandwidth of 0.1-300 Hz (online) and sampled at the rate of 1 kHz during the acquisition itself. Continuous head position estimation was started after 20 s of raw data recording and kept running for rest of the acquisition period.
| Date made available | 20 Apr 2021 |
|---|---|
| Publisher | figshare |
Research output
- 1 Article
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A magnetoencephalography dataset for motor and cognitive imagery-based brain–computer interface
Rathee, D., Raza, H., Roy, S. & Prasad, G., Dec 2021, In: Scientific Data . 8, 1, 13 p., 120.Research output: Contribution to journal › Article › peer-review
Open AccessFile28 Link opens in a new tab Citations (Scopus)145 Downloads (Pure)
Student theses
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Advancing MEG- and EEG-based decoding of motor imagery for practical brain-computer interfaces for neuro-rehabilitation
Roy, S. (Author), Prasad, G. (Supervisor) & Mc Creadie, K. (Supervisor), Dec 2021Student thesis: Doctoral Thesis
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