Discover datasets

Dataset consists of 6 participants who performed SSVEP tasks. We designed stimulations at 3 different frequencies (2 Hz, 4 Hz, 8 Hz). Each participant attended to 3 trials for each frequency in which they remained static as much as possible to avoid artifacts. They attended to 3 trials for each frequency in which they made voluntary head/neck and eye movements. Please refer to Kumaravel et al., (IEEE EMBC 2021) for further details.

Participants

6

Size

242 MB

Participants

4

Size

6.13 GB

This dataset contains behavioral events and electrophysiological recordings from an experiment run in the Computational Memory Lab at the University of Pennsylvania from 2021-2022 with funding from U.S. Army Medical Research and Development Command (USAMRDC) through the Medical Technology Enterprise Consortium (MTEC) project MTEC-20-06-MOM-013, "Restoring memory with task-independent semi-chronic closed-loop direct brain stimulation and non-invasive closed-loop stimulus timing optimization". Thi

Participants

34

Size

1.30 TB

CONTACT For questions about this data set, please contact Anna Mai (anna.mai@mpi.nl; ORCiD 0000-0002-8343-9216). PERMISSIONS These data may not be used for commericial purposes, including but not limited to use in any kind of training set for commercial machine learning applications. These data may not be used in any way that either in part or in whole disambiguates participant identity, including but not limited to attempts at 3D facial reconstruction. RECORDING SETUP These d

Participants

10

Size

12.4 GB

# Information This dataset contains intracranial EEG (iEEG) recordings from 8 patients during single pulse electrical stimulation used in the publication of: Ojeda Valencia G, Gregg N, Huang H, Lundstrom B, Brinkmann B, Pal Attia T, Van Gompel J, Bernstein M, In MH, Huston J, Worrell G, Miller KJ, and Hermes D. 2023. Signatures of electrical stimulation driven network interactions in the human limbic system. Journal of Neuroscience (in press). # License This dataset is made available under the

Participants

8

Size

14.2 GB

Dataset is related to publication: Mąka, S., Chrustowicz, M., & Okruszek, Ł. (2023). Can we dissociate hypervigilance to social threats from altered perceptual decision-making processes in lonely individuals? An exploration with Drift Diffusion Modeling and event-related potentials. Psychophysiology, e14406. https://doi. org/10.1111/psyp.14406

Participants

52

Size

19.9 GB

Participants (N=17, all males) with an average age of 32.8 years performed a guided visual search task in parallel with a second binaurally presented auditory task (Ries, et al., 2016). EEG data from each participant were recorded using a 64-channel BioSemi ActiveTwo system digitized at 512 Hz. Four external electrodes were used to record bipolar horizontal and vertical EOG signals, and a single external electrode was placed on each of the left and right mastoids to provide the reference signals

TNO dataset

Participants

21

Size

7.25 GB

TX20 dataset Vehicle survivability is critically important in today’s military. Survivability is critically impacted by the performance of human operators – especially as it degrades with various factors. Significant DoD investments have focused on developing and integrating autonomous technologies to mitigate the effects of human error. However, simply implementing autonomy without having a clear plan for integrating with human operators can lead to relatively poor performance and thus low use

Participants

24

Size

43.1 GB

EEG: silent and perceive speech on 30 Spanish sentences Large Spanish Speech EEG dataset Authors <ul> <li>Carlos Valle</li> <li>Carolina Mendez-Orellana</li> <li>María Rodríguez-Fernández</li> </ul> Resources: <ul> <li>Code availaible at: https://github.com/cgvalle/Large_Spanish_EEG</li> <li>Publication: Valle, C., Mendez-Orellana, C., Herff, C., & Rodriguez-Fernandez, M. (2024). Identification of perceived sentences using deep neural networks in EEG. Journal of neural engineer

Participants

56

Size

25.2 GB