Discover datasets

### Free Recall with Open-Loop Stimulation at Encoding #### Description This dataset contains behavioral events and intracranial electrophysiological recordings from a delayed free recall task with open-loop stimulation at encoding. The experiment consists of participants studying a list of words, presented visually one at a time, completing simple arithmetic problems that function as a distractor, and then freely recalling the words from the just-presented list in any order. The data was col

Participants

38

Size

64.9 GB

References ---------- Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., D

Participants

37

Size

372 MB

Semantic conditioning task The dataset was used in this article: Pavlov YG, Menger NS, Keil A, Kotchoubey B. 2024. Contingency awareness shapes neural responses in fear conditioning. https://doi.org/10.1101/2024.08.13.607803

Participants

81

Size

19.8 GB

References ---------- Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., D

Participants

7

Size

18.1 GB

Description -------- This is a data set consisting of simultaneous electroencephalography (EEG), electrospinography (ESG), electroneurography (ENG), and electromyography (EMG) recordings from 26 participants. There were nine different recording conditions: i) resting state with eyes open, ii) mixed median nerve stimulation (arm nerve), iii) mixed tibial nerve stimulation (leg nerve), iv) sensory nerve stimulation of the index finger, v) sensory nerve stimulation of the middle finger, vi) simulta

Participants

26

Size

377 GB

Description -------- This is a data set consisting of simultaneous electroencephalography (EEG), electrospinography (ESG), electroneurography (ENG), and electromyography (EMG) recordings from 40 participants. There were four different recording conditions: i) resting state with eyes open, ii) mixed median nerve stimulation (arm nerve), iii) mixed tibial nerve stimulation (leg nerve), and iv) alternating mixed median or tibial nerve stimulation. For each participant, there is i) the simultaneous

Participants

40

Size

683 GB

This dataset was utilized for the publication of the manuscript by Zhang et al. (in preparation). A subset of the data has been employed in [1] and [2]. Summary: This data set comprises the de-identified subjects with interictal iEEG recordings with sleep from University of California Los Angels Mattel Children’s Hospital, and Children’s Hospital of Michigan, Detroit. Subject-wise information is contained in each folder, including iEEGs collected from 185 subjects during sleep. The channel nam

Participants

185

Size

102 GB

### Free Recall of Word Lists with Repeated Items #### Description This dataset contains behavioral events and intracranial electrophysiological recordings from a repated item free recall task. The experiment consists of participants studying a list of words, presented visually one at a time, and then freely recalling the words from the just-presented list in any order. On each list, there is a 7-second delay period between the encoding and recall phases. The data were collected at clinical s

Participants

48

Size

157 GB

References ---------- Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C.

Participants

29

Size

13.3 GB

Notes ---------- Electroglottography (EGG) and audio are included in the EEG files themselves, rather than in sidecar files, as they were converted from analog to digital on the same hardware. The audio is the audio the subject heard, i.e. their delayed auditory feedback. If you want the speech waveform aligned to the time the subject produced it, you can shift the audio back by the timestamps recorded (for each trial) in the delay field of the events sidecar file. EGG has already been minimal

Participants

32

Size

118 GB