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# Images of stylized faces improve ERP features used for emotion detection For their ease of accessibility and low cost, current Brain-Computer Interfaces (BCI) used to detect subjective emotional and affective states rely largely on electroencephalographic (EEG) signals. Numerous datasets are publicly available for any researcher to design models for affect detection from EEG. However, few designs focus on optimally exploiting the nature of the stimulus elicitation to improve accuracy. We foun

Participants

26

Size

2.46 GB

Updated

Jun 6, 2026

Reinforcement learning task with 50 healthy controls (25 after a sad mood manipulation, 25 after a happy mood manipulation) Task with a training section and testing section. Task adapted from here: https://doi.org/10.1126/science.1102941. Mood Manipulation occurs during task before each training block. Task included in Matlab programming language. Data collected from 2019-2021 in Cognitive Rhythms and Computation Lab at University of New Mexico. Check the .xls sheet under code folder f

Participants

50

Size

18.3 GB

Updated

Jun 6, 2026

Reinforcement learning task with 50 healthy controls (25 after a sad mood manipulation, 25 after a neutral mood manipulation) Task with a training section and testing section. Task adapted from here: https://doi.org/10.1126/science.1102941. Separate mood manipulation occurred earlier. Task included in Matlab programming language. Data collected from 2019-2021 in Cognitive Rhythms and Computation Lab at University of New Mexico. Check the .xls sheet under code folder for more meta data.

Participants

50

Size

9.81 GB

Updated

Jun 6, 2026

This dataset consists of electroencephalography (EEG) signals acquired with a 124 EEG ANT-Neuro device. ### Participants and Sessions There are 13 participants included, ten performed one session and three performed two sessions. All participants had normal or corrected vision and hearing, apart from sub-16. ### Task The task consisted of imagining and perceiving stimuli from three modalities; visual pictorial, visual orthographic (writing) or auditory. Each of the stimuli belonged to one of

Participants

12

Size

655 MB

Updated

Jun 6, 2026

Two reversal learning tasks with different reinforcer (monetary reward vs. primary threat reinforcer). Positive feedback in the reward task indicated monetary reward (+10 Cent) and negative feedback monetary non-reward (+0 Cent). In the punishment task, positive feedback signaled successful avoidance of a loud white noise burst and negative feedback the application of the noise burst. The white noise burst intensity was titrated to match monetary reward (+10 Cent) for every participant (81 dB, 8

Participants

26

Size

31.5 GB

Updated

Jun 6, 2026

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

18

Size

16.4 GB

Updated

Jun 6, 2026

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

Updated

Jun 6, 2026

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 Niso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort,

Participants

30

Size

76.7 GB

Updated

Jun 6, 2026

# Auditory single word recognition in MEG This dataset is described in Gaston et al. (2022). Stimuli and TextGrids are available from the Massive Auditory Lexical Decision database (Tucker et al., 2019). Converted to BIDS using MNE-BIDS (Appelhoff et al., 2019; Niso et al., 2018). # 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.

Participants

18

Size

11.6 GB

Updated

Jun 6, 2026

# Steer the Ship Twenty-one participants learned to control the trajectory of a ship, represented by centrally presented rotating arrow. Prior to each round participants were cued about the degree of controller and environmental noise ("wind") they would experience. During the round, participants pressed the 'f' and 'j' keys to apply angular force in either a clockwise or counterclockwise direction. The goal of the task was to keep the ship closely oriented towards a target. Points were accumul

Participants

21

Size

3.30 GB

Updated

Jun 6, 2026