Discover datasets

A 40-class steady-state visually evoked potential (SSVEP) brain-computer interface speller dataset acquired from 40 healthy subjects using beta-range stimulation frequencies (14.0–21.8 Hz) to reduce visual fatigue. The dataset comprises 33-channel EEG recordings (31 scalp + 2 mastoid references) sampled at 1024 Hz across 6 sessions per subject, with 240 trials total using the joint frequency-phase modulation (JFPM) approach. This resource supports the development and benchmarking of low-fatigue BCI applications for communication interfaces.

Participants

40

Channels

31 (10-10)

Citations

4

HED

v8.4.0

Size

16.5 GB

Version

v1.0.2

Updated

Aug 18, 2026

A benchmark steady-state visual evoked potential (SSVEP) dataset comprising EEG recordings from 34 healthy subjects performing a 40-target brain-computer interface speller task using joint frequency and phase modulation. The dataset includes 64-channel EEG data sampled at 250 Hz with visual stimuli flickering at frequencies ranging from 8 to 15.8 Hz, designed to facilitate development and evaluation of SSVEP-based BCI algorithms.

Participants

34

Channels

64 (10-10)

Citations

98

HED

v8.4.0

Size

6.48 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset comprises steady-state visually evoked potential (SSVEP) recordings from 23 healthy participants performing a brain-computer interface task during various locomotor states (standing, walking, running). The study includes 73-channel EEG data acquired at 100 Hz across 4 sessions per subject, with visual stimuli presented at three frequencies (5.45, 8.57, and 12.0 Hz). The dataset is designed to evaluate mobile BCI performance and the feasibility of SSVEP-based control during dynamic physical activity. This is a BIDS-formatted derivative of the original dataset published by Lee et al. (2021) and available at https://osf.io/r7s9b/.

Participants

23

Channels

73 (other)

Citations

34

HED

v8.4.0

Size

1.31 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset contains 64-channel EEG recordings from 24 healthy subjects performing two SSVEP-BCI paradigms (low-frequency 8-15.5 Hz and high-frequency 25.5-33 Hz, 16 targets each) using JFPM visual flicker stimulation. Each subject completed training and fatigue-inducing sessions, enabling study of how mental fatigue affects SSVEP-BCI performance and classification accuracy. The dataset was converted to BIDS format using MOABB and is intended to support research on dynamic stopping strategies and fatigue-aware BCI algorithms.

Participants

24

Channels

64 (10-10)

Citations

4

HED

v8.4.0

Size

33.1 GB

Version

v1.0.1

Updated

Aug 18, 2026

This dataset contains SSVEP (Steady-State Visually Evoked Potential) EEG recordings from 12 healthy subjects seated in an exoskeleton-equipped wheelchair, collected as part of E. Kalunga's PhD research at the University of Versailles. Subjects focused on LED panels flickering at 13, 17, and 21 Hz, or on a fixation point (reject/rest class), across 1–5 sessions per subject. The dataset was designed for developing online SSVEP-based brain-computer interface (BCI) systems using Riemannian geometry, with applications toward assistive robotics.

Participants

12

Channels

8 (10-10)

Citations

74

HED

v8.4.0

Size

160 MB

Version

v1.0.1

Updated

Aug 18, 2026

A 32-channel EEG dataset from 12 healthy subjects performing a spatial navigation task using a single-flicker steady-state visual evoked potential (SSVEP) brain-computer interface. The dataset comprises two sessions per subject: a structured training session with 200 trials per subject recorded at 2048 Hz, and an online adaptive BCI game session recorded at 512 Hz. This paradigm employs a spatially-coded approach where a single 15 Hz flickering stimulus in the center of the screen produces distinct spatial topographies corresponding to four cardinal directions (north, east, west, south), enabling direction-specific decoding without frequency-based analysis methods.

Participants

12

Channels

32 (10-10)

Citations

52

HED

v8.4.0

Size

10.9 GB

Version

v1.0.2

Updated

Aug 18, 2026

A comparative evaluation dataset of steady-state visually evoked potential (SSVEP) based brain-computer interfaces, comprising 14-channel EEG recordings from 11 healthy subjects performing attention tasks on flickering visual stimuli at five different frequencies (6.66–12.00 Hz). The dataset includes preprocessed signals, artifact annotations, and comprehensive documentation of signal processing pipelines for benchmarking state-of-the-art BCI algorithms.

Participants

11

Channels

14 (10-10)

Citations

39

HED

v8.4.0

Size

277 MB

Version

v1.0.2

Updated

Aug 18, 2026

A 256-channel EEG dataset from 11 healthy subjects performing a steady-state visually evoked potential (SSVEP) brain-computer interface task. Subjects focused attention on flickering visual stimuli at five different frequencies (6.66, 7.50, 8.57, 10.00, 12.00 Hz) to select commands. The dataset includes 1,104 trials acquired at 250 Hz sampling rate and was used to systematically evaluate state-of-the-art signal processing algorithms for SSVEP-based BCIs, achieving 74.42% mean accuracy with optimized configurations.

Participants

11

Channels

256 (egi-geodesic)

Citations

38

HED

v8.4.0

Size

9.72 GB

Version

v1.0.2

Updated

Aug 18, 2026

A 256-channel EEG dataset from 11 healthy subjects performing a steady-state visually evoked potential (SSVEP) brain-computer interface task. Subjects attended to flickering visual stimuli at five frequencies (6.66, 7.50, 8.57, 10.00, and 12.00 Hz) presented sequentially, with 1104 total trials recorded at 250 Hz sampling rate. This dataset was used for comparative evaluation of state-of-the-art signal processing algorithms and classification methods for SSVEP-based BCIs.

Participants

11

Channels

256 (egi-geodesic)

Citations

40

HED

v8.4.0

Size

11.4 GB

Version

v1.0.2

Updated

Aug 16, 2026

This dataset comprises 12-class steady-state visual evoked potential (SSVEP) recordings acquired from 10 healthy subjects using 8 EEG channels during a brain-computer interface task. The data were collected to evaluate and compare canonical correlation analysis (CCA)-based methods for SSVEP detection. The dataset includes preprocessed EEG signals with joint frequency-phase modulated visual stimuli ranging from 9.25 to 14.75 Hz. In the reference study, CCA-based methods achieved 92.78% classification accuracy with an information transfer rate of 91.68 bits/min using a combination approach.

Participants

9

Channels

8 (10-10)

Citations

92

HED

v8.4.0

Size

196 MB

Version

v1.0.2

Updated

Aug 18, 2026