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Multi-Frequency Steady-State Visual Evoked Potential Dataset

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dataset
posted on 2023-10-02, 09:40 authored by JING MUJING MU, SHUO LIU, ANTHONY BURKITTANTHONY BURKITT, DAVID GRAYDENDAVID GRAYDEN

A detailed data descriptor is published in Scientific Data. Article can be accessed here. Please cite this article when using this dataset.

Mu, J., Liu, S., Burkitt, A.N., Grayden, D.B.. Multi-frequency steady-state visual evoked potential dataset. Sci Data 11, 26 (2024). https://doi.org/10.1038/s41597-023-02841-5

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This dataset includes steady-state visual evoked potentials (SSVEPs) collected using single-frequency, dual-frequency, and tri-frequency stimulation.


File names: All files are named with the participant number and session number “P##_Ses#”, where P refers to Participant and Ses refers to Session. There are 9 sessions for each participant.


Data available in both .mat and .csv format. Download data_in_csv.zip for all data in .csv format.


Number of participants: 35 (20 female)


Stimulation frequencies: 7, 11, 13, 17, 19, 23 Hz


Stimulation methods:

Single frequency (T1): 50% duty cycle square waves

Dual frequency:

  • Frequency superposition OR (T21) on 50% duty cycle square waves
  • Frequency superposition ADD (T22) on 50% duty cycle square waves, each at half brightness
  • Checkerboard pattern (T23) with 50% duty cycle square waves

Tri frequency:

  • Frequency superposition OR (T31) on 50% duty cycle square waves
  • Frequency superposition ADD (T32) on 50% duty cycle square waves, each at 1/3 brightness


Number of stimuli:

Tested with all possible frequency combinations in dual- and tri-frequency stimulation.

  • Single frequency: 6 (one stimulus of each frequency)
  • Dual frequency: 15 (C(6,2)=15)
  • Tri frequency: 20 (C(6,3)=20)


Sessions:

  • Session 1: single frequency stimulation
  • Sessions 2-5: dual-frequency stimulation
  • Sessions 6-9: tri-frequency stimulation


You can use the Matlab code “dataset_processData.m” provided here to separate data (.mat) into trials. The separated data is then named “P##_T##_R#_#”, where P is Participant, T is Test (stimulation method), R is Repeat (each test is repeated 4 times), and the last number is stimuli index. Stimuli were randomly shuffled on the screen but the index does not change and lower frequencies have smaller index, e.g., 11 Hz has index 2 in T1, (7,17) combination has index 3 in T21, T22, T23. A pdf version of the code is also included (“dataset_processData.pdf”).


Should you require further information, please email Jing Mu (jing.mu [at] unimelb.edu.au).

Funding

ARC Training Centre in Cognitive Computing for Medical Technologies

Australian Research Council

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