RT dataset
T1 Dataset: Non-binary m-sequences for more comfortable brain–computer interfaces based on c-VEPs
A1 Martínez Cagigal, Víctor
A2 Universidad de Valladolid. Grupo de Ingeniería Biomédica
K1 Neurotechnology
K1 Biomedical Engineering
K1 Artificial intelligence
K1 Computer science
K1 Neuroscience
K1 Non-binary codes
K1 Visual fatigue
K1 Code-modulated visual evoked potential (c-VEP)
K1 Brain–computer interface (BCI)
K1 Electroencephalography (EEG)
K1 2490 Neurociencias
K1 1203.04 Inteligencia Artificial
AB Code-modulated visual evoked potentials (c-VEPs) have marked a milestone in the scientific literature due to their ability to achieve reliable, high-speed brain–computer interfaces (BCIs) for communication and control. Generally, these expert systems rely on encoding each command with shifted versions of binary pseudorandom sequences, i.e., flashing black and white targets according to the shifted code. Despite the excellent results in terms of accuracy and selection time, these high-contrast stimuli cause eyestrain for some users. For this reason, we propose the use of non-binary p-ary m-sequences, whose levels are encoded with different shades of gray, as a more pleasant alternative than traditional binary codes.In this dataset, a total of 16 healthy participants engaged in BCI spelling tasks using five different p-ary m-sequences: binary GF(2^6) with a base of 2, GF(3^5) with a base of 3, GF(5^3) with a base of 5, GF(7^2) with a base of 7, and GF(11^2) with a base of 11. Each participant completed a single session that included a calibration phase consisting of 300 cycles (repetitions of the p-ary m-sequence), followed by an online spelling task of 32 trials (with 10 cycles per trial) for each condition. Online selections were made using a 4x4 command matrix (chance level of 6.25%), consisting of alphabetic characters from A to P. In addition, qualitative measures regarding visual fatigue and satisfaction were collected.
YR 2024
FD 2024-10-25
LK https://uvadoc.uva.es/handle/10324/70945
UL https://uvadoc.uva.es/handle/10324/70945
LA spa
NO Martínez-Cagigal, V., Santamaría-Vázquez, E., Pérez-Velasco, S., Marcos-Martínez, D., Moreno-Calderón, S., & Hornero, R. (2023). Non-binary m-sequences for more comfortable brain–computer interfaces based on c-VEPs. Expert Systems with Applications, 232, 120815.
NO Grupo de Ingeniería Biomédica
DS UVaDOC
RD 21-nov-2024