Dataset: Non-binary m-sequences for more comfortable brain–computer interfaces based on c-VEPs

Abstract

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.