Zero-calibration cVEP BCI using word prediction: a proof of concept

TL;DR

This paper introduces a zero-calibration cVEP BCI system that predicts words using VEP relative lag analysis and a dictionary, eliminating the need for initial calibration sessions.

Contribution

It presents a novel zero-calibration approach for cVEP BCIs by leveraging relative lag extraction and word prediction, reducing setup time and user effort.

Findings

Achieved accurate word prediction without calibration.

Reduced BCI setup time significantly.

Demonstrated feasibility of zero-calibration cVEP BCI.

Abstract

Brain Computer Interfaces (BCIs) based on visual evoked potentials (VEP) allow for spelling from a keyboard of flashing characters. Among VEP BCIs, code-modulated visual evoked potentials (c-VEPs) are designed for high-speed communication . In c-VEPs, all characters flash simultaneously. In particular, each character flashes according to a predefined 63-bit binary sequence (m-sequence), circular-shifted by a different time lag. For a given character, the m-sequence evokes a VEP in the electroencephalogram (EEG) of the subject, which can be used as a template. This template is obtained during a calibration phase at the beginning of each session. Then, the system outputs the desired character after a predefined number of repetitions by estimating its time lag with respect to the template. Our work avoids the calibration phase, by extracting from the VEP relative lags between successive characters, and predicting the full word using a dictionary.