# A 40-Class SSVEP Speller Dataset: Beta Range Stimulation for Low-Fatigue BCI Applications

## TL;DR

This paper introduces a new SSVEP speller dataset using beta-range stimulation to reduce visual fatigue in BCI applications.

## Contribution

The novelty lies in using beta-range (14–22 Hz) stimulation to minimize fatigue effects in SSVEP-based BCI systems.

## Key findings

- Beta-range stimulation reduces visual fatigue effects confirmed by subjective ratings and EEG band power analysis.
- High classification accuracy was achieved using calibration-based algorithms on the dataset.
- The dataset includes 40 participants with six SSVEP speller sessions and resting-state recordings.

## Abstract

The inherent non-stationarity of electroencephalography (EEG) signals necessitates large, consistent datasets for reliable brain–computer interface (BCI) research. In steady-state visual evoked potential (SSVEP) paradigms, prolonged exposure to visual stimuli can induce visual fatigue, leading to alterations in EEG patterns that degrade BCI performance. To mitigate fatigue-induced variability, this study employs visual stimulation in the beta frequency range (14–22 Hz), a range that appears less susceptible to the effects of fatigue. We present a comprehensive 40-class SSVEP speller dataset acquired from 40 participants, with EEG data recorded from 31 central-to-occipital channels. Each subject completed six sessions of the SSVEP speller task in addition to pre- and post-experiment resting-state recordings under both eyes-open and eyes-closed conditions. Subjective fatigue ratings combined with EEG band power analyses confirm that beta-range stimulation minimizes fatigue effects. Moreover, the high classification accuracy achieved by calibration-based algorithms indicates that the dataset is well-suited for training advanced SSVEP-based BCI systems.