A time-frequency feature fusion-based deep learning network for SSVEP frequency recognition.

ABSTRACT:

Introduction

Steady-state visual evoked potential (SSVEP) has emerged as a pivotal branch in brain-computer interfaces (BCIs) due to its high signal-to-noise ratio (SNR) and elevated information transfer rate (ITR). However, substantial inter-subject variability in electroencephalographic (EEG) signals poses a significant challenge to current SSVEP frequency recognition. In particular, it is difficult to achieve high cross-subject classification accuracy in calibration-free scenarios, and the classification performance heavily depends on extensive calibration data.

Methods

To mitigate the reliance on large calibration datasets and enhance cross-subject generalization, we propose SSVEP time-frequency fusion network (SSVEP-TFFNet), an improved deep learning network fusing time

SUBMITTER: Dai Y 

PROVIDER: S-EPMC12515880 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature