Improving SSVEP BCI Spellers With Data Augmentation and Language Models

Abstract

Steady-State Visual Evoked Potential (SSVEP) spellers are a promising communication tool for individuals with disabilities. This Brain-Computer Interface utilizes scalp potential data from (electroencephalography) EEG electrodes on a subject's head to decode specific letters or arbitrary targets the subject is looking at on a screen. However, deep neural networks for SSVEP spellers often suffer from low accuracy and poor generalizability to unseen subjects, largely due to the high variability in EEG data. In this study, we propose a hybrid approach combining data augmentation and language modeling to enhance the performance of SSVEP spellers. Using the Benchmark dataset from Tsinghua University, we explore various data augmentation techniques, including frequency masking, time masking, and noise injection, to improve the robustness of deep learning models. Additionally, we integrate a language model (CharRNN) with EEGNet to incorporate linguistic context, significantly enhancing word-level decoding accuracy. Our results demonstrate accuracy improvements of up to 2.9 percent over the baseline, with time masking and language modeling showing the most promise. This work paves the way for more accurate and generalizable SSVEP speller systems, offering improved communication solutions for individuals with disabilities.