SSVEP-DAN: A Data Alignment Network for SSVEP-based Brain Computer Interfaces

November 21, 2023 · Entered Twilight · 🏛 arXiv.org

Repo contents: DANet_benchmark.py, DANet_model.py, DANet_wearable.py, README.md, SSVEP_DAN_env.yml, SSVEP_spectral.py, SSVEP_tSNE.py, data_preprocessing.py, trainANDdataloader.py, trca_ablation.py, trca_main.py, trca_util.py

Authors Sung-Yu Chen, Chi-Min Chang, Kuan-Jung Chiang, Chun-Shu Wei arXiv ID 2311.12666 Category cs.LG: Machine Learning Cross-listed eess.SP Citations 2 Venue arXiv.org Repository https://github.com/CECNL/SSVEP-DAN ⭐ 12 Last Checked 3 months ago

Abstract

Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems. However, their efficiency heavily relies on individual training data obtained during time-consuming calibration sessions. To address the challenge of data insufficiency in SSVEP-based BCIs, we present SSVEP-DAN, the first dedicated neural network model designed for aligning SSVEP data across different domains, which can encompass various sessions, subjects, or devices. Our experimental results across multiple cross-domain scenarios demonstrate SSVEP-DAN's capability to transform existing source SSVEP data into supplementary calibration data, significantly enhancing SSVEP decoding accuracy in scenarios with limited calibration data. We envision SSVEP-DAN as a catalyst for practical SSVEP-based BCI applications with minimal calibration. The source codes in this work are available at: https://github.com/CECNL/SSVEP-DAN.