Application of Kurtosis Based Dynamic Window to Enhance SSVEP Recognition

Yin, Haojun; Ji, Zhouyu; Lian, Zequan; Yang, Yang; Liu, Nankun; Wang, Hongtao

doi:10.1109/cac57257.2022.10055430

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Hadi et al [38] proposed a novel DW classifier, using ensembling learning for SSVEP recognition. [39], [40] enhanced DW threshold selection to further improve the ITR for SSVEP classification. DL-based DW has also been used in SSVEP recognition, e.g., Zhou et al [41] proposed an EEGNet-DW approach, which uses a different EEGNet model and threshold for each DW length.…”

Section: Dynamic Window For Eeg Classificationmentioning

confidence: 99%

Front-End Replication Dynamic Window (FRDW) for Online Motor Imagery Classification

Chen,

An,

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Motor imagery (MI) is a classical paradigm in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Online accurate and fast decoding is very important to its successful applications. This paper proposes a simple yet effective front-end replication dynamic window (FRDW) algorithm for this purpose. Dynamic windows enable the classification based on a test EEG trial shorter than those used in training, improving the decision speed; front-end replication fills a short test EEG trial to the length used in training, improving the classification accuracy. Within-subject and cross-subject online MI classification experiments on three public datasets, with three different classifiers and three different data augmentation approaches, demonstrated that FRDW can significantly increase the information transfer rate in MI decoding. Additionally, FR can also be used in training data augmentation. FRDW helped win national champion of the China BCI Competition in 2022.

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