Enhanced Generative Adversarial Networks for Unseen Word Generation from EEG Signals
2024 12th International Winter Conference on Brain-Computer Interface (BCI)(2023)
摘要
Recent advances in brain-computer interface (BCI) technology, particularly
based on generative adversarial networks (GAN), have shown great promise for
improving decoding performance for BCI. Within the realm of Brain-Computer
Interfaces (BCI), GANs find application in addressing many areas. They serve as
a valuable tool for data augmentation, which can solve the challenge of limited
data availability, and synthesis, effectively expanding the dataset and
creating novel data formats, thus enhancing the robustness and adaptability of
BCI systems. Research in speech-related paradigms has significantly expanded,
with a critical impact on the advancement of assistive technologies and
communication support for individuals with speech impairments. In this study,
GANs were investigated, particularly for the BCI field, and applied to generate
text from EEG signals. The GANs could generalize all subjects and decode unseen
words, indicating its ability to capture underlying speech patterns consistent
across different individuals. The method has practical applications in neural
signal-based speech recognition systems and communication aids for individuals
with speech difficulties.
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关键词
generative adversarial network,brain-computer interface,electroencephalogram,imagined speech
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