With the rapid development of artificial intelligence and brain science, decoding text from brain signals for cognitive brain-computer interface (BCI) has garnered increasing attention. Among various brain signals, electroencephalography (EEG), a non-invasive modality, offers advantages such as ease of acquisition and high temporal resolution. This paper proposes a method for EEG signal acquisition based on control question stimulation with 8 electrode channels and introduces the ChiCo dataset, which aligns 1040 Chinese text corpora with EEG data. Furthermore, we present a novel framework, EEGTCT, based on the BART model and discrete codex coding, to decode EEG into corresponding Chinese text, achieving a BLEU-1 score of 52.95 on the ChiCo dataset. The findings provide valuable insights for improving BCI for individuals with speech disorders and for understanding the neural mechanisms of Chinese language processing.

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EEGTCT: Electroencephalogram-Based Chinese Text Decoding

  • Sibo Zhang,
  • Bihui Yu,
  • Lili Zhou,
  • Gang Wang,
  • Jingxuan Wei,
  • Linzhuang Sun,
  • Liping Bu

摘要

With the rapid development of artificial intelligence and brain science, decoding text from brain signals for cognitive brain-computer interface (BCI) has garnered increasing attention. Among various brain signals, electroencephalography (EEG), a non-invasive modality, offers advantages such as ease of acquisition and high temporal resolution. This paper proposes a method for EEG signal acquisition based on control question stimulation with 8 electrode channels and introduces the ChiCo dataset, which aligns 1040 Chinese text corpora with EEG data. Furthermore, we present a novel framework, EEGTCT, based on the BART model and discrete codex coding, to decode EEG into corresponding Chinese text, achieving a BLEU-1 score of 52.95 on the ChiCo dataset. The findings provide valuable insights for improving BCI for individuals with speech disorders and for understanding the neural mechanisms of Chinese language processing.