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BCI based on inner speech become a promising and user-friendly systems for human-machine interaction. In resent years interest for this technology has significantly grown due to latest advancements in machine learning and neural networks. In our study the EEG inner speech database containing patterns of 7 word-directions obtained from 13 subjects was formed. Various machine learning techniques were applied to separate word patterns for future application in BCI system. Based on these results, we proposed a cascade machine learning model achieving an overall accuracy of 35.7% for 7 word discrimination task.

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EEG Inner Speech Classification Using Machine Learning Cascade Model

  • 会议论文

  • 发表日期：

    2025年

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  • A. Kh. Ekizyan,
  • P. D. Shaposhnikov,
  • D. V. Kostulin,
  • I. G. Shevchenko,
  • D. G. Shaposhnikov

摘要

BCI based on inner speech become a promising and user-friendly systems for human-machine interaction. In resent years interest for this technology has significantly grown due to latest advancements in machine learning and neural networks. In our study the EEG inner speech database containing patterns of 7 word-directions obtained from 13 subjects was formed. Various machine learning techniques were applied to separate word patterns for future application in BCI system. Based on these results, we proposed a cascade machine learning model achieving an overall accuracy of 35.7% for 7 word discrimination task.