Chinese character writing trajectory recognition method, system and electronic equipment based on brain-computer interface
By collecting and analyzing the neural electrical signals in the user's brain, using machine learning algorithms to identify the writing trajectory of Chinese characters, and adopting a method of imagining Chinese character strokes in one stroke, the problems of complex two-dimensional Chinese character structure and complicated training content are solved, and the efficiency of Chinese character writing communication and the scalability of training for people with diseases are improved.
Patent Information
- Application Number
- CN202311574868.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-11-23
- Publication Date
- 2025-09-12
- Estimated Expiration
- 2043-11-23
AI Technical Summary
In the existing technology, the two-dimensional Chinese character structure is complex and there are a large number of Chinese characters, which leads to complicated training content for users, a long time consumption, easy fatigue, and high cognitive load. It is difficult to promote long-term use among people with diseases. In addition, the training content is not extensible and expandable, the consistency of the neural electrical signals imagined when writing Chinese characters is low, and the communication efficiency is low.
By collecting the neural electrical signals of the first area of interest and the second area of interest in the user's brain, using machine learning algorithms to extract feature signals, identifying the user's writing motion trajectory parameters, and adopting a coherent Chinese character stroke imagination method, the training content is simplified and the consistency of neural electrical signals and communication efficiency are improved.
It simplifies the training content, improves the consistency and communication efficiency of the neural electrical signals in the imagination of Chinese character writing, reduces the user's cognitive load, expands the scalability of the training content, and is suitable for long-term use by people with diseases.