Chinese Speech BCI Neural Decoding Using Semantic Integration
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Solution Overview
Problem
Current research on neural decoding of speech imagery is limited for Chinese language, particularly in tonal languages like Chinese, and lacks semantic decoding capabilities, hindering effective communication for individuals with speech disorders.
Innovation Solution
A speech brain-computer interface neural decoding system is developed, comprising an EEG data acquisition module, significance feature screening and verification module, and speech imagery EEG data decoding module, using a spatial attention layer, convolutional layers, and wav2vec 2.0 speech algorithm to decode and synthesize speech spectrum information into real speech.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If research focuses on non-tonal languages like English and Spanish for speech imagery neural decoding, then classification accuracy can be achieved, but semantic decoding capability is lost
Solution Approach 1:
The patent merges classification functionality with semantic decoding by integrating a semantic decoder into the neural decoding system. The system simultaneously performs speech imagery classification and reconstructs semantic meaning, combining two previously separate functions into a unified framework that handles both tasks together.
Solution Approach 2:
The neural decoding system is designed with multi-functionality to handle both classification and semantic decoding tasks. The speech imagery semantic decoder can process different types of speech imagery data and perform multiple operations (classification, reconstruction, semantic extraction) making the system versatile rather than specialized for a single function.
2Ease of operation
If motor imagery is used to help paralyzed patients control external devices, then device control is achieved, but communication capability is not restored
Solution Approach 1:
The patent introduces an intermediary semantic decoding layer between the motor imagery detection and the output device control. This intermediary component translates the detected neural signals into semantic meaning first, then converts it into controllable commands, adding a layer of semantic understanding that enables natural communication rather than just binary device control.
Solution Approach 2:
The system replaces the mechanical/physical motor imagery control mechanism with a neural-based semantic decoding mechanism. Instead of relying on physical movement patterns or simple motor imagery for device control, the system uses neural signal processing to directly decode semantic content from speech imagery, substituting the mechanical control approach with a more sophisticated neural interpretation approach.
Data Source
AI summary
A speech brain-computer interface neural decoding system based on Chinese language, a method for controlling the speech brain-computer interface neural decoding system, and a method for implementing the speech brain-computer interface neural decoding system are disclosed. The speech brain-computer interface neural decoding system includes an electroencephalography (EEG) data acquisition module, a significance feature screening and verification module, a speech imagery EEG data decoding module, and an understandable speech synthesis module. The speech brain-computer interface neural decoding system integrates EEG data collection, EEG feature extraction, EEG feature screening, EEG signal decoding for reconstructing speech spectrum information and understandable speech synthesis. After obtaining reconstructed spectrogram features, a Pearson correlation analysis may be performed with original spectrogram features, with a correlation generally being ≥80%. The decoding performance of the speech brain-computer interface neural decoding system is superior to traditional decoding models, effectively improving communication between patients with speech disorders and the outside world.