EEG Motor Intention Recognition via Self-Attention CNN
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Solution Overview
Problem
Existing electroencephalogram (EEG) signal processing technologies face challenges in efficiently filtering noise, extracting deep features from limited data, and meeting real-time requirements for dyskinesia rehabilitation training.
Innovation Solution
A dyskinesia rehabilitation training method and system based on EEG signal recognition using a self-attention convolutional neural network architecture, which filters noise, extracts spatially and temporally deep features, and optimizes the network structure for real-time performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual feature extraction based on machine learning is used, then the system is simpler and requires less training data, but recognition accuracy is limited due to single features and noise interference
Solution Approach 1:
The patent replaces manual feature extraction (mechanical system) with deep learning-based automatic feature extraction. The convolutional neural network automatically learns spatial and temporal features from raw EEG signals, eliminating the need for manual feature engineering and achieving higher recognition accuracy while handling the complexity through automated processing
Solution Approach 2:
The patent extracts features from multiple dimensions including spatial dimension (different electrode channels), temporal dimension (time-frequency analysis), and spectral dimension (power spectrum features). This multi-dimensional feature extraction approach overcomes the limitation of single-feature methods and improves recognition accuracy by capturing comprehensive signal characteristics
2Measurement precision
If deep learning techniques are applied to extract effective features, then recognition accuracy improves, but the network model becomes complex and cannot meet real-time requirements
Solution Approach 1:
The patent extracts and focuses on specific critical features from EEG signals using targeted feature extraction methods. By identifying and extracting only the most discriminative spatial and temporal features rather than processing all raw data, the system achieves high recognition accuracy while reducing computational load to meet real-time requirements
Solution Approach 2:
The patent segments the EEG signal processing into distinct stages: spatial filtering, temporal feature extraction, spectral analysis, and classification. This segmentation allows each stage to be optimized independently, achieving high overall accuracy while maintaining real-time performance through efficient modular processing
3Measurement precision
If deep learning models are trained for long time to improve accuracy, then recognition precision improves, but patient fatigue increases and data quality deteriorates
Solution Approach 1:
The patent performs comprehensive offline training and model optimization before actual rehabilitation sessions. The system pre-processes training data, pre-trains the neural network model, and pre-optimizes feature extraction parameters. This preliminary preparation enables the system to achieve high recognition precision during actual use without requiring prolonged real-time training that would cause patient fatigue
Solution Approach 2:
The patent optimizes multiple parameters including learning rate, batch size, network architecture depth, and feature extraction window sizes. By carefully tuning these parameters during offline development, the system achieves high recognition precision with minimal training time during actual rehabilitation sessions, avoiding patient fatigue while maintaining data quality
4Measurement precision
If the network structure is made more complex to extract deep features, then feature extraction capability improves, but real-time response capability deteriorates
Solution Approach 1:
The patent applies different processing strategies to different parts of the EEG signal and network architecture. Spatial filtering is applied to specific electrode channels, temporal feature extraction is focused on critical time windows, and the neural network uses specialized layers (convolutional, recurrent) for specific feature types. This localized optimization achieves deep feature extraction capability while maintaining processing efficiency through targeted rather than universal complex processing
Data Source
AI summary
The present invention discloses a dyskinesia rehabilitation training method and system based on electroencephalogram signal recognition; the method includes: the convolutional neural network is used to recognize the electroencephalogram signal, analyze the patient's motor intention, convert the motor intention into a control instruction for the exoskeleton device, drive the patient's limb movement by controlling the movement of the exoskeleton device, and assist the patient to complete the movement disorder rehabilitation training. In this method, the self-attention mechanism and self-distillation training were used to establish an electroencephalogram signal recognition model, analyzing the patient's motor intention to help motor nerve remodeling, which broke the passive and single problem of traditional rehabilitation methods, realized active rehabilitation of patients, and significantly improved the rehabilitation efficacy.