Brain Computer Interface Feature Extraction Filter Training
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Solution Overview
Problem
Current brain computer interface (BCI) technologies face challenges in accurately and quickly analyzing brain waves due to variations in test conditions, requiring lengthy training and retraining of filters, which hinders real-time application, especially for emergency patients like quadriplegia, and slows the development of BCI technology.
Innovation Solution
An apparatus and method for a BCI that includes a feature extraction filter trainer to minimize background brain wave influence and maximize intended brain wave differences, using a classifier trainer with a feature vector to quickly and accurately classify brain waves, incorporating an electroencephalograph, preprocessor, and brain wave classifier, with features like spatial and temporal filtering, and a classifier calibration unit to account for background brain wave changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If common spatial pattern (CSP) filters are used to extract brain wave features, then measurement precision is improved, but training time increases significantly and real-time application is delayed
Solution Approach 1:
The patent applies preliminary action by pre-training the CSP filter using training data collected before the actual BCI application. This allows the filter to be optimized in advance for the specific user's brain wave patterns, so that when real-time classification is needed (e.g., in emergency situations), the filter is already ready and no time-consuming training is required during critical moments.
Solution Approach 2:
The patent implements dynamics by making the CSP filter adaptable and retrainable. The system allows the filter to be dynamically adjusted and retrained with new data as it becomes available, enabling the model to evolve and improve over time while maintaining the ability to perform rapid classification once trained.
2Adaptability or versatility
If CSP filters are retrained with new training data to adapt to changing test conditions, then adaptability is improved, but productivity decreases due to frequent retraining requirements
Solution Approach 1:
The system implements a dynamic training approach where the CSP filter can be retrained when performance degradation is detected or when new training data is available. This dynamic retraining strategy balances adaptability with productivity by only retraining when necessary, rather than continuously or frequently.
Solution Approach 2:
The patent employs feedback mechanisms to monitor the performance of the CSP filter and determine when retraining is needed. By using performance metrics and feedback from classification results, the system can intelligently decide when to initiate retraining, avoiding unnecessary training cycles and maintaining high productivity while still adapting to changing conditions.
3Measurement precision
If multiple electrodes are installed at different positions to capture brain wave variations, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the brain wave detection task across multiple electrodes positioned at different locations on the scalp. Each electrode captures localized brain wave signals, and the CSP filter processes these segmented signals to extract spatial patterns. This segmentation approach improves measurement precision by capturing spatial variations in brain wave activity while managing device complexity through systematic signal processing.
Data Source
AI summary
The present disclosure discloses an apparatus for a brain computer interface (BCI) including a feature extraction filter trainer for training a feature extraction filter which minimizes an influence of a background brain wave while maximizing a difference between intended brain waves; and a classifier trainer for training a classifier for classifying the intended brain waves by using a feature vector obtained by filtering the intended brain wave at the feature extraction filter. With the apparatus, only the background brain wave is additionally measured, such that previous intended brain wave data can be reused and the brain wave can be classified more quickly and accurately.