Brain Wave Intention Decoding via Dispersion Analysis
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Solution Overview
Problem
Conventional brain wave decoding techniques require a calibration process to optimize prediction-model-type weighting coefficients, which is time-consuming and exhausting for individuals with severe motor impairments.
Innovation Solution
The proposed intention decoding apparatus analyzes brain wave data by classifying event-related potentials into stimulus events, identifying the classification with maximum dispersion as the intended stimulus, and calculating a discriminant model expression to decode intentions without requiring an advance preparation step.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional brain wave decoding techniques are used with calibration process, then prediction accuracy is improved, but time consumption and operational burden increase
Solution Approach 1:
The system performs preliminary classification of brain wave data into multiple stimulus event groups before decoding, establishing a structured framework that eliminates the need for time-consuming calibration processes while maintaining high prediction accuracy
Solution Approach 2:
The invention changes the approach from optimizing weighting coefficients through calibration to using classification-based dispersion analysis, fundamentally altering the parameter optimization method to reduce time consumption
2Measurement precision
If conventional calibration process is performed to optimize weighting coefficients, then decoding accuracy is improved, but operational complexity increases
Solution Approach 1:
The system automatically performs classification and dispersion calculation without requiring manual calibration input from the user, making the operation simpler while maintaining high decoding accuracy through automated analysis
3Productivity
If classification by dispersion is performed on all stimulus events, then decoding speed is improved, but computational complexity increases
Solution Approach 1:
The system segments brain wave data into multiple stimulus event groups based on classification, allowing parallel processing of each group which increases decoding speed while managing computational complexity through structured division
Data Source
AI summary
An intention decoding apparatus and a decoding method that quickly and precisely analyze brain waves for decoding an intention decision without requiring an advance brain wave measurement for, for example, a calibration step, and an intention conveyance assist apparatus, an intention conveyance assist system, and a program using a decoding result of the intention decision in the brain. The intention decoding apparatus that analyzes brain waves to decode an intention performs a process to examine a dispersion by classifying brain wave data of event-related potentials corresponding to a plurality of stimulus events into any one stimulus event and the other stimulus events among the plurality of stimulus events, on all the stimulus events, and identifies a classification where the dispersion becomes maximum to identify the one stimulus event in the classification as the intention. The intention conveyance assist apparatus includes a presentation unit that presents a decoding result.