Imagined Speech Brain-Signal Recognition for Precise Intention Timing
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Solution Overview
Problem
Non-invasive brain-computer interfaces face challenges in accurately recognizing user intentions based on imagined speech due to low-quality brain signals, particularly in distinguishing between different commands and determining the time point of intention generation.
Innovation Solution
A method and apparatus that utilize learning data to set designated commands, perform background processes for recognition, and accurately detect the time point of user intention through brain signal analysis, converting the signals into text for feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If non-invasive brain signal measurement is used, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system performs a background process before actual communication to establish baseline brain signal patterns and designate specific commands. This preliminary setup enables the system to distinguish intended commands from background neural activity, improving measurement precision without requiring invasive surgery.
Solution Approach 2:
The system continuously monitors brain signals and provides feedback by recognizing designated commands and converting them to text. This feedback loop allows the system to refine its understanding of user intentions over time, improving accuracy despite using non-invasive measurement.
2Ease of operation
If imagined speech is used for communication, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system segments the communication process into distinct phases: background process for command designation, intended communication detection, and text conversion. This segmentation allows the system to focus on detecting specific neural patterns associated with imagined speech commands, improving precision while maintaining ease of operation.
Solution Approach 2:
The system changes the parameter of brain signal analysis by focusing on specific frequency bands and temporal patterns characteristic of imagined speech. By adjusting these parameters, the system can distinguish intended commands from background noise, improving measurement precision.
3Adaptability or versatility
If multiple designated commands are recognized, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system performs a background process that designates specific commands before actual communication occurs. This preliminary action simplifies the real-time processing by pre-establishing the set of recognizable commands, enabling multiple command recognition without proportionally increasing device complexity.
Data Source
AI summary
The present invention relates to a brain signal-based user assisting method including the steps of: acquiring learning data for setting a designated command; setting one of a plurality of designated command candidates as a designated command, based on the acquired learning data; performing a background process for recognizing the designated command; determining whether the designated command is recognized continuously more than given times, based on the background process; and if it is determined that the designated command is recognized continuously more than the given times, performing a user's brain signal recording.