EEG Intrinsic Frequency Estimation From Resting-State Signals
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Solution Overview
Problem
Existing electroencephalogram analysis methods require numerous transitions between resting and motor imagery states, increasing the time and mental and physical burden on the analysis subject, particularly for patients with paralyzed parts.
Innovation Solution
An electroencephalogram analysis device that acquires and analyzes electroencephalographic signals during rest to estimate intrinsic frequencies correlated with movement intentions using a sequential Bayesian method, allowing for transformation into intrinsic frequencies without requiring movement or additional transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of trials and duration of transition from resting state to motor imagery state are increased to increase accuracy in identification of intrinsic frequency, then the measurement precision is improved, but the time required for measurement and the mental and physical burden on the analysis subject increase
Solution Approach 1:
The patent applies preliminary action by estimating the intrinsic frequency during the resting state before the actual motor imagery task. The computation unit calculates an estimate value of the peak frequency using electroencephalographic signals acquired during rest, which is then transformed into the intrinsic frequency. This preliminary estimation eliminates the need for multiple full trial cycles, significantly reducing measurement time while maintaining accuracy.
Solution Approach 2:
The patent extracts the essential information needed for intrinsic frequency identification from the resting state signals alone, rather than requiring complete motor imagery trial data. By taking out the peak frequency estimate from rest period signals and using transformation rules, the system obtains sufficient accuracy without the time cost of full motor imagery trials.
2Measurement precision
If the number of trials and duration of transition from resting state to motor imagery state are increased to increase accuracy in identification of intrinsic frequency, then the measurement precision is improved, but the mental and physical burden on the analysis subject increases
Solution Approach 1:
The system performs preliminary frequency estimation during the resting state, which is a passive condition requiring minimal mental and physical effort from the subject. By completing the intrinsic frequency identification in advance during rest, the subject is spared from repeated active motor imagery tasks that would increase mental and physical burden.
Solution Approach 2:
The patent extracts the necessary frequency information from the resting state signals, eliminating the need for subjects to perform repeated motor imagery transitions. This extraction approach maintains measurement precision while significantly reducing the operational burden on the analysis subject.
3Measurement precision
If the measurement time is increased to identify intrinsic frequency with higher accuracy, then the measurement precision is improved, but the productivity decreases
Solution Approach 1:
The patent performs the intrinsic frequency estimation as a preliminary action during the resting state, which is a time-efficient approach. By acquiring signals during rest and computing the peak frequency estimate in advance, the system achieves high measurement precision without requiring lengthy measurement sessions, thus improving productivity.
Solution Approach 2:
The system extracts the peak frequency estimate from resting state signals and transforms it into the intrinsic frequency, achieving accurate measurement in a fraction of the time required for traditional multi-trial methods. This extraction approach significantly improves measurement efficiency while maintaining precision.
Data Source
AI summary
An electroencephalogram analysis device includes a signal acquisition unit that acquires a time series of electroencephalographic signals of an analysis subject, and a computation unit that obtains an intrinsic frequency correlated with a movement intention of the analysis subject based on a frequency characteristic related to the time series of the electroencephalographic signals of the analysis subject during rest.