EEG Target Recognition in Natural Reading With Semantic Highlighting

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Solution Overview

Problem

Existing methods for studying brain cognition in natural reading environments face challenges due to semantically ambiguous textual information, making it difficult to analyze EEG signals effectively.

Innovation Solution

A target recognition method using a trained EEG classification model to identify fuzzy semantic targets in a natural reading environment, followed by real-time highlighting of relevant annotations and information on a display device based on EEG signals.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If EEG signals are used to study brain cognition in natural reading environments, then information acquisition efficiency is improved, but semantic ambiguity makes it difficult to analyze EEG signals effectively

Engineering Contradiction:

Improveinformation acquisition efficiencyVSAvoidEEG signal analysis accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent introduces an EEG classification model as an intermediary between the raw EEG signals and the semantic analysis. This model processes the complex EEG data and translates it into interpretable cognitive states, bridging the gap between signal acquisition and meaningful interpretation in natural reading environments

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent transforms the analysis approach by changing parameters from traditional ERP component analysis to a comprehensive EEG classification framework that considers multiple features including amplitude, frequency, and temporal dynamics, thereby improving measurement precision while maintaining high information acquisition efficiency

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If traditional ERP analysis methods are used, then specific brain region responses can be identified, but cognitive processing stages such as decision-making and working memory updating cannot be comprehensively analyzed

Engineering Contradiction:

Improvebrain region response identificationVSAvoidcognitive task pattern classification capability

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The EEG classification model is designed with multi-functionality to serve multiple cognitive analysis purposes simultaneously. It can identify specific brain region responses while also classifying various cognitive task patterns including decision-making, working memory updating, and attention states, making the system adaptable to diverse research needs

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent segments the cognitive processing analysis into distinct stages by training separate classification models for different cognitive tasks. This allows precise identification of brain region responses for each cognitive stage while maintaining the ability to comprehensively analyze the entire cognitive process

Inventive Principle:

Principle #1Segmentation

3Measurement precision

If clear objective paradigms are used for text analysis, then specific text cognition can be studied, but semantically ambiguous information in natural reading environments cannot be effectively processed

Engineering Contradiction:

Improvetext cognition analysis accuracyVSAvoidnatural reading environment applicability

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent implements a dynamic analysis framework that adapts to the semantic characteristics of the text being analyzed. The EEG classification model adjusts its parameters and processing approach based on the detected semantic ambiguity, enabling effective processing of both clear and ambiguous textual information in natural reading environments

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20260013781A1Target recognition methods based on electroencephalogram signals in natural reading environment

Publication Date: 2026.01.15 TIANJIN UNIV

AI summary

A target recognition method based on an electroencephalogram (EEG) signal in a natural reading environment is provided, including: presenting a fuzzy semantic target to a subject through a user interaction window on a display device; acquiring EEG signals of the subject via a wireless EEG acquisition device to obtain a target EEG signal corresponding to the fuzzy semantic target; determining a binary classification result corresponding to the fuzzy semantic target based on the target EEG signal through a trained EEG classification model; in response to the binary classification result indicating that the fuzzy semantic target is recognized, determining a semantic category to which the fuzzy semantic target belongs; and controlling, based on the semantic category, the display device to highlight annotations on the user interaction window; wherein highlighting annotations includes highlighting a text or an image related to the fuzzy semantic target and displaying associated information of the semantic category.