Brain Image Semantic Vector Reconstruction
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Solution Overview
Problem
Current methods for decoding mental content from functional brain images are limited, particularly in generating natural language representations, as they require extensive databases for stimuli and struggle with complex mental states like language text, and are not feasible for reconstructing complex constructs such as clouds of words sized by their probability in the subject's mind.
Innovation Solution
A system and method that uses a combination of linguistic semantic vector representation, basis learning, and text generation modules to decode mental content from functional brain images, where semantic vectors are assigned to training data, mapped to brain activation patterns, and used to generate natural language sentences, enabling the reconstruction of mental content from unknown stimuli without relying on specific input types or databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative brain decoding is used to reconstruct mental content, then the ability to represent complex mental states is improved, but the requirement for extensive databases with most conceivable stimuli increases
Solution Approach 1:
The patent extracts only the essential semantic features from stimuli rather than storing complete stimulus databases. By mapping brain activity patterns to semantic vectors that capture the core meaning, the system eliminates the need for extensive stimulus databases while maintaining the ability to represent complex mental states.
Solution Approach 2:
The patent introduces semantic vectors as an intermediary representation between brain activity patterns and mental content. Instead of directly mapping to specific stimuli from a database, the system uses semantic vectors to bridge the gap, allowing generalization to unseen stimuli while requiring minimal training data.
2Adaptability or versatility
If a database with most conceivable stimuli is used for generative brain decoding, then the coverage of mental content is improved, but the feasibility and complexity of the system decreases
Solution Approach 1:
The system extracts only the essential semantic dimensions needed to represent mental content, rather than maintaining comprehensive stimulus databases. This extraction approach maintains coverage of mental content while dramatically reducing system complexity and improving feasibility.
Solution Approach 2:
The patent changes the parameter space from discrete stimulus categories to continuous semantic vectors. This transformation allows the system to represent any mental content within the semantic space without requiring explicit database entries, thereby reducing complexity while maintaining versatility.
3Measurement precision
If language text is decoded using traditional methods, then the accuracy for known stimuli is improved, but the applicability to unknown or complex mental constructs decreases
Solution Approach 1:
The patent uses semantic vectors as an intermediary that enables generalization from known to unknown stimuli. The semantic representation captures the essential meaning that can be applied to any stimulus within the semantic space, maintaining accuracy for known stimuli while extending applicability to unknown or complex mental constructs.
Solution Approach 2:
The semantic vector representation serves multiple functions simultaneously: it captures the meaning of known stimuli for accurate decoding, represents unseen stimuli through semantic similarity, and enables generation of novel mental content representations. This multi-functionality resolves the contradiction between precision and versatility.
Data Source
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AI summary
By way of introduction, the present embodiments described below include apparatuses and methods for generating natural language representations of mental content from functional brain images. Given functional imaging data acquired while a subject reads a text passage, a reconstruction of the text passage is produced. Linguistic semantic vector representations are assigned (1301) to words, phrases or sentences to be used as training stimuli. Basis learning is performed (1305), using brain imaging data acquired (1303) when a subject is exposed to the training stimuli and the corresponding semantic vectors for training stimuli, to learn an image basis directly. Semantic vector decoding (1309) is performed with functional brain imaging data for test stimuli and using the image basis to generate a semantic vector representing the test imaging stimuli. Text generation (1311) is then performed using the decoded semantic vector representing the test imaging stimuli.