Forward-Backward Fourier Transform EEG Imaging for Brain Disease Detection
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Solution Overview
Problem
Existing methods for interpreting and diagnosing brain disorders from EEG signals are challenging due to their complexity and noise, making it difficult for medical practitioners to accurately detect and classify brain diseases.
Innovation Solution
The implementation of a Forward Backward Fourier Transform (FBFT) model for brain disease detection, which enhances feature extraction and classification by transforming EEG signals into interpretable medical images, utilizing a novel time-frequency technique and optimal channel selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional EEG analysis methods are used, then the complexity of interpretation is reduced, but diagnostic accuracy and detection precision deteriorate
Solution Approach 1:
The patent introduces an intermediary system comprising automated feature extraction algorithms, signal processing pipelines, and AI/ML classification models that mediate between raw EEG signals and diagnostic conclusions. This intermediary layer transforms complex raw signals into structured features and diagnostic predictions, reducing interpretation complexity while maintaining or improving diagnostic accuracy through systematic analysis.
Solution Approach 2:
The patent replaces manual mechanical interpretation methods with automated computational systems. Instead of relying on practitioners to manually analyze complex EEG patterns, the system uses automated feature extraction, digital signal processing, and machine learning algorithms to perform the analysis, thereby reducing interpretation complexity while enhancing measurement precision through consistent, data-driven decision-making.
2Reliability
If EEG signals are amplified and denoised, then signal quality improves, but processing time and computational complexity increase
Solution Approach 1:
The patent applies preliminary signal processing actions including amplification, denoising, and feature extraction before main analysis. By pre-processing EEG signals to enhance quality and extract relevant features upfront, the system reduces the computational burden of subsequent analysis stages, thereby improving signal quality while managing processing time through efficient staged processing.
Solution Approach 2:
The patent extracts relevant features from raw EEG signals through dedicated feature extraction modules. By separating and extracting only the most diagnostically relevant features from the full signal spectrum, the system improves signal quality by focusing on meaningful patterns while reducing processing time by eliminating analysis of redundant or less informative signal components.
3Productivity
If dimensionality reduction is applied to EEG data, then computational efficiency improves, but information loss may occur
Solution Approach 1:
The patent transforms EEG data from spatial/time-domain parameters to frequency-domain parameters through Fourier transform and other spectral analysis methods. This parameter transformation maintains essential diagnostic information by converting spatial patterns into frequency characteristics that are equally informative for diagnosis but more computationally efficient to process and analyze.
Solution Approach 2:
The patent transitions EEG analysis from three-dimensional spatial data (multiple channels across time) to two-dimensional frequency-time representations through spectral analysis. This dimensional transformation preserves critical diagnostic information by projecting spatial patterns into frequency domains while reducing computational complexity through the inherent structure of frequency representations.
Data Source
AI summary
The present disclosure provides for a forward backward Fourier transform (FBFT) model for brain disease detection. According to one aspect of the present disclosure a forward backward Fourier transform (FBFT) model for brain disease detection. According to a second aspect of the present disclosure a method of using a forward backward Fourier transform (FBFT) model for brain disease detection.