EEG Data Compression via Static Image Rendering
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Solution Overview
Problem
Current electroencephalography (EEG) data processing methods face challenges in efficiently transmitting and rendering high-resolution brain activity data due to bandwidth limitations and processing power constraints, making it difficult to access and analyze EEG test results in real-time across various devices without significant network load and computational overhead.
Innovation Solution
A method that involves a remote computer system receiving raw EEG signals, calculating and compressing them into static images based on viewport parameters, and transmitting these images to client devices for rendering, allowing for real-time access and analysis without requiring extensive local processing or high network bandwidth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If raw high-resolution EEG data is transmitted to client devices for real-time access and analysis, then data quality and resolution are improved, but network bandwidth consumption increases significantly
Solution Approach 1:
The patent extracts only the essential visual features of EEG data (waveform patterns, amplitude variations, frequency characteristics) and transmits them as compressed image representations rather than transmitting the complete raw signal data. This extraction approach maintains diagnostic quality while significantly reducing data transmission volume and network bandwidth consumption.
Solution Approach 2:
The patent creates visual copies (image representations) of EEG signal characteristics that can be displayed and analyzed on client devices without requiring transmission of the original high-volume raw data. These image copies preserve the essential diagnostic information while occupying minimal network bandwidth during transmission.
2Productivity
If raw high-resolution EEG data is processed locally on client devices, then data analysis capability is improved, but device processing power requirements increase
Solution Approach 1:
The patent introduces an intermediary processing step where a server or cloud platform performs the computationally intensive tasks of EEG signal processing, feature extraction, and image generation. The client devices only need to receive and display the pre-processed image data, eliminating the need for high local processing power while maintaining full analytical capability.
Solution Approach 2:
The patent segments the EEG processing workflow into distinct stages: data acquisition at the EEG device, complex signal processing and feature extraction at the server level, and simple visualization at the client level. This segmentation allows computationally intensive operations to be performed on powerful servers while client devices handle only lightweight display tasks.
3Loss of information
If complete raw EEG data is transmitted to enable real-time viewing, then data completeness is improved, but transmission time and latency increase
Solution Approach 1:
The patent transforms the data format parameter from raw time-series signal values to compressed image representations. This parameter change dramatically reduces the data volume that needs to be transmitted while preserving all essential diagnostic information, thereby achieving rapid transmission with minimal latency without sacrificing data completeness.
Data Source
AI summary
One variation of a method for hosting mobile access to dense electroencephalography data includes: receiving a set of signals, in a raw resolution, recorded by a set of channels in an electroencephalography headset during an electroencephalography test; receiving, from a client computing device, a view parameters for viewing the set of signals on a display; calculating a quantity of raw signal points per pixel column of the display based on the view parameters and a length of a segment of the electroencephalography test; for each signal in the set of signals, for each discrete contiguous sequence of the quantity of raw signal points within the segment of the signal, calculating a value set characterizing the discrete contiguous sequence of the quantity of raw signal points in the signal; and generating a static image representing value sets for each channel, in the set of channels, across the segment of the electroencephalography test.