3D Spatial EEG Map from 2D Scalp Data via Trilateration
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Solution Overview
Problem
Non-invasive brain-computer interfaces (BCIs) based on electroencephalography (EEG) have limited functional capabilities due to their inability to accurately localize deep brain signals from scalp recordings, which are essential for fine motor control and sensory perceptions like vision and sound, while invasive methods come with significant risks.
Innovation Solution
A method to computationally derive three-dimensional (3D) spatial EEG data from two-dimensional (2D) scalp recordings using trilateration, identifying signal source locations by analyzing relative time delays and signal fragments across multiple EEG contacts, effectively mimicking the spatial resolution of invasive EEGs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If non-invasive scalp EEG recordings are used, then safety and ease of operation are improved, but measurement precision and spatial resolution deteriorate
Solution Approach 1:
The patent applies dimensionality change by transitioning from 2D scalp EEG data to 3D spatial EEG maps. The system receives 2D EEG signals from scalp electrodes and computationally reconstructs 3D representations of brain activity, enabling localization of deep brain sources in three-dimensional space while maintaining non-invasive safety
Solution Approach 2:
The patent introduces computational algorithms as an intermediary between the non-invasive scalp EEG recordings and the deep brain signal localization. The system uses signal processing techniques including frequency band decomposition, time delay estimation, and trilateration algorithms to bridge the gap between surface recordings and deep source localization
2Measurement precision
If invasive EEG methods are used, then measurement precision is improved, but harmful factors and reliability worsen
Solution Approach 1:
The patent creates a computational copy of invasive EEG functionality using non-invasive methods. The system reconstructs deep brain EEG signals that would normally require invasive electrode placement, providing equivalent measurement precision through mathematical modeling and signal processing rather than physical intrusion
Solution Approach 2:
The patent replaces the mechanical invasive system (surgical electrode implantation) with a non-invasive computational system. Instead of physically inserting electrodes into the brain, the system uses external scalp recordings combined with advanced algorithms to achieve deep source localization, eliminating surgical risks while maintaining measurement capability
3Adaptability or versatility
If deep brain signals are localized from scalp recordings, then functional capabilities are improved, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex signal processing task into distinct frequency bands. The system decomposes EEG signals into multiple frequency components, processes each band separately to identify signal fragments and time delays, and then integrates results to localize deep brain sources, making the complex problem more manageable
Data Source
AI summary
A method of deriving depth EEG data from non-invasive 2D EEG data is described. The method receives several EEG scalp signals, each of which is produced by a contact of an EEG recording device. The method converts each EEG scalp signal into multiple frequency band signals. The method identifies a set of contacts that have similar signal fragments in frequency band signals for a particular frequency band. The method determines relative time delay in frequency band signal arrival at the set of contacts. The method determines relative radius of sphere for the set of contacts based on the relative time delay in frequency band signal arrival at the set of contacts. The method then determines a signal source location by performing trilateration on the set of contacts using locations of the set of contacts and the relative radius of sphere for the set of contacts.