Remote EEG Monitoring With Stimulus-Synced Home Neurocognitive Tasks
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Solution Overview
Problem
Clinical EEG monitoring is hindered by high costs and difficulty in maintaining patient engagement, limiting its use for remote and longitudinal monitoring due to the high cost of device acquisition and staffing, and the inability of consumer-grade devices to time-stamp stimuli for data capture.
Innovation Solution
Systems and methods for remote and longitudinal EEG monitoring using consumer-grade devices that synchronize EEG data with stimuli presentation, enabling independent operation at home and allowing for the extraction of informative features like event-related potentials, with features stored in a personalized EEG passport for anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinical-grade EEG devices are used for remote monitoring, then measurement precision and reliability are improved, but device cost and operational complexity increase significantly
Solution Approach 1:
The patent employs consumer-grade EEG devices that are inexpensive and accessible to patients for home use. These devices are replaced or reused across multiple monitoring sessions, providing cost-effective longitudinal data collection without requiring expensive clinical-grade equipment at each patient location.
Solution Approach 2:
The patent introduces a centralized processing server as an intermediary that receives raw EEG data from consumer devices, performs sophisticated signal processing, artifact removal, and feature extraction. This mediator enables clinical-grade analysis capabilities while patients use simple consumer devices at home.
2Reliability
If clinical EEG monitoring is implemented for longitudinal tracking, then disease progression detection is improved, but patient engagement difficulty and operational costs increase
Solution Approach 1:
The system enables patients to independently conduct EEG monitoring sessions at home without requiring clinical staff presence. Patients self-administer the monitoring tasks, with automated guidance through the neurocognitive tasks and stimuli presentation, eliminating the need for specialized staffing while maintaining monitoring reliability.
Solution Approach 2:
The system pre-configures monitoring protocols, task sequences, and stimulus presentations before patient use. Automated pipelines are established in advance for data processing and analysis, allowing patients to simply follow predefined instructions rather than requiring real-time clinical guidance, thereby improving ease of operation while maintaining reliability.
3Device complexity
If consumer-grade EEG devices are used, then device cost and accessibility are improved, but ability to time-stamp stimuli and capture synchronized data deteriorates
Solution Approach 1:
The system combines the consumer EEG device with a computer system that presents visual and auditory stimuli. The computer generates precise time-stamps for each stimulus presentation and transmits these timing markers to the EEG device, merging the capabilities of both systems to achieve synchronized data capture that neither device could achieve alone.
Solution Approach 2:
The system implements a feedback loop where the computer system sends timing information and task parameters to the EEG device, and the EEG device transmits recorded data back to the computer for analysis. This bidirectional feedback ensures that stimuli timing and EEG recordings are precisely synchronized, compensating for the limitations of consumer-grade hardware.
Data Source
AI summary
According to an aspect, there is provided systems and methods for remote and longitudinal monitoring of electroencephalographic changes. The method includes remotely collecting electroencephalographic data from an automated session of neurocognitive tasks involving a presentation of audio and/or visual stimuli, time synchronizing the electroencephalographic data to the presentation of the stimuli, processing the electroencephalographic data using an automated pipeline to extract a plurality of features contained in the electroencephalographic data for a patient profile, and performing anomaly detection in the profile of the plurality of features contained in the electroencephalographic data. The feature is associated with a stimuli of the audio and/or visual stimuli and a metric from the electroencephalographic data. The patient profile comprises of a personal baseline.