Low-Power Neuromodulation via In-Ear EEG and Deep Learning
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Solution Overview
Problem
Conventional sleep studies require large and costly equipment, limiting their accessibility and comfort for users, and hindering advancements in neuroscience and sleep research.
Innovation Solution
A low-power neuromodulation system that uses in-ear electrodes to record EEG signals, integrated circuits for noise reduction, and a deep learning model for real-time sleep stage classification, enabling closed-loop on-device sleep modulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sleep study equipment is used, then measurement precision is improved, but device complexity and cost increase substantially
Solution Approach 1:
The patent extracts the essential sleep stage classification function from complex conventional equipment and implements it using only a single EEG channel processed through a lightweight deep learning model. This extraction approach maintains measurement precision while eliminating unnecessary complexity and cost associated with traditional polysomnography equipment.
Solution Approach 2:
The patent employs inexpensive, disposable in-ear electrodes instead of expensive, reusable clinical electrodes. These simple electrodes provide sufficient signal quality for sleep stage classification while dramatically reducing equipment cost and complexity, making the system accessible for home use.
2Measurement precision
If conventional EEG recording equipment is used, then measurement precision is improved, but ease of operation deteriorates due to discomfort and lab requirements
Solution Approach 1:
The system performs self-service by automatically classifying sleep stages using the deep learning model without requiring trained technicians or lab personnel. The automated processing and classification enable users to conduct sleep studies independently at home, greatly improving ease of operation while maintaining measurement precision through the optimized single-channel EEG approach.
3Measurement precision
If deep learning models with long kernels are used, then measurement precision is improved, but use of energy increases substantially
Solution Approach 1:
The patent applies partial action by using a simplified deep learning model that processes only the essential single-channel EEG signal rather than full polysomnography data. This partial processing approach achieves sufficient measurement precision for sleep stage classification while dramatically reducing computational load and power consumption, making the system feasible for battery-powered wearable devices.
Data Source
AI summary
There is provided a low-power neuromodulation system and a method for low-power neuromodulation. The system including a controller to receive electroencephalogram (EEG) signals from one or more pairs of electrodes, the controller including a processor, memory, integrated circuitry, field programmable gate array, or a combination thereof, to execute: an analog front-end (AFE) module to digitize and amplify the received EEG signals; a processing module to classify sleep stages using a deep learning model, the deep learning model taking the digitized and amplified EEG signals as input, the deep learning model including representation learning to capture time-invariant information from the input, sequential learning to capture the sleep stage transition using features encoded in the representation learning, and a dense network to generate a prediction for the sleep stages using the captured time-invariant information and the captured sleep stage transitions; and an output module to output the classification of sleep stages.