Audio Recommendation Model for Personalized Neural Stimulation
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Solution Overview
Problem
Existing treatments for cognitive diseases such as Alzheimer's and dementia are limited in efficacy and patient compliance due to the lack of personalized and non-invasive methods that effectively stimulate neural oscillations for cognitive enhancement.
Innovation Solution
A system that combines rhythmic light and audio stimulation with music to synchronize neural oscillations in delta, theta, and gamma frequency bands, using machine learning to select music and visual stimuli that mimic the brain's natural responses, enhancing neural entrainment and avoiding neural adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional treatments for cognitive diseases are used, then treatment coverage is provided, but treatment efficacy is limited and patient compliance is poor
Solution Approach 1:
The patent combines auditory stimulation (music) with visual stimulation (rhythmic light patterns) to create a multi-sensory neural stimulation system. This merging of multiple stimulation modalities enhances treatment efficacy by simultaneously engaging different neural pathways while maintaining patient engagement through varied sensory input.
Solution Approach 2:
The system dynamically adjusts stimulation parameters including audio frequency, visual light frequency, intensity, and timing based on real-time brain response monitoring. This allows optimization of neural oscillation entrainment to achieve therapeutic frequency bands (delta, theta, alpha, beta, gamma) while adapting to individual patient responses.
2Manufacturing precision
If generic audio stimulation is used, then stimulation is provided, but brain response variability is high and target frequency activation is inconsistent
Solution Approach 1:
The system incorporates real-time monitoring of brain responses through EEG or other neural sensing, using this feedback to dynamically adjust audio and visual stimulation parameters. This closed-loop control ensures precise activation of target frequency bands while adapting to individual patient neural responses and maintaining optimal entrainment.
Solution Approach 2:
The stimulation system transitions from static, pre-programmed audio tracks to dynamic, adaptive audio generation where parameters such as tempo, rhythm, and frequency are continuously adjusted based on real-time brain state monitoring. This enables precise targeting of specific frequency bands while adapting to changing neural responses during the treatment session.
3Strength
If strong onset music is used to produce strong brain responses, then neural stimulation intensity increases, but neural adaptation may occur reducing long-term efficacy
Solution Approach 1:
The system employs periodic variation in stimulation intensity and patterns, alternating between higher intensity phases for strong neural entrainment and lower intensity phases to prevent adaptation. The rhythmic structure of the music and light stimulation is designed to periodically modulate neural oscillations across different frequency bands, maintaining engagement over extended treatment durations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively treats, prevents, or protects against cognitive diseases by amplifying natural brain responses to music, improving treatment efficacy and patient compliance through enjoyable and engaging audiovisual stimuli.
Implementation Method 1
Neural oscillation occurs in humans and animals and includes rhythmic or repetitive neural activity in the central nervous system. Neural tissue can generate oscillatory activity by mechanisms within individual neurons or by interactions between neurons.
Implementation Method 2
The systems and methods described herein may apply one or more songs from the music library to a machine learning model trained to determine a predicted response. The systems and methods described herein may determine whether the predicted response for the song satisfies a criteria (e.g., a brain response at a target frequency or frequency range, and at or above a target amplitude).
Implementation Method 3
A system that combines rhythmic light and audio stimulation with music to synchronize neural oscillations in delta, theta, and gamma frequency bands, using machine learning to select music and visual stimuli that mimic the brain's natural responses, enhancing neural entrainment and avoiding neural adaptation.
Data Source
AI summary
A system comprises a memory and one or more processors. The memory stores weights for a machine learning model. The weights are trained on a training data of a training set. The training data includes input audio signals, patient attributes, and measured brain response signals. The one or more processors are configured to receive an audio signal, determine a predicted brain response signal for the audio signal by applying the audio signal to the machine learning model, generate a flag for the audio signal based on whether the predicted brain response signal satisfies one or more stimulation criterion.