Transcranial Infrared Laser Stimulation Feedback Control
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Solution Overview
Problem
Current transcranial infrared laser stimulation (TILS) lacks a feedback control mechanism to optimize the dosage, timing, and location of light source for enhanced brain stimulation, which is necessary for improving cognitive and memory functions, particularly in middle-aged and older adults at risk for cognitive decline.
Innovation Solution
A system incorporating a light source, controller, signal detecting unit, and processor that uses Fourier Transform and machine learning algorithms to adjust the dosage, timing, and location of brain stimulation based on detected signals from EEG, fMRI, and BOLD data, ensuring optimal results through a feedback loop control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transcranial infrared laser stimulation is applied without feedback control, then the system is simple and easy to operate, but the dosage, timing and location of stimulation cannot be optimized for enhanced brain function
Solution Approach 1:
The patent implements a feedback control mechanism where a processor receives signals from a signal detecting unit, analyzes them using Fourier Transform and machine learning algorithms, and generates feedback signals to the controller to adjust the light source parameters. This closed-loop system optimizes dosage, timing and location of brain stimulation dynamically, resolving the contradiction between reliability and complexity by introducing intelligent control.
2Measurement precision
If Fourier Transform and machine learning algorithms are implemented for signal analysis, then the precision of brain status detection is improved, but the processing time and computational complexity increase
Solution Approach 1:
The patent applies Fourier Transform with predetermined time windows and pre-defined frequency bands, and uses machine learning algorithms that have been trained in advance to recognize brain states. This preliminary preparation of analysis frameworks allows rapid processing of incoming signals without sacrificing detection precision, resolving the time-precision tradeoff.
3Manufacturing precision
If the light source parameters are continuously adjusted based on feedback, then the optimal stimulation dosage is achieved, but the system requires more complex control mechanisms
Solution Approach 1:
The system uses machine learning algorithms that automatically learn optimal control strategies from training data and autonomously generate feedback signals for light source adjustment. This self-learning capability reduces the need for complex manual control mechanisms while achieving precise stimulation dosage, resolving the contradiction between precision and control complexity.
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 enhances cognitive functions by improving cerebral oxygenation and mitochondrial respiration, leading to significant improvements in reaction time, memory, and attention in older adults, with adjustments made to achieve peak hormetic doses and prevent tissue damage.
Implementation Method 1
Photobiomodulation involves the absorption of photons and the subsequent modulation of metabolic processes in cells, including neurons. For red to near-infrared light, the major intracellular molecule absorbing photons is cytochrome c oxidase (CCO), a mitochondrial respiratory enzyme that can be upregulated in vitro and in vivo.
Implementation Method 2
the detected signal is processed through Fourier Transform (FT) with at least one predetermined time window, which is divided by the value of the detected signal transformed by FT and integrated for a predetermined bandwidth to generate a normalized FT signals
Data Source
AI summary
In one aspect, a system for in vivo and transcranial stimulation of brain tissue of a subject may include at least one light source, a controller to control operation of the light source, a signal detecting unit and a processor configured to receive signals from the signal detecting unit, analyze the signals and generate a feedback signal to the controller to control the light source until optimal results are obtained. In one embodiment, the light source is a laser instrument and the wavelength can range from 800 to 1100 nm. In another embodiment, the irradiance of the laser instrument can range from 50 to 1000 mW/cm2.