Feedback-Controlled TILS System for Brain Stimulation
Solution Overview
Problem
Current transcranial infrared laser stimulation (TILS) methods lack a feedback control mechanism to optimize the dosage, timing, and location of light sources for enhanced brain stimulation.
Innovation Solution
A system and method that incorporate a feedback loop control system using a processor to analyze signals from detectors, such as EEG or fMRI, to adjust the light source parameters, including dosage, timing, and location, to achieve optimal brain function enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transcranial infrared laser stimulation is applied to enhance brain function, then cognitive and memory functions are improved, but the dosage and timing cannot be optimized without feedback control
Solution Approach 1:
The patent implements a feedback control mechanism where a processor receives signals from detectors (EEG, fMRI, or NIRS), analyzes brain activity data, and automatically adjusts light source parameters (dosage, timing, location) to optimize photobiomodulation effects. This closed-loop system enables real-time optimization of brain stimulation while maintaining manageable system complexity through automated control algorithms.
2Manufacturing precision
If feedback control mechanism is added to optimize light source parameters, then stimulation precision is improved, but device complexity increases
Solution Approach 1:
The system incorporates detectors that monitor brain activity and a processor that analyzes this data to automatically adjust light source parameters. This feedback loop enables precise control of dosage, timing, and location of photobiomodulation stimulation, optimizing therapeutic effects while the automated nature of the control keeps the system manageable.
Solution Approach 2:
The system performs self-adjustment by automatically analyzing brain activity signals and modifying light source parameters without requiring manual intervention. The processor autonomously determines optimal stimulation parameters based on real-time brain state monitoring, reducing the need for complex external control mechanisms.
3Productivity
If real-time signal analysis is performed to adjust stimulation parameters, then treatment effectiveness is improved, but processing time and complexity increase
Solution Approach 1:
The system performs preliminary analysis of brain activity signals to predict optimal stimulation parameters before delivering the actual photobiomodulation treatment. By pre-processing and analyzing detector signals in advance, the system can quickly adjust light source parameters without significant delay, improving treatment effectiveness while minimizing processing time loss.
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 feedback-controlled TILS system effectively enhances cognitive and memory functions in middle-aged and older adults by optimizing the stimulation parameters, leading to improved cerebral oxygenation and mitochondrial respiration.
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
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 method for measuring cell metabolism through photobiomodulation comprising steps of receiving a broadband near-infrared spectroscopy (bbNIRS) image including one or more responding signals to light stimulation regarding a specific cell; extracting two or more turning points on one of the responding signals; extracting two or more threshold points on one of the responding signals; generating a first line by curve fitting to approximate the turning points on the responding signal and determining a slop of the first line; generating a second line by curve fitting to approximate the threshold points on the responding signal and determining a slop of the second line; and comparing the input images, the first line and the second line to determine a rate of cell metabolism of the specific cell.