Biological Interface System Signal Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current biological interface systems face challenges in obtaining stable and specific electrical signals for controlling devices, particularly for patients with neurological diseases like amyotrophic lateral sclerosis, due to signal degradation over time and limited resolution, which hinders effective control of devices such as prostheses and communication tools.
Innovation Solution
A biological interface system comprising a sensor with multiple electrodes to detect multicellular signals and a processing unit that generates processed signals, integrated with a patient training apparatus to optimize control by correlating signal data with movement, allowing for improved control of devices through feedback mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Strength
If neural interface systems utilize signals from large groups of neurons (ECoG, LFP, EEG), then the system can obtain sufficient signal amplitude, but the specificity and resolution of the control signal is limited
Solution Approach 1:
The patent combines multiple signal sources (individual neuron spikes and multicellular signals like LFP/EEG) into a hybrid control system. The sensor array captures both high-resolution single-neuron signals and high-amplitude multicellular signals, merging them to achieve both specificity and strength in control signals.
Solution Approach 2:
The patent employs a composite sensing approach that integrates multiple electrode types and signal processing methods. The sensor comprises both high-impedance electrodes for single-neuron detection and lower-impedance electrodes for multicellular signal detection, creating a composite sensing system that leverages the strengths of each approach.
2Measurement precision
If neural interface systems utilize signals directly from individual neurons, then the control signal specificity and resolution is improved, but the signal amplitude is insufficient and stability degrades over time
Solution Approach 1:
The patent combines multiple signal sources (individual neuron spikes and multicellular signals like LFP/EEG) into a hybrid control system. The sensor array captures both high-resolution single-neuron signals and high-amplitude multicellular signals, merging them to achieve both specificity and strength in control signals.
3Measurement precision
If neural interface systems utilize signals directly from individual neurons, then the control signal specificity is improved, but the signal stability degrades over time due to neural changes
Solution Approach 1:
The patent implements dynamic adaptation mechanisms where the system continuously adjusts to changing neural signals over time. The processing unit adapts the control signal generation based on evolving neural patterns, allowing the system to maintain stability despite neural plasticity and signal drift.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system monitors the detected neural signals and adjusts its processing parameters accordingly. This closed-loop approach allows the system to compensate for signal degradation and maintain stable control performance over extended periods.
4Reliability
If sophisticated biological interface systems include numerous safety features and configuration routines, then the reliability is improved, but the device complexity increases
Solution Approach 1:
The patent implements preliminary configuration routines and patient training procedures that are performed before full system operation. These preliminary actions establish safe operating parameters and calibrate the system in advance, reducing the need for complex real-time adjustments and simplifying ongoing operation.
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 enhances the ability of patients to control devices by stabilizing and correlating multicellular signals with movement data, providing reliable and adaptive control, thus improving the functionality and usability of biological interface systems.
Implementation Method 1
a sensor comprising a plurality of electrodes for detecting multicellular signals emanating from one or more living cells of a patient
Data Source
AI summary
Various embodiments of a biological interface system and related methods are disclosed. The system may include a sensor having a plurality of electrodes for detecting multicellular signals emanating from one or more living cells of a patient, and a processing unit configured to receive the multicellular signals from the sensor and process the multicellular signals to produce a processed signal. The processing unit may be configured to transmit the processed signal to a controlled device that is configured to receive the processed signal. The system may also include a patient training apparatus configured to receive a patient training signal that causes the patient training apparatus to controllably move one or more joints of the patient. The system may be configured to perform an integrated patient training routine to produce the patient training signal, to store a set of multicellular signal data detected during a movement of the one or more joints, and to correlate the set of multicellular signal data to a second set of data related to the movement of the one or more joints.