Brain-Machine Interface Neuromodulation for Decoder Stability
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Solution Overview
Problem
Current brain-machine interfaces are limited in their ability to enhance neural control through neuromodulation, primarily focusing on peripheral improvements and requiring extensive data for accurate and sustained operation, with a lack of research in using neuromodulation to directly control machines from brain signals.
Innovation Solution
An enhanced brain-machine interface that utilizes neuromodulation, specifically spatiotemporal amplitude-modulated patterns (STAMPs) of stimulation, to condition and decode neural signals, enabling direct control of devices like prosthetic limbs by reinforcing neural dynamics during training and use, thereby improving stability and reducing the need for extensive calibration and training time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neuromodulation is used to enhance neural control, then the stability and control accuracy are improved, but the device complexity and research requirements increase
Solution Approach 1:
The patent combines neuromodulation stimulation delivery with brain signal acquisition and machine control functions into a single integrated neural interface device. The neural interface includes sensors for collecting brain signals, processors for analyzing signals and controlling devices, and stimulation components for delivering neuromodulation, thereby reducing the need for separate external devices and simplifying the overall system architecture.
Solution Approach 2:
The neural interface device is designed to perform multiple functions: acquiring neural signals, processing and decoding brain activity, delivering neuromodulation stimulation, and controlling external devices. This multi-functional approach consolidates what would traditionally require separate systems into a single platform, reducing overall system complexity while enhancing reliability.
2Reliability
If extensive data collection is used to train decoders, then the accuracy and duration of neural control are improved, but the training time and data requirements increase
Solution Approach 1:
The system performs preliminary neuromodulation stimulation to prime and condition neural circuits before actual control tasks begin. This preliminary action enhances the quality and clarity of neural signals, allowing decoders to achieve high accuracy with reduced training data and shorter training periods compared to conventional approaches.
Solution Approach 2:
The system implements closed-loop feedback where neural signals are continuously monitored, decoded, and used to control devices while simultaneously delivering neuromodulation feedback to reinforce desired neural patterns. This feedback mechanism accelerates learning and reduces training time by providing real-time guidance and reinforcement during the training process.
3Productivity
If peripheral stimulation is used to improve response, then the electromyographic response is enhanced, but the ability to directly control machines from brain signals is limited
Solution Approach 1:
Instead of stimulating peripheral tissues to improve response (as in conventional electromyography approaches), the system inverts the approach by directly stimulating the brain to enhance neural signals. This inversion enables more direct control of machines from brain activity, as the neuromodulation is applied at the source (brain) rather than at the periphery (muscles), thereby improving both productivity and ease of 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 solution enables high-degree-of-freedom output system control, enhances the stability of neural interfaces for extended use, and decreases training time, allowing for more intuitive and efficient control of devices, such as prosthetic limbs, by directly addressing key limitations in existing neural interfaces.
Implementation Method 1
the neural device is configured to administer neuromodulation stimulation
Implementation Method 2
one or more sensors for collecting signals of interest
Data Source
AI summary
Described is an improved brain-machine interface including a neural interface and a controllable device in communication with the neural interface. The neural interface includes a neural device with one or more sensors for collecting signals of interest and one or more processors for conditioning the signals of interest, extracting salient neural features from and decoding the conditioned signals of interest, and generating a control command for the controllable device. The controllable device performs one or more operations according to the control command, and the neural device administers neuromodulation stimulation to reinforce operation of the controllable device.