Human-Machine Interface Using Gamma Signals for Real-Time Actuator Control
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Solution Overview
Problem
Current non-invasive brain-machine interfaces are limited by their low information transfer rate, primarily using slower rhythms like alpha and beta waves, which restrict their capability to control actuators with sub-second reaction times, and they often ignore extracranial signals like EMG, making them unsuitable for complex tasks and facing ethical and health concerns due to invasive alternatives.
Innovation Solution
A method utilizing non-invasive devices to acquire and process gamma frequency brain and muscle signals, amplifying and filtering these signals to extract spectral features, which are then used to control actuators with real-time feedback, enabling faster and more versatile control of a wider range of tasks without invasive techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If non-invasive brain-machine interfaces use slower rhythms like alpha and beta waves for signal acquisition, then the system maintains safety and avoids invasive procedures, but the information transfer rate is limited and sub-second reaction times cannot be achieved
Solution Approach 1:
The patent changes the frequency parameter of brain signals from traditional slow rhythms (alpha 8-12 Hz, beta 12-30 Hz) to fast gamma rhythms (30-200 Hz). This parameter change enables higher information transfer rates and sub-second reaction times while maintaining non-invasive safety, as gamma rhythms can be detected using the same EEG methodology but provide significantly more temporal resolution and control precision.
Solution Approach 2:
The patent introduces extracranial signals (EMG from muscles, EOG from eyes) as intermediary signals that complement brain signals. These intermediary signals provide additional control channels and enhance the information transfer rate, allowing the system to achieve high-speed control without invasive brain implantation. The intermediary signals act as mediators between the user's intent and the actuator control.
2Object-affected harmful factors
If non-invasive devices are used to acquire brain signals, then ethical and health concerns are avoided, but the capability to control complex tasks with multiple degrees of freedom is restricted
Solution Approach 1:
The patent merges multiple signal sources (brain gamma rhythms, muscle EMG, eye EOG) into a unified control system. This combination of signals provides redundant and complementary information channels, enabling complex task control with multiple degrees of freedom while maintaining non-invasive safety. The merged signal processing approach allows the system to decode intricate motor intentions that would be impossible with a single signal type.
Solution Approach 2:
The patent creates a universal control system that can handle diverse actuators and complex tasks through multi-functional signal processing. The system uses the same non-invasive sensing platform to detect multiple types of physiological signals (brain, muscle, eye) and translates them into control commands for various actuators, making the system adaptable to complex applications without requiring invasive modifications.
3Speed
If gamma frequency signals are used instead of slower rhythms, then real-time control with sub-second reaction times is achieved, but signal processing complexity increases
Solution Approach 1:
The patent replaces traditional mechanical signal processing approaches with advanced computational methods. Instead of using complex hardware filters and processors to handle high-frequency gamma signals, the system employs software-based spectral analysis (Fourier transforms, wavelet transforms) and machine learning algorithms to extract features from gamma rhythms. This substitution of computational processing for mechanical processing manages the complexity while enabling real-time control.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor gamma rhythm signals and adjust control commands in real-time. The feedback loop processes gamma frequency information and provides immediate corrective actions, enabling sub-second reaction times. The feedback system manages processing complexity by using adaptive algorithms that learn from ongoing signal patterns rather than requiring exhaustive real-time analysis of all signal parameters.
Data Source
Figure 1
AI summary
This invention relates to a method to control at least one actuator to carry out at least one task comprising repetitive sequences of steps, shorter than 1000 milliseconds by acquiring of endogenously generated electrical potentials within gamma frequency range generated by the brain and/or by the muscles of the body, head, or eyes of a human user, by acquiring electrical signals with sampling rate of at least 250 samples/s, by extracting the features of the signals related to high-frequency gamma waves between 30-200 Hz, by classifying, mapping and converting the signals into actions in real time, and by sending feedback and neurofeedback to a human user to enable him to control the interface. The invention further refers to a human-machine interface for applying the method. The invention further refers to a machine computing unit configured to carry out part of the steps of the method. The invention further refers to computer programs comprising instructions for applying the method.