Brain-Machine Interface Neural Signal Decoding Accuracy
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Solution Overview
Problem
Existing brain-machine interface (BMI) systems face limitations in accuracy and latency when decoding neural signals into control signals, particularly due to their reliance on endogenous event-related potentials and biofeedback mechanisms, which are binary, inaccurate, and dependent on user learning.
Innovation Solution
A brain-machine interface system that utilizes sensors to sample neural signals, transforms them into a common representational space, and employs an Actor recurrent neural network policy with a deep recurrent neural network and generative sequence decoder to predict control signals, while also determining intrinsic biometric-based rewards for online reinforcement learning, enabling more accurate and responsive control of target devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If endogenous event-related potentials are used as control signals, then control signal capacity increases, but accuracy decreases
Solution Approach 1:
The patent segments the decoding process into multiple independent components: feature extraction from neural signals, sequence generation through RNN, and control signal derivation. This segmentation allows each component to be optimized independently, maintaining accuracy while increasing control capacity through multi-degree-of-freedom signals.
Solution Approach 2:
The patent transitions from binary control signals (0 or 1) to continuous multi-dimensional control signals representing multiple degrees of freedom. This dimensional expansion allows the system to convey richer control information while maintaining accuracy through the use of recurrent neural networks that process temporal sequences of neural features.
2Ease of operation
If biofeedback mechanisms are used, then user learning capability is enhanced, but latency increases to super high levels
Solution Approach 1:
The system performs preliminary processing of neural signals by continuously extracting features and maintaining a recurrent state that encodes temporal context. This preliminary action allows the system to be ready to generate control signals immediately when needed, reducing latency while still providing feedback for user learning.
Solution Approach 2:
The patent replaces traditional mechanical biofeedback loops with a computational recurrent neural network that processes neural signals. This substitution eliminates the delays inherent in traditional feedback mechanisms while preserving the learning capability through continuous computational modeling of neural patterns.
3Device complexity
If traditional decoders are used, then system simplicity is maintained, but accuracy and responsiveness deteriorate
Solution Approach 1:
The patent introduces a recurrent neural network as an intermediary between neural signal acquisition and control signal generation. This intermediary component processes temporal sequences of neural features and generates accurate control signals, bridging the gap between simple signal acquisition and complex control requirements while maintaining system interpretability.
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AI summary
A brain-machine interface system configured to decode neural signals to control a target device includes a sensor to sample the neural signals, and a computer-readable storage medium having software instructions, which, when executed by a processor, cause the processor to transform the neural signals into a common representational space stored in the system, provide the common representational space as a state representation to inform an Actor recurrent neural network policy of the system, generate and evaluate, utilizing a deep recurrent neural network of the system having a generative sequence decoder, predictive sequences of control signals, supply a control signal to the target device to achieve an output of the target device, determine an intrinsic biometric-based reward signal, from the common representational space, based on an expectation of the output of the target device, and supply the intrinsic biometric-based reward signal to a Critic model of the system.