Neural Interface Tensor Regression for Signal Noise Reduction
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Solution Overview
Problem
Existing direct neural interface systems face inefficiencies due to noise from background brain activity and muscular artifacts, which generate spurious command signals and pollute data sets, limiting the effective use of information from neuronal signals.
Innovation Solution
A direct neural interface system that acquires electrophysiological signals and represents them as N-way data tensors, using Generalized Linear regression with nonlinear link functions or Generalized Additive Linear regression to generate command signals, with outlier detection and correction, effectively addressing noise-induced issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional BCI methods use a limited number of features extracted from EEG or ECoG signals, then the system complexity is reduced, but the information utilization efficiency deteriorates
Solution Approach 1:
The patent transitions from conventional feature extraction methods to tensor-based multi-way regression, adding dimensional structure (time, frequency, space) to the data representation. This allows the system to utilize the full information content of ECoG signals without proportionally increasing system complexity, as the tensor framework provides a structured approach to handling high-dimensional data.
Solution Approach 2:
The patent changes the fundamental parameters of signal processing by moving from hand-crafted feature extraction to data-driven tensor decomposition. This parameter change enables the system to automatically learn relevant features from the data while maintaining computational tractability through the structured tensor approach.
2Measurement precision
If regression models are applied to neuronal signals to generate command signals, then the accuracy of command signal generation is improved, but noise-induced errors from background brain activity and muscular artifacts increase
Solution Approach 1:
The patent extracts and separates signal components by decomposing the ECoG signals into distinct tensor modes corresponding to different sources (brain activity vs. artifacts). This extraction approach allows the system to isolate and eliminate noise components while preserving the useful neural information, thereby improving measurement precision without being degraded by harmful factors.
Solution Approach 2:
The patent introduces tensor decomposition as an intermediary processing step between signal acquisition and command generation. This intermediary transforms the raw signals into a structured representation where noise and useful information are separated, allowing for selective processing that improves accuracy while filtering out artifacts.
3Loss of information
If multi-way regression is applied to neuronal signals represented as three-way tensors, then the information utilization efficiency is improved, but the device complexity increases
Solution Approach 1:
The patent segments the complex processing task into distinct tensor operations corresponding to different signal dimensions (time, frequency, space). This segmentation allows the system to process information efficiently by operating on structured data blocks, improving information utilization while managing complexity through modular processing steps.
Data Source
AI summary
A direct neural interface system comprises: a signal acquisition subsystem for acquiring electrophysiological signals representative of neuronal activity of a subject's brain; and a processing unit for representing electrophysiological signals acquired over an observation time window in the form of a N-way data tensor, N being greater than or equal to two, and generating command signals for a machine by applying a regression model over the data tensor; wherein the processing unit is configured or programmed for generating command signals for a machine by applying Generalized Linear regression, with a nonlinear link function, over the data tensor. A method of interfacing a subject's brain to a machine by using such a direct neural interface system is provided.