EEG Brain-Computer Interface With Riemannian Signal Classification

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Solution Overview

Problem

Individuals with severe motor disabilities or diseases are unable to use their motor skills or other essential abilities, leading to dependence on others for mobility, and there is a need for a system that can interpret their intentions and command devices like a joystick, keyboard, mouse, motor capabilities, tongue, mouthpiece etc. A BCI that can translate brain signals into executable commands for smart devices.

Innovation Solution

A brain computer interface (BCI) using Riemannian Geometry based on EEG based EEG system that integrates EEG based EEG system with embedded robot operating system that translates ERP in electroencephalograph wave forms into commands executable by an assistance device(s) or system(s) comprising a user interface coupled to an electroencephalograph (EEG) decoder that is configured to receive and analyze EEG wave forms and produce a command signal.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual or electric wheelchairs are used for mobility assistance, then individuals with motor disabilities can move around, but they remain dependent on others for control and operation

Engineering Contradiction:

Improveindependence in mobility controlVSAvoidsystem complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The patent replaces manual mechanical control (joysticks, buttons) and basic electric control with a brain-computer interface that directly translates neural signals into wheelchair control commands. This substitution eliminates the need for intermediate mechanical or manual control mechanisms, enabling direct thought-to-motion control while maintaining system functionality.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces an EEG-based brain-computer interface as an intermediary between the user's neural intentions and the wheelchair's motor system. This intermediary captures brain waves, processes them through algorithms, and converts them into executable control commands, bridging the gap between neural activity and mechanical action without requiring manual intervention.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Extent of automation

If conventional BCI systems are used to translate brain signals into commands, then control capability is provided, but the system complexity and computational requirements increase significantly

Engineering Contradiction:

Improvebrain signal to command translationVSAvoidcomputational complexity

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The patent extracts and processes only the essential features from raw EEG signals—specifically focusing on event-related potentials and spectral characteristics—rather than attempting to analyze the entire complex signal set. This extraction approach isolates the most informative components for control while discarding redundant information, reducing computational burden.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent segments the brain signal processing into distinct functional modules: signal acquisition from EEG sensors, preprocessing to remove artifacts, feature extraction to identify relevant patterns, classification to interpret intentions, and command generation to translate into control signals. This modular segmentation allows each component to be optimized independently and reduces overall system complexity.

Inventive Principle:

Principle #1Segmentation

3Speed

If EEG signals are processed in real-time for immediate control response, then responsiveness is improved, but processing time and computational load increase

Engineering Contradiction:

Improvecontrol response timeVSAvoidsignal processing time

Core Design Contradiction:

SpeedVSLoss of time

Solution Approach 1:

The patent performs preliminary processing of EEG signals by continuously monitoring and pre-processing the raw data stream, preparing features and patterns in advance before control decisions are needed. This allows the system to have pre-computed features ready for rapid classification when control actions are required, reducing the critical processing time during actual control moments.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements continuous EEG signal processing where the system constantly analyzes brain waves, maintains running statistics, and keeps the control pipeline active rather than processing signals in discrete batches. This continuous operation eliminates idle processing time and ensures that the system is always ready to translate neural intentions into control commands with minimal delay.

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS20250348146A1Integrated smart system controllable by asynchronous EEG based braincomputer interface using riemannian geometry using embedded robotoperating system

Publication Date: 2025.11.13 BOARD OF RGT THE UNIV OF TEXAS SYST

AI summary

The invention discloses an integrated non-intrusive, safe and user-friendly electroencephalography (EEG) system capable of classifying signals generated from both Event Related Potential (ERP) based steady-state visually evoked potential (SSVEP) and pure cognition, leveraging Riemannian Geometry-based signal classification algorithms for precise command generation. The system seamlessly combines SSVEP-based visual stimuli with cognition-based EEG signals to provide a comprehensive interface for brain-computer interaction (BCI) applications. Riemannian Geometry techniques are employed for robust signal classification and efficient command generation, enhancing the system's accuracy and reliability.