Low-Channel EEG-ECG Vehicle Control for Inner Speech Recognition
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Solution Overview
Problem
Existing technologies for individuals with disabilities, such as ALS, cerebral palsy, or speech disorders, lack a low-cost, high-accuracy solution for converting brain or heart signals into commands for transportation independence.
Innovation Solution
A method using low-channel ECG and EEG sensors to collect data, preprocess it with filters and calibration, and classify features using supervised learning algorithms like SVM for internalized speech recognition, enabling control of vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-channel EEG headsets are used for brain signal classification, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts and utilizes specific characteristic frequency bands (alpha, beta, gamma waves) from EEG signals rather than processing all frequency ranges across multiple channels. This selective extraction maintains classification accuracy while reducing the effective complexity of signal processing requirements.
Solution Approach 2:
The patent employs low-cost, disposable electrodes and simplified EEG headsets with fewer channels, replacing expensive, high-channel professional equipment. The system achieves viable classification accuracy using minimal sensor arrays, making the technology accessible and cost-effective.
2Measurement precision
If multimodal physiological signals (EEG, EMG, GSR, BVP, PPG, ECG) are combined for affective computing, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent develops a unified machine learning framework that can process multiple types of physiological signals (EEG, ECG, EMG, GSR, BVP, PPG) through a single integrated system. This universal approach allows the same classification algorithms to handle diverse signal types, reducing overall system complexity while maintaining high measurement precision through multimodal data fusion.
3Device complexity
If ECG signals are used for inner speech recognition, then device complexity is reduced, but measurement precision may be compromised
Solution Approach 1:
The patent transforms ECG signals by extracting specific temporal and spectral parameters that are characteristic of inner speech patterns. By changing the representation parameters of the ECG signal (focusing on specific frequency components and temporal features), the system achieves accurate inner speech classification using only a single physiological modality.
Solution Approach 2:
The patent replaces complex mechanical sensor systems (multiple EEG channels, various physiological sensors) with a simplified electrical signal processing approach using ECG. The machine learning algorithms substitute for complex hardware configurations, achieving comparable or sufficient accuracy through sophisticated software-based signal interpretation.
Data Source
AI summary
The present disclosure relates to methods and apparatuses for classifying internalized speech. In particular, disclosed herein is a method for interpreting electrocardiogram (ECR) and electroencephalogram (EEG) signals in an individual using electrodes placed on the individual's skin. The method disclosed herein may be performed using a low-cost, low-channel ECG apparatus, such as by placing three sensors on the individual's skin and which may be wearable and portable to facilitate its use and with an eight-channel EEG apparatus, such as by placing sensors on the individual's head. The sensors may be placed on the left and right sides of the individual's forehead and on the left side below the individual's neck to collect ECG signals, although other placements are possible. Autoregressive coefficient (AR), Shannon entropy, fractal measures, and multiscale wavelet variance estimation may then be applied to the collected signals to determine the individual's internalized speech.