Inner Speech EMG Training Loop for Phoneme Accuracy
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Solution Overview
Problem
Conventional noninvasive brain-computer interfaces (BCIs) using electromyography (EMG) electrodes face challenges in accurately detecting and decoding subaudible speech signals due to insufficient training data and inconsistent muscle activation patterns during inner speech, leading to inefficiencies and resource wastage.
Innovation Solution
An iterative training system that uses EMG electrodes to teach users to produce consistent sub-threshold muscle activations for inner speech, updating machine learning models in real-time to improve phoneme prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If EMG electrodes are used to detect subaudible speech signals, then device interaction without overt movements is enabled, but detection accuracy is insufficient due to inconsistent muscle activation patterns
Solution Approach 1:
The system provides real-time feedback to users about their muscle activation patterns during inner speech production. This feedback loop enables users to consciously adjust and refine their sub-threshold muscle activations, transforming an initially inconsistent physiological signal into a reliable, detectable pattern that maintains both ease of operation and measurement precision
Solution Approach 2:
Users actively participate in optimizing their own detectability by learning to produce consistent inner speech patterns through system guidance. The system serves itself by using user-generated data to improve its detection algorithms, creating a self-enhancing cycle where more users generate better training data that improves detection for all users
2Device complexity
If machine learning models are trained with limited data, then device complexity is reduced, but model accuracy deteriorates due to insufficient training data
Solution Approach 1:
The system serves dual purposes: it functions as both a practical inner speech detection tool and a self-training mechanism. The same system used for detection also collects and processes training data, eliminating the need for separate, extensive training phases and reducing overall device complexity while improving model accuracy through continuous learning
Solution Approach 2:
The system performs preliminary data collection and model training in the background before full deployment. By pre-processing and pre-training with available data upfront, the system reduces the computational burden during operation and ensures adequate model accuracy is achieved before the device is fully utilized
3Measurement precision
If iterative training is implemented to improve detection accuracy, then measurement precision is enhanced, but resource consumption increases due to continuous model updates
Solution Approach 1:
The system implements periodic model updates rather than continuous retraining. Training occurs at scheduled intervals or when sufficient new data is accumulated, allowing the system to maintain high detection accuracy while consuming computational resources only when necessary, thereby reducing overall energy loss
Solution Approach 2:
The system dynamically adjusts training parameters such as learning rate, batch size, and update frequency based on available resources and data quality. By changing these parameters adaptively, the system optimizes the balance between improving measurement precision and minimizing resource consumption during iterative training
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy of inner speech detection, enabling seamless device interaction without overt movements, reducing resource consumption and improving user efficiency.
Implementation Method 1
conventional noninvasive brain-computer interfaces (BCIs) using electromyography (EMG) electrodes
Data Source
AI summary
Methods and systems are disclosed for iteratively training a user and a ML model to produce accurate inner speech outputs. The methods and systems access a ML model and perform a first training iteration in which EMG data corresponding to inner speech is processed by the machine learning model to decode the EMG data into a set of predicted phonemes, phoneme sounds, words or phrases. The methods and systems present the set of predicted phonemes, phoneme sounds, words or phrases to the user and form a first set of training data comprising the set of predicted phonemes, phoneme sounds, words or phrases, the EMG data, and the set of specified phonemes, phoneme sounds, words or phrases as ground truth information. The methods and systems update parameters of the ML model based on the first set of training data prior to starting a second training iteration.