Inner Speech EMG Feedback Loop for Accurate Silent Decoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional EMG systems for detecting silent speech face challenges due to insufficient training data and inaccurate decoding of inner speech, leading to inefficient and resource-wasting user experiences.
Innovation Solution
An iterative training system that enhances user proficiency in producing consistent sub-threshold muscle activation patterns through real-time feedback from a machine learning model, improving EMG signal accuracy and enabling seamless device interaction without overt movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional EMG systems are used for detecting silent speech, then the system can detect muscle activity, but the decoding accuracy of inner speech is insufficient and training data is inadequate
Solution Approach 1:
The system implements an iterative training loop where the machine learning model provides real-time feedback to the user about their inner speech production quality. The user produces inner speech, the system decodes it, and then feeds back the decoded result to guide the user's next production attempt. This closed-loop feedback mechanism enables progressive improvement of both user proficiency and model accuracy without requiring large external training datasets.
Solution Approach 2:
The system uses the user's own inner speech productions as training data through the iterative feedback process. Rather than relying on pre-collected training datasets from multiple users, each user's practice sessions generate their own personalized training data, allowing the model to adapt and improve continuously during interaction.
2Measurement precision
If users produce inner speech for training, then the system can improve decoding accuracy, but the process consumes computational resources and time
Solution Approach 1:
The system implements a progressive training approach where the full training process is distributed across multiple short interaction sessions. Rather than requiring extensive continuous training, the system accumulates improvements through many brief, low-resource sessions, making the overall process more efficient and less burdensome on user time and system resources.
3Ease of operation
If the system requires overt movements for communication, then input accuracy can be ensured, but user convenience and device interaction efficiency are reduced
Solution Approach 1:
The system replaces traditional mechanical input methods (voice speech requiring muscle movement, keyboard typing) with detection of subtle electromyographic signals from minimal facial muscle activity. By using EMG sensors to detect electrical signals from very slight muscle contractions associated with inner speech, the system enables communication without the overt mechanical movements required by conventional interfaces.
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
Enables efficient and accurate detection of inner speech, reducing resource consumption and enhancing user interaction efficiency with electronic devices.
Implementation Method 1
Certain systems use EMG electrodes to detect silent speech of users. The silent speech can then be used in these systems to perform various operations.
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.