Wearable Collar EMG Training for Accurate Inner Speech Detection
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Solution Overview
Problem
Conventional EMG systems for detecting silent speech face challenges in accuracy due to external interference and individual differences, requiring lengthy training and often leading to unintended muscle movements and resource wastage.
Innovation Solution
A system that trains users to produce inner speech accurately by monitoring EMG data and providing feedback on a display device, using a wearable collar with electrodes to detect and indicate inner speech, reducing noise and improving detection precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional EMG systems are used to detect silent speech, then speech detection capability is provided, but accuracy deteriorates due to external interference and individual differences
Solution Approach 1:
The system segments the speech detection process into multiple stages: initial EMG signal collection during training, processing to extract speech patterns, and separate execution phase. This segmentation allows the system to train on controlled data and then execute detection with reduced sensitivity to external interference.
Solution Approach 2:
The system performs preliminary training action before actual speech detection. During training, the system collects EMG signals and establishes individual speech patterns. This preliminary action creates a reference model that improves subsequent detection accuracy by compensating for individual differences and reducing sensitivity to external interference.
2Measurement precision
If training is provided to improve detection accuracy, then measurement precision improves, but time consumption increases due to lengthy training required
Solution Approach 1:
The system implements partial training by collecting EMG signals for a limited set of representative words or phrases rather than exhaustive training data. This partial action approach achieves sufficient accuracy for practical use while significantly reducing training time compared to comprehensive training methods.
Solution Approach 2:
The system performs self-calibration by automatically adapting to individual speech patterns during the training phase without requiring manual configuration or extensive user input. This self-service approach streamlines the training process and reduces the time burden on users.
3Reliability
If EMG monitoring is continuously performed to detect inner speech, then detection capability is maintained, but resource consumption increases leading to wastage
Solution Approach 1:
The system performs EMG monitoring periodically rather than continuously. Speech detection is activated at specific intervals or triggered by contextual cues, maintaining detection reliability for critical moments while reducing overall resource consumption during non-speech periods.
Solution Approach 2:
The system uses feedback mechanisms to adjust monitoring intensity based on detected speech patterns and contextual information. When speech is detected or suspected, monitoring intensity increases; otherwise, it reduces to lower levels, optimizing the balance between detection reliability and resource consumption.
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 and efficiency of silent speech detection, allowing seamless interaction with electronic devices without overt physical movements, reducing resource wastage and improving user experience.
Implementation Method 1
Other noninvasive computer interfaces leverage electromyograph (EMG) electrodes, which detect electrical signals associated with muscle activity.
Implementation Method 2
Speech-related EMG signals can be measured in various locations across the face and neck, including on the side of a subject's throat, near the larynx, and under the chin. Of particular interest to BCI is the use of surface EMG to discriminate and recognize subaudible speech signals produced with relatively little or no acoustic input.
Data Source
AI summary
Methods and systems are disclosed for training users to produce inner speech. The system presents a prompt on a display device with one or more instructions for a user to produce inner speech and, in response to presenting the prompt, monitors EMG data to detect existence of inner speech. The system, in response to detecting existence of the inner speech, presents on the display device an indication that inner speech has been detected in the EMG data.