Single-Channel EEG Control for Wanting-Driven Haptic Stimulation
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Solution Overview
Problem
Existing EEG systems struggle to accurately capture and process the 'wanting' state of brain signals, which is crucial for emotion detection, particularly in single-channel scenarios, due to interference from artifacts like eye-blinks and beta waves, and lack effective methods to predict and control haptic stimulators based on these signals.
Innovation Solution
A single-channel EEG system with an adaptive multiscale dispersion entropy algorithm and subspace-based processing is used to remove artifacts like eye-blinks and predict the 'wanting' state, generating a command signal for a haptic caressing stimulator to enhance pleasure by adjusting stimulation intensity based on real-time brain activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single-channel EEG system is used to capture brain signals, then device complexity is reduced and ease of operation is improved, but measurement precision deteriorates due to interference from artifacts like eye-blinks and beta waves
Solution Approach 1:
The patent extracts and removes harmful artifacts (eye-blinks, beta waves) from the single-channel EEG signal through signal processing techniques. Specifically, it uses algorithms to identify and eliminate these interfering components while preserving the underlying brain activity related to the wanting state, thereby maintaining measurement precision despite using a simple single-channel system
Solution Approach 2:
The patent introduces an intermediary processing stage between signal acquisition and interpretation. It uses adaptive filtering and machine learning models as intermediaries to separate the desired brain signals from artifacts, allowing the simple single-channel system to achieve accurate measurement through sophisticated intermediate processing
2Measurement precision
If artifact removal and prediction algorithms are added to the single-channel EEG system, then measurement precision is improved, but device complexity increases due to processing requirements
Solution Approach 1:
The patent implements self-service through adaptive algorithms that automatically adjust to the user's brain activity patterns. The system performs self-calibration and continuous adaptation without requiring manual intervention or complex external processing equipment, achieving high precision through intelligent autonomous processing
Solution Approach 2:
The patent dynamically changes processing parameters based on the detected brain state. It adapts filtering characteristics, prediction model parameters, and analysis thresholds in real-time according to the user's physiological state, allowing the system to maintain high precision across different conditions without requiring multiple fixed complex processing paths
3Measurement precision
If real-time prediction and smoothing of brain state signals is implemented, then control precision for haptic stimulator is improved, but loss of time increases due to processing delays
Solution Approach 1:
The patent performs preliminary action by pre-computing prediction models and smoothing parameters during idle periods or using previously collected data. It prepares prediction algorithms in advance so that when real-time processing is needed, the system can apply pre-optimized parameters with minimal computational delay, reducing time loss while maintaining precision
Solution Approach 2:
The patent uses periodic action by updating predictions and smoothing at optimized intervals rather than continuously. It processes brain signals at strategically chosen time points when sufficient data has accumulated for accurate prediction, balancing real-time responsiveness with computational efficiency to minimize time loss while maintaining control precision
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
The system effectively captures and predicts the 'wanting' state of brain signals, enabling a haptic stimulator to enhance pleasure by adjusting stimulation intensity without physical feedback, providing a user-friendly and accurate emotion-driven control.
Implementation Method 1
measuring of 'wanting' state of the brain signals
Data Source
AI summary
A system is disclosed including a headset with one single multi-pin dry electrode and an earlobe electrode recording EEG, a signal processing unit including lowpass filter, battery, amplifier, A/D convertor, and Bluetooth transmitter. The system further includes a signal processing software on a smart phone, wirelessly receiving and processing the EEG readings and estimating a user's sexual wanting emotion, sensed from the user's scalp Cz zone; replacing eye-blink peak anomalies with prediction of EEG signal samples; generating continuous command signals, wirelessly transmitting the signals, converting signals to analogue signals, applying signals to a DC motor coupled to a haptic caressing body stimulator, actuating vibration, circular and/or thrusting movement to control the stimulator by following the level of the brain wanting state based on real-time prediction of user's next level of sexual wanting emotion, in the absence of any feedback and solely based on the user's brain wave (EEG) reading.