Embedded Brainwave Device with Synchronized Environmental Sensing
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Solution Overview
Problem
Existing brain-machine interface systems face challenges in associating brain data signals with semantic values to trigger appropriate actions due to constrained learning processes and insufficiently relevant training sets, making it difficult to construct efficient predictors.
Innovation Solution
A device comprising both brainwave and environmental sensors, such as video and sound sensors, worn by a user, processes and associates brain data with environmental data to enhance learning mechanisms, allowing for the construction of an effective training set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If constrained learning processes are used with limited training data, then the learning mechanism is simpler to implement, but the predictive model accuracy deteriorates
Solution Approach 1:
The patent combines brainwave data from EEG sensors with environmental data from multiple sensors (video camera, sound sensor, accelerometer, gyroscope, magnetometer) to create a comprehensive training dataset. This merging of diverse data sources enriches the training information without requiring complex learning algorithms, thus improving predictive model accuracy while maintaining implementation simplicity.
Solution Approach 2:
The patent introduces an intermediary processing stage that automatically associates brainwave signals with environmental context through sensor data correlation. This intermediary layer pre-processes and structures the combined data before feeding it to the predictive model, reducing the complexity of the learning mechanism while enhancing model accuracy through better-prepared training data.
2Device complexity
If only brainwave sensors are used, then the device structure is simpler, but the interpretation of mental data becomes less accurate
Solution Approach 1:
The patent merges brainwave sensor data with environmental sensor data (video, audio, motion) to create a multi-dimensional dataset that provides contextual information about the user's mental state. This combination improves interpretation accuracy by correlating neural activity with environmental factors without requiring a fundamentally more complex device architecture.
Solution Approach 2:
The device integrates multiple sensor types that serve multiple functions: the video camera captures visual environment and user expressions, the sound sensor records audio context and user speech, while the motion sensors track head and body movements. This multi-functionality enriches mental data interpretation while maintaining a unified wearable device structure.
3Reliability
If extensive learning phases with screen images are used, then the training set becomes more comprehensive, but the learning process becomes more constraining and time-consuming
Solution Approach 1:
The patent performs preliminary data collection by automatically recording environmental sensor data and brainwave signals during normal user activities without requiring dedicated learning sessions. This preliminary action captures real-world data in the user's natural context, creating a relevant training set without the time-consuming constrained learning phases of previous methods.
Solution Approach 2:
The system enables self-service data collection where the user simply wears the device during daily activities, and the device automatically collects and processes training data without requiring the user to participate in structured learning sessions or interact with screens. This approach maintains training set relevance while eliminating time constraints.
Data Source
AI summary
A system having one or more devices for automatic brainwave analysis, configured to be worn by a user, and a remote processing unit configured to store all of the signals from said one or more devices, havingat least one brainwave sensor, providing a stream of brain data associated with said user;at least one environmental sensor, providing an environmental data stream;means for selecting a signal extracted from said stream of brain data, for associating them with at least one corresponding signal extracted from said stream of environmental data, for storing said signals and for transmitting them to the remote processing unit, the latter comprising means for extracting classification information from said data, and for constituting a set associating said data with the extracted classification information.