EEG Smartphone Interaction Analysis Without Clock Synchronization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods fail to accurately analyze neuronal activity associated with spontaneous smartphone interactions due to the lack of consistent neuronal activity patterns and the need for synchronized clocks, making seamless neurobehavioral data analysis challenging.
Innovation Solution
A method for determining event-related neuronal activation by comparing behavioral outputs with predefined patterns using a portable electronic device, such as a smartphone, without requiring artificial stimuli or laboratory settings, allowing for asynchronous correlation and database compilation of neuronal activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If synchronous neuronal recording and behavioural output recording are performed using at least two devices with synchronised clocks, then correlations between neuronal activity and behavioural outputs can be identified, but the complexity of the system increases and seamless analysis is prohibited
Solution Approach 1:
The patent combines neuronal recording and behavioural output recording into a single integrated system, eliminating the need for multiple devices with synchronized clocks. The portable electronic device simultaneously captures both EEG signals and touchscreen interactions, thereby reducing system complexity while maintaining correlation accuracy through unified timestamping.
Solution Approach 2:
The portable electronic device performs multiple functions: it acts as both the neuronal recording device (EEG headset) and the behavioural output recorder (smartphone with touchscreen). This multi-functional approach eliminates the need for separate specialized devices and their synchronization infrastructure.
2Measurement precision
If touchscreen interactions are used as event markers for neuronal activity analysis, then high-resolution event-based analysis can be achieved, but it remains unknown whether consistent neuronal activity patterns exist
Solution Approach 1:
The patent performs preliminary analysis to establish consistent neuronal activity patterns associated with touchscreen interactions before using them as reliable event markers. By pre-identifying and validating these patterns, the system ensures their reliability for subsequent high-resolution event-based analysis of spontaneous behavior.
Solution Approach 2:
The system uses feedback from initial recordings to refine and validate the consistency of neuronal patterns associated with touchscreen events. Through iterative analysis and validation, the system confirms the reliability of these patterns before deploying them for precise event-based measurement.
3Measurement precision
If laboratory-based measures with artificial stimuli are used, then clear temporal landmarks are provided for analysis, but the results cannot be simply applied to spontaneous smartphone interactions
Solution Approach 1:
Instead of using artificial stimuli to create temporal landmarks, the patent inverts the approach by using naturally occurring touchscreen interactions as the event markers. This allows the system to maintain temporal precision while analyzing spontaneous, ecologically valid smartphone usage behavior in real-world settings.
Solution Approach 2:
The system allows spontaneous smartphone interactions to serve themselves as the temporal landmarks for analysis. The touchscreen events naturally provide the timing references needed for event-based neuronal analysis, eliminating the need for external artificial stimuli and enabling direct application to real-world behavior.
4Productivity
If touches on smartphone are separated by less than 500 milliseconds, then typical smartphone usage is captured, but this is far shorter than the normal preparatory time observed for self-paced button presses
Solution Approach 1:
The patent dynamically adapts the analysis framework to accommodate the compressed timescales of smartphone interactions. By adjusting the temporal windows and analysis parameters to match the rapid 500ms or shorter intervals between touches, the system captures neuronal processing in the context of actual smartphone usage dynamics rather than laboratory button-press timelines.
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 high-resolution, event-based analysis of neuronal circuits engaged in self-paced motor control, providing effective solutions for identifying developments in user's neuronal activity, enabling early diagnosis of a user's neuronal activity, enabling high-resolution, event-based analysis of neuronal activity, enabling high-resolution, event-based analysis of neuronal activity, and enabling high-resolution, event-based analysis of neuronal activity, and enabling high-resolution, event-based analysis of neuronal circuits, providing effective solutions for identifying and monitoring neuronal circuits, providing effective solutions for identifying and monitoring neuronal activity.
Implementation Method 1
The resultant brains' electrical signal, when measured from the scalp (e.g. Electroencephalogram (EEG))
Data Source
AI summary
Described herein is a method of detecting a change in electrical signals in a brain of a user, the method comprising at least recording at least one electrical signal in the brain of a user by EEG; and recording at least one user behavior comprising a tap, a swipe, a gesture, or a sound input; wherein the recording is performed while the user is using a portable electronic device.