EEG Lighting Control for Hands-Free Building Operation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current lighting and building management systems lack the capability to be controlled using electroencephalography (EEG) technology, which could enable intuitive and user-specific control of lighting and building operations based on brain signals.
Innovation Solution
An EEG control system is developed, comprising a wearable or implantable EEG device with electrodes to detect brain signals, circuitry for real-time processing, and a processor that analyzes these signals to generate control data signals to control lighting and building management systems, including luminaires and HVAC appliances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If EEG control system is implemented, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary system comprising EEG electrodes, signal processing circuitry, and a controller that translates brain signals into control commands. This intermediary layer enables intuitive control without direct mechanical interaction, improving ease of operation while managing complexity through modular system design.
Solution Approach 2:
The invention replaces traditional mechanical control interfaces (switches, knobs, buttons) with a neurophysiological-based control system. Brain signals detected by EEG electrodes substitute for manual mechanical operations, fundamentally changing the interaction paradigm from mechanical to physiological control.
2Productivity
If real-time signal processing is implemented, then productivity is improved, but use of energy increases
Solution Approach 1:
The system processes EEG signals in periodic intervals rather than continuously, analyzing brain signals at specific sampling rates and triggering control actions only when predetermined patterns are detected. This periodic processing approach maintains productivity by responding to user intent while reducing energy consumption compared to continuous real-time processing.
Solution Approach 2:
The controller automatically detects and interprets brain signals without requiring external intervention or continuous power-intensive processing. The system self-regulates by monitoring signal patterns and executing control commands only when necessary, optimizing the balance between productivity and energy usage.
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 intuitive control of lighting and building management systems through brain signals, enhancing user experience and adaptability by allowing real-time adjustments to lighting and environmental conditions based on user brain activity.
Implementation Method 1
Electroencephalography (EEG) is an electrophysiological monitoring method to record electrical activity of the brain. It is typically noninvasive, with the electrodes placed along the scalp, although invasive electrodes are sometimes used such as in electroencephalography. EEG measures voltage fluctuations resulting from ionic current within the neurons of the brain.
Data Source
AI summary
A system including a controllable device configured to provide a premises related service in an area of a premises. The system includes an electroencephalography (EEG) device configured to be positioned with respect to a head of a user and process signals detected in real-time. The system also includes a processor in communication with the EEG device, a memory accessible by the processor and instructions stored in the memory for execution by the processor. A data is stored in the memory that associates each of a plurality of predetermined sets of signals from the brain detected via the EEG device with at least one control instruction. The execution of the instructions configures the processor to using the stored data, analyze the real-time detected signals to determine that the real-time detected signals correspond to one of the plurality of predetermined set of signals associated with the one control instruction and generate a control data signal based on the one control instruction.