EEG Meal Assistance Robot Control With Visual Feedback
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Solution Overview
Problem
Conventional care robots, including meal assistance robots, face limitations in user control, particularly for individuals with impaired physical activities, as they require labor-intensive input methods and lack feedback for incorrect selections, and existing BCI technologies face challenges with noise in EEG signals and invasive electrode requirements.
Innovation Solution
A method and apparatus for controlling robots using a brain-computer interface (BCI) that acquires and interprets EEG signals to identify user intentions, utilizing visual stimulation to select objects and motor imagery to execute motions, allowing for independent control of meal assistance robots through EEG signals without invasive electrodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional control methods (joysticks or voice recognition) are used for care robots, then the robot can be controlled, but users with impaired physical activities find it difficult to manipulate and cannot receive feedback for incorrect selections
Solution Approach 1:
The patent replaces mechanical control interfaces (joysticks, buttons) and voice recognition systems with a brain-computer interface that directly reads neural signals. This substitution allows users with physical impairments to control the robot through neural activity alone, eliminating the need for manual manipulation while providing reliable control through direct brain-to-machine communication.
Solution Approach 2:
The patent introduces an EEG-based neural signal detection system as an intermediary between the user's intent and the robot's execution. This intermediary captures neural signals, processes them to identify user intent, and translates them into robot commands, providing both control capability and feedback mechanisms that conventional interfaces lack for physically impaired users.
2Ease of operation
If EEG-based BCI devices are used for robot control, then users can control the robot with their thoughts, but the measured EEG signals contain much noise and are difficult to interpret
Solution Approach 1:
The patent implements a feedback mechanism where the system provides visual or auditory confirmation to the user when their neural signals are successfully detected and interpreted. This feedback loop allows users to verify that their thoughts are being correctly captured and translated into robot commands, making the otherwise difficult-to-interpret EEG signals usable for practical control applications.
Solution Approach 2:
The patent uses visual stimulation with different signal cycles as a reference copy to compare against the noisy EEG signals. By presenting known visual patterns and capturing the corresponding neural response, the system creates a template that can be used to identify and filter meaningful signals from the noise in subsequent measurements, improving signal interpretation accuracy.
3Reliability
If electrode based BCI devices are used, then direct brain-to-computer connection can be achieved, but a recording electrode must be inserted into the cerebral cortex which is invasive
Solution Approach 1:
The patent replaces the invasive mechanical electrode insertion method with a non-invasive EEG-based detection system. This substitution maintains the ability to capture neural signals for brain-computer control while eliminating the harmful invasive procedure, using electrical field detection through the scalp instead of physical penetration of the cerebral cortex.
4Productivity
If conventional care robots are used, then basic assistance can be provided, but much support labor is still needed and user inconvenience remains in inputting commands
Solution Approach 1:
The patent enables users to directly control the care robot through their own neural signals without requiring external operators or complex input devices. This self-service capability allows users with physical impairments to independently input commands and control the robot's actions, eliminating the need for support labor in command input while improving convenience and autonomy.
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 users with impaired physical activities to control robots independently and accurately, reducing labor and costs associated with care services while improving self-esteem, by using EEG signals to determine desired actions and execute corresponding robot motions.
Implementation Method 1
acquiring a first biosignal indicating an intention to start an operation of the robot from a user... acquiring a second biosignal evoked by the visual stimulation from the user to identify an object selected by the user
Implementation Method 2
providing the user with visual stimulation of differently set signal cycles corresponding to a plurality of objects for which the robot executes motions
Data Source
AI summary
The present disclosure relates to technology that controls a robot based on brain-computer interface, and a robot control method acquires a first biosignal indicating an intention to start the operation of the robot from a user to operate the robot, provides the user with visual stimulation of differently set signal cycles corresponding to a plurality of objects for which the robot executes motions, acquires a second biosignal evoked by the visual stimulation from the user to identify an object selected by the user, and acquires a third biosignal corresponding to a motion for the identified object from the user to induce the robot to execute the corresponding motion.