Brain Training Simulator Using EEG Intention Recognition
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Solution Overview
Problem
Conventional rehabilitation therapies are inadequate for chronic patients as they are bottom-up methods unsuitable for sensor-motor loop rehabilitation from a cerebral nerve perspective, particularly for patients with weak electromyogram signals or degenerative brain diseases like dementia or cerebral apoplexy, as they lack optimal recognition of user intention and feedback.
Innovation Solution
A brain training simulator and simulation system that recognizes user action intention using brain signals, adjusts the training apparatus operations, and provides neurofeedback to enhance rehabilitation training by inducing motivation, applicable to various patient groups including those with degenerative brain diseases, through non-invasive brain signal measurement and artificial intelligence-based intention recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional bottom-up rehabilitation therapy methods are used, then physical movement rehabilitation can be achieved, but sensor-motor looped rehabilitation from cerebral nerve perspective cannot be achieved
Solution Approach 1:
The patent implements a top-down feedback mechanism where brain signals (EEG) are detected and used to control rehabilitation robot movements. The system captures neural commands from the cerebral cortex and provides feedback through the robot, completing the sensor-motor loop at the neural level rather than just the physical level. This resolves the contradiction by enabling both physical movement and cerebral nerve-level rehabilitation through neural feedback control.
Solution Approach 2:
The patent replaces conventional mechanical control systems with a brain-computer interface system. Instead of relying solely on physical movement capability, the system substitutes mechanical control with neural signal detection and processing. The EEG-based control system translates cerebral nerve activity into robot control commands, enabling rehabilitation for patients with severe motor impairments while maintaining the mechanical rehabilitation function.
2Measurement precision
If brain signal recognition is used to control training apparatus, then user intention recognition is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer between brain signal detection and robot control. The system uses EEG signals as intermediate carriers to translate neural intentions into control commands. This intermediary approach simplifies the overall system architecture by using a well-established bio-signal measurement method (EEG) rather than directly interfacing with complex neural mechanisms, thus improving intention recognition while managing system complexity through proven technological intermediaries.
3Productivity
If neurofeedback with stimulation driven inducement is implemented, then rehabilitation training effectiveness is maximized, but device complexity and cost increase
Solution Approach 1:
The patent merges multiple rehabilitation functions into a single integrated system. The rehabilitation robot combines mechanical therapy functions with brain-computer interface control and neurofeedback capabilities. By merging these functions, the system achieves enhanced rehabilitation effectiveness through coordinated neural-mechanical interaction while avoiding the complexity and cost of separate independent systems for each function.
4Ease of operation
If conventional rehabilitation methods are used for chronic patients with weak electromyogram signals, then treatment can be provided, but optimal rehabilitation cannot be achieved due to lack of intention recognition
Solution Approach 1:
The patent inverts the conventional control approach by controlling the rehabilitation robot through brain signals rather than through muscle signals (electromyogram). Instead of detecting muscle activity to infer movement intention, the system detects cerebral neural commands directly. This inversion enables effective rehabilitation for chronic patients with weak or absent electromyogram signals, as it bypasses the degraded muscle signaling pathway and accesses intact neural command generation.
Data Source
AI summary
Provided are a brain training simulator and a brain training simulation system. The brain training simulator includes at least one memory, and at least one processor configured to acquire a brain signal of a user acting in a first action state based on a non-invasive brain activation measurement method, determine whether an intention of the user is recognized, by selecting preset intention data that matches data of the brain signal by a preset percentage or greater, control an operation of the training apparatus based on whether the intention is recognized, and control playback of training content displayed on the training apparatus to correspond to the operation of the training apparatus.