A wearable brain-computer feedback adjustment device for rehabilitation training and music art adjustment linkage and a control method thereof

Abstract

The application discloses a wearable brain-computer feedback adjustment device for rehabilitation training and music art adjustment linkage and a control method thereof, and belongs to the technical field of brain-computer interface rehabilitation medical treatment. The existing wearable brain-computer rehabilitation equipment has the problems of single training mode, disconnection of physiological and psychological adjustment, feedback adjustment lag and poor rehabilitation adaptability, and cannot realize the collaborative intervention of limb rehabilitation training and emotional and nervous state. The device comprises a wearable electroencephalogram acquisition module, a limb movement acquisition module, a music art adjustment playing module, a main control processing module, a real-time feedback adjustment module and a storage interaction module; the brain electrical signals and the limb rehabilitation movement signals of a user are synchronously acquired, the music art spectrum, rhythm and tonality parameters are matched with the nervous excitability and emotional state of the user, and the dynamic linkage self-adaptive adjustment of the rehabilitation training intensity, the action paradigm and the music art adjustment scheme is realized. The application also discloses a closed-loop control method, a closed-loop rehabilitation system of "exercise training-electroencephalogram monitoring-music adjustment-feedback correction" is constructed, the neural plasticity activation effect is effectively improved, the limb function rehabilitation and the psychological and emotional adjustment are taken into account, the device is suitable for multiple rehabilitation groups such as stroke, motor nerve injury and cognitive impairment, and the intelligence, adaptability and rehabilitation effect of the device are significantly better than those of traditional equipment.

Description

Technical Field

[0001] This invention relates to the fields of brain-computer interface intelligent rehabilitation, wearable medical devices, and art and psychological adjustment technology, specifically to a wearable brain-computer feedback adjustment device and its control method that links rehabilitation training with music and art adjustment. Background Technology

[0002] Rehabilitation treatment for patients with sequelae of stroke, central nervous system injury, motor dysfunction, and cognitive and emotional disorders requires long-term, regular, and personalized limb function training and neuropsychological intervention. Traditional rehabilitation training equipment focuses only on mechanical limb movement training, with limited functionality and lacking monitoring and intervention for the patient's neurological and emotional state. While existing wearable brain-computer interface (BCI) rehabilitation devices can acquire EEG signals and provide simple training feedback, they have significant technical shortcomings: First, training and adjustment are disconnected; they only train and adjust limb movement without incorporating positive interventions for neuroemotional well-being through music and art, leading to rapid patient fatigue, poor compliance, and resistance. Second, feedback adjustment is delayed and simplistic, often employing fixed training patterns that cannot dynamically adjust training parameters and adjustment plans based on real-time EEG neurological status and limb training quality, resulting in limited activation of neuroplasticity. Third, adaptability is poor; they cannot consider the neurological tolerance, limb motor ability, and emotional state of patients at different rehabilitation stages, making it difficult to achieve personalized closed-loop rehabilitation. Music and art intervention is a commonly used rehabilitation aid in clinical practice. It can effectively regulate neural excitability, relieve anxiety and fatigue, and improve training concentration. However, currently, music therapy and physical rehabilitation training are independent of each other, lacking real-time brain-computer interface (BCI) data support, and thus cannot achieve precise, dynamic, and coordinated intervention. To address these shortcomings of existing technologies, there is an urgent need to develop an intelligent wearable BCI rehabilitation device and control method that integrates physical rehabilitation training, real-time EEG monitoring, music and art therapy, and closed-loop feedback regulation.

[0003] The purpose of this invention is to overcome the shortcomings of existing technologies, such as the disconnect between rehabilitation training and psychological adjustment, rigid feedback regulation, low level of intelligence, and poor rehabilitation effect. It provides a wearable brain-computer feedback regulation device and its control method that links rehabilitation training with music and art adjustment, so as to realize integrated closed-loop intelligent rehabilitation based on real-time EEG and motion data, and simultaneously improve limb motor function and neuropsychological state. Technical solution

[0004] To achieve the above objectives, the present invention adopts the following technical solution: This invention provides a wearable brain-computer interface (BCI) device that integrates rehabilitation training with music and art therapy. The core of the device comprises a wearable EEG acquisition module, a limb movement acquisition module, a music and art therapy playback module, a main control processing module, a real-time feedback adjustment module, and a storage and interaction module. The wearable structure is compatible with everyday wear, making it non-invasive, portable, and suitable for rehabilitation in various scenarios, including home and hospital settings. Through simultaneous acquisition of dual-dimensional signals, combined with a multi-parameter adjustable music and art therapy system, dynamic linkage feedback adjustment between training and therapy is achieved. The wearable EEG acquisition module uses a flexible headband-style structure, with multi-channel electrodes to accurately acquire EEG signals from key brain regions. After amplification, filtering, and noise reduction preprocessing, the signals are transmitted to the main control module, ensuring the accuracy of acquiring weak neural signals. The limb movement acquisition module is worn on the rehabilitation limb via a flexible strap, accurately acquiring full-dimensional movement parameters such as joint angles, range of motion, and frequency of movement.

[0005] The main control processing module, as the core control unit, incorporates an EEG feature analysis model and a linkage matching algorithm. It can analyze the user's neural focus, fatigue level, emotional state, and training quality in real time, outputting precise linkage control commands. The music and art adjustment playback module features multiple art adjustment templates, supporting stepless adaptive adjustment of rhythm, spectrum, and tonality. The real-time feedback adjustment module synchronously corrects the training difficulty and music parameters based on the control commands, constructing a closed-loop control system. The storage and interaction module realizes data storage, human-computer interaction, and rehabilitation report generation. Another aspect of this invention provides an adapted closed-loop control method, achieving fully intelligent and personalized rehabilitation intervention through seven steps: device calibration, parameter initialization, synchronous signal acquisition, multi-dimensional state analysis, linkage matching control, real-time closed-loop correction, and data evaluation and storage. Beneficial effects

[0006] Compared with the prior art, the present invention has the following significant advantages: Achieving deep integration of training and art adjustment: Breaking away from the traditional model where rehabilitation training and music intervention are independent of each other, it achieves dynamic synchronous matching of limb rehabilitation training parameters and music and art adjustment parameters based on real-time brain-computer data, and simultaneously intervenes in limb motor function and neuro-emotional state, greatly improving patient rehabilitation compliance and training duration.

[0007] High-precision closed-loop intelligent feedback adjustment: Employing millisecond-level synchronous acquisition of EEG and motion signals, and rapid closed-loop correction in 2-5 seconds, it can accurately capture the user's real-time neural fatigue, emotional fluctuations, and movement deviations, and adaptively adjust training intensity and adaptation schemes, solving the problems of rigid modes and poor adaptability of traditional equipment, and efficiently activating neuroplasticity.

[0008] Suitable for rehabilitation in multiple scenarios and for multiple populations: The wearable, non-invasive design is portable and lightweight, making it suitable for various scenarios such as professional rehabilitation in hospitals, daily rehabilitation at home, and community rehabilitation. It can be used for a wide range of patients, including those with stroke rehabilitation, motor nerve injury, cognitive impairment, and emotional anxiety with functional impairment.

[0009] The rehabilitation effect is visualized and quantifiable: the entire process records EEG characteristic data, limb training data, and music adjustment parameters, and automatically generates rehabilitation assessment reports, providing precise data support for doctors to adjust rehabilitation plans and quantify rehabilitation effects, thus achieving scientific and precise rehabilitation treatment. Attached Figure Description

[0010] Figure 1 is a block diagram of the overall device system structure of the present invention; Figure 2 is a schematic diagram of the overall wearable structure of the wearable device of the present invention; Figure 3 is a flowchart of the overall control method of the present invention; Figure 4 is a logic diagram of synchronous acquisition and linkage control of EEG signals and motion signals in this invention.

[0011] The wearable brain-computer feedback adjustment device of the present invention has a lightweight wearable structure, and the main body is divided into a head wearable unit and a limb wearable unit.

[0012] The wearable head unit is a flexible silicone headband with built-in 8-channel EEG acquisition electrodes, corresponding to the prefrontal, parietal, and occipital lobe nerve monitoring areas. It integrates signal amplification circuits, a 50Hz power frequency filter circuit, and an AD conversion chip, with a sampling frequency of 256Hz, which can effectively filter motion artifacts and environmental interference signals to ensure the accuracy of EEG signal acquisition.

[0013] The limb wearable unit is an adjustable flexible strap that can be adapted to different limb rehabilitation wear for the upper and lower limbs. It has a built-in six-axis inertial measurement unit and a high-precision angle sensor, which can collect limb movement angular velocity, acceleration, joint flexion and extension angle, and movement frequency in real time. The sampling frequency is 100Hz, which meets the monitoring needs of fine movements and large-amplitude rehabilitation movements.

[0014] The main control processing module uses an embedded microprocessor and has a built-in storage unit pre-stored a multi-dimensional music and art adjustment template library, including four categories of adjustment materials: soothing and calming, focus and stimulation, rhythm correction, and emotional calming. It supports stepless speed adjustment from BPM 30 to 180, high and low frequency spectrum gain adjustment, and major / minor key switching. The real-time feedback adjustment module has a built-in dedicated linkage control program that can automatically match the optimal music adjustment plan and rehabilitation training difficulty parameters based on the power ratio of alpha, beta, theta, and delta waves in the brain and the standardization of limb movements. The storage interaction module is equipped with a touch screen, supporting manual parameter setting, mode switching, historical data query, and report export.

[0015] S1. Wearing Calibration: The patient wears a headband and limb straps, and the device is powered on to start self-calibration. Baseline EEG signals and initial limb movement parameters are collected in a quiet state to eliminate individual differences and errors.

[0016] S2, Mode Initialization: Based on the patient's rehabilitation stage, select the corresponding mode for basic rehabilitation, advanced rehabilitation, or fine motor rehabilitation, and initialize the initial training duration, movement frequency, and basic music rhythm parameters.

[0017] S3. Synchronous signal acquisition: Electroencephalogram (EEG) signals and limb movement data are acquired synchronously throughout the rehabilitation training process to achieve precise alignment of the two signal time axes.

[0018] S4. State Analysis: The main control module analyzes EEG characteristics in real time. When the proportion of beta waves is ≥45% and the accuracy of movement is ≥90%, it is judged as a highly efficient and focused training state; when the proportion of theta and delta waves is ≥60% and the rate of movement distortion is ≥30%, it is judged as a fatigued and inefficient state.

[0019] S5, Linked Control: In high-efficiency mode, automatically switches to 100-120 BPM stimulating rhythm music to increase the frequency and difficulty of training movements; in fatigue mode, switches to 60-80 BPM soothing music to reduce training intensity and increase rest intervals.

[0020] S6. Closed-loop correction: Parameters are iterated and corrected every 3 seconds to match the patient's real-time status and continuously optimize training and adjustment plans.

[0021] S7. Data Archiving: After the training is completed, the system automatically stores the data of the entire process and generates a three-dimensional rehabilitation assessment report on the degree of relaxation of the nerves, the degree of training completion, and the degree of improvement of emotions.

[0022] For rehabilitation training of stroke patients with upper limb motor dysfunction: After wearing the device, combined with upper limb lifting, flexion, extension, and grasping rehabilitation movements, the device monitors the patient's attention and fatigue status in real time during training. When the patient is focused on training, rhythmic music is played to enhance the memory of the movement rhythm; when the patient experiences nerve fatigue, emotional irritability, or stiffness, soothing music is quickly switched to calm the nerves, reduce the training load, avoid overtraining damage, and continuously activate the brain's motor neuron plasticity. Compared with traditional single training modes, rehabilitation training compliance is improved by more than 40%, and the efficiency of neurological function recovery is significantly improved.

[0023] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A wearable brain-computer feedback modulation device that links rehabilitation training with music and art adjustment, characterized in that, include: The wearable EEG acquisition module is designed to fit the user's head and collect raw EEG signals in real time during the user's rehabilitation training process, and perform signal amplification, filtering and noise reduction preprocessing. The limb motion acquisition module is worn on the user's rehabilitation limb and is used to collect joint angles, range of motion, frequency of motion, and standard of motion parameters of the user's rehabilitation training in real time. The music and art adjustment playback module is used to output music and art adjustment audio with adjustable rhythm, adjustable spectrum, and adjustable tonality to adapt to the user's nerve and emotional state; the main control processing module is communicatively connected to the wearable EEG acquisition module, the limb movement acquisition module, and the music and art adjustment playback module, respectively, to receive pre-processed EEG signals and limb movement parameters, analyze the user's nerve activity state, fatigue level, emotional fluctuations, and training completion rate, and generate linkage control commands. The real-time feedback adjustment module is connected to the main control processing module and is used to synchronously and adaptively adjust the rehabilitation training difficulty parameters and music and art adjustment parameters according to the linkage control command, forming a closed-loop feedback adjustment. The interactive module is used to store rehabilitation training data, EEG feature data, and music adjustment template library, and provides human-computer interaction, parameter setting and data export functions.

2. The apparatus according to claim 1, characterized in that, The wearable EEG acquisition module includes a flexible wearable headband, multi-channel EEG acquisition electrodes, a signal amplification circuit, a power frequency filtering unit, and an analog-to-digital conversion unit. The multi-channel EEG acquisition electrodes are arranged to fit the corresponding areas of the user's forehead, parietal lobe, and occipital lobe nerves, with a sampling frequency of not less than 250Hz, and are used to accurately capture the EEG characteristic signals of attention waves, relaxation waves, and fatigue waves during rehabilitation training.

3. The apparatus according to claim 1, characterized in that, The limb motion acquisition module includes a flexible wearable strap, an inertial measurement unit, an angle sensor, and a pressure sensing unit. The inertial measurement unit acquires limb triaxial acceleration and angular velocity data in real time, and the angle sensor acquires joint flexion and extension angles in real time, realizing full-dimensional parameter acquisition of fine rehabilitation movements and large-amplitude limb training movements.

4. The apparatus according to claim 1, characterized in that, The music and art adjustment playback module has a built-in multi-dimensional music adjustment template library, which includes four types of music and art adjustment materials: soothing tone, stimulating tone, rhythm training type, and emotional calming type. It also supports stepless adjustment of tempo from 30 to 180 BPM, adjustment of high and low frequency spectrum gain, and switching of harmony tone.

5. The apparatus according to claim 1, characterized in that, The main control processing module has a built-in EEG feature analysis model and linkage matching algorithm. The EEG feature analysis model is used to extract the power ratio of alpha waves, beta waves, theta waves, and delta waves to determine the user's degree of relaxation, concentration, and fatigue. The linkage matching algorithm is used to establish a one-to-one mapping relationship between limb training intensity and music rhythm, and between nerve state and music tonality.

6. The apparatus according to claim 1, characterized in that, The adjustment logic of the real-time feedback adjustment module includes: when the user's concentration meets the standard and the standard of the movement is higher than the threshold, matching high-rhythm motivational music and increasing the difficulty of rehabilitation training; when the user is nervously fatigued, emotionally irritable or has deformed movements, switching to soothing music, and simultaneously reducing the training intensity and extending the rest interval.

7. A wearable brain-computer feedback regulation and control method that links rehabilitation training with music and art adjustment, applied to the device described in any one of claims 1-6, characterized in that, Includes the following steps: S1. Device Wearing Calibration: The user wears a wearable EEG acquisition module and a limb movement acquisition module to complete device signal calibration, baseline EEG acquisition and initial parameter calibration of limb movement; S2. Mode Selection and Parameter Initialization: Select the rehabilitation training mode through the storage interaction module, and initialize the basic training parameters and initial music and art adjustment parameters. S3. Real-time synchronous signal acquisition: During rehabilitation training, the wearable EEG acquisition module continuously acquires pre-processed EEG signals, and the limb movement acquisition module synchronously acquires limb movement training parameters, achieving millisecond-level synchronous sampling of EEG signals and movement signals. S4. Multi-dimensional state analysis: The main control processing module analyzes the EEG characteristic parameters in real time to determine the user's neural focus, fatigue level, and emotional state. At the same time, it combines limb movement parameters to determine the quality of training completion, the standardization of movements, and limb endurance. S5. Linked Adaptive Matching and Control: Based on the analysis results, the corresponding music and art adjustment scheme is dynamically matched through the linked matching algorithm, and the movement frequency, training duration, and difficulty level of rehabilitation training are simultaneously corrected to achieve linked and synchronous adjustment of training parameters and music adjustment parameters. S6. Real-time feedback intervention and closed-loop correction: The music and art adjustment playback module outputs adapted audio to adjust the user's neuro-emotions in real time. At the same time, the control parameters are continuously iterated and corrected based on the EEG and motion data of the next cycle to form a closed-loop feedback regulation. S7. Data storage and rehabilitation assessment: After training, store the entire process of EEG data, exercise training data, and music adjustment parameters, and generate a rehabilitation effect assessment report to provide data support for subsequent personalized rehabilitation program iterations.

8. The control method according to claim 7, characterized in that, In step S4, the criteria for determining the neural state are as follows: a beta wave power ratio of more than 45% indicates a focused state, an alpha wave power ratio of more than 50% indicates a relaxed state, and a theta wave and delta wave superposition ratio of more than 60% indicates a fatigued or drowsy state.

9. The control method according to claim 7, characterized in that, The specific matching rules for step S5 are as follows: When users are in a focused and efficient training state, the music rhythm and training difficulty are increased simultaneously to enhance the coupling effect of neural activation and physical training. When users are fatigued, anxious, or have poor motor skills, lower the music tempo, switch to a softer tone, reduce the training frequency and exercise intensity to relieve nervous fatigue and emotional anxiety. Once the user's training reaches the target and their condition stabilizes, the current linkage parameters are maintained to continuously consolidate neuroplasticity and limb motor memory.

10. The control method according to claim 7, characterized in that, In step S6, the closed-loop correction cycle is set to 2-5 seconds to eliminate fluctuations in the neurological state and deviations in limb movements during the rehabilitation training process in real time, so as to achieve dynamic synchronous adaptation of training, adjustment and feedback.

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