ShaoNao Sci-Tech Secures Shanghai's First Medical Device Registration Certificate for Motor Imagery-Based BCI Rehabilitation System

Dec 24, 2024 07:45 CST Updated 07:45

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Recently, the technology transfer project of Professor Banghua Yang's team from the Brain-Computer Engineering Research Center of the School of Mechatronic Engineering and Automation at Shanghai University:"EEG Acquisition Rehabilitation Training Equipment", supporting the partner company SHAONAO-TECH in passing the review by the Shanghai Medical Products Administration, officially obtaining the first brain-computer interface medical registration certificate in Shanghai. This marks that Shanghai University, focusing on national needs around the "Five-Five Strategy," has achieved another practical application of scientific and technological achievements transformation in the Greater Shanghai University Science Park.！

The approved medical registration certificate represents the research achievements of Professor Banghua Yang's team from the School of Mechanical and Electrical Engineering and Automation over the past 20 years. Their work has focused on core decoding technology for brain-computer interfaces (BCI) and its integration with VR technology in the field of rehabilitation engineering. This product is a fully China-produced rehabilitation device targeting neurological system diseases. The device utilizes core intelligent decoding technology of motor imagery-based brain-computer interfaces, combined with virtual reality, for the rehabilitation treatment of neurological disorders such as stroke. Based on an experimental platform, it ultimately forms a BCI intelligent decoding and training product for patients in the flaccid paralysis phase post-stroke. This equipment provides integrated support for EEG data acquisition, transmission, storage, model training, and backend services, while also supporting various model validations and algorithm optimization improvements, with final verification and evaluation in clinical settings. The "EEG Acquisition Rehabilitation Training Device" achieves competitive performance with fewer leads, enhancing clinical accessibility and stability.

This device guides patients to perform tasks through animations, collects EEG signals, and wirelessly transmits them to a computer for analysis and processing, enabling real-time monitoring of the patient's rehabilitation training. The device integrates biofeedback rehabilitation technology, which can assist limb movements through electrical stimulation while interacting with animated scenarios, enhancing the interactivity and enjoyment of the training. This innovative device from SHAONAO-TECH brings new treatment hope to patients with brain diseases and neurological rehabilitation needs, showing great potential in the rehabilitation of conditions such as stroke and traumatic brain injury sequelae.

In recent years, robot technology based on brain-computer interface regulation has provided new methods for stroke rehabilitation. Various paradigms of brain-computer interface robots have been widely used in post-stroke rehabilitation to promote functional recovery in patients.

As one of the earliest scholars in China to engage in brain-computer interface technology research, Professor Banghua Yang from Shanghai University has long been committed to the integration of EEG signal processing, brain-computer interface, and virtual reality technology, as well as their engineering applications. She has secured nearly 20 national-level projects, published over 150 papers, and applied for more than 10 international and Chinese patents. She was also selected as a leading talent in Baoshan District. Notably, she pays special attention to motor function disorders in stroke patients, focusing on transforming research findings into practical applications.

In 2021, Professor Yang Banghua founded SHAONAO-TECH to further advance the productization of research achievements. He aims to make advanced brain-computer interface technology more widely applied in rehabilitation training for stroke patients, achieving more precise and efficient recovery.

Focus on Adaptive Transfer EEG Recognition Algorithms for Accurate and Efficient Signal Decoding

Due to impaired brain function, certain limbs of stroke patients often cannot function properly. Traditional rehabilitation training mainly relies on passive limb exercises. However, Professor Yang Banghua's team has taken a different approach, starting from the "brain," the source of command, allowing the patient's brain to actively participate in the rehabilitation process.

Professor Yang Banghua's goal is to read people's "thoughts" based on the motor imagery paradigm.

Specifically, brain-computer interface technology detects brain waves to determine whether a patient has the intention to actively complete rehabilitation movements. Once confirmed, it immediately sends commands back to the patient and external devices such as rehabilitation robots, forming a closed-loop neurorehabilitation training system that achieves the rehabilitation process from the brain to the limbs and from the limbs back to the brain.

This training method not only demands certain abilities from the patients but also poses challenges to the performance of the equipment. The patients' motor imagery capability, as well as whether they can generate effective event-related desynchronization or event-related synchronization during the motor imagery process, directly impacts the accuracy of control and the final rehabilitation outcome. Meanwhile, for a brain-computer interface based on the motor imagery paradigm to achieve accurate intention detection and effective control, it must undergo prolonged training and optimization.

SHAONAO-TECH is also moving forward in this direction.Professor Banghua Yang told Arterial Network that accurately parsing motor imagery signals requires an in-depth understanding of the motor imagery coding paradigm in brain-computer interfaces and human neurophysiological activities, as well as developing appropriate algorithms for analysis.

It is reported that SHAONAO-TECH has integrated multiple key steps to achieve efficient and accurate EEG signal analysis through its self-developed adaptive transfer EEG recognition algorithm. First, the team uses technologies such as PSD, CSP, wavelet transform, time-frequency analysis, HHT, and deep learning for feature extraction to distinguish the essential characteristics of different tasks. Then, the team builds various classifiers such as LDA, Bayesian, SVM, and neural networks to accurately identify different motion tasks.

In terms of neural decoding, the team not only proposed a spatio-temporal-frequency joint optimization transfer learning algorithm for two types of motor imagery tasks but also focused on developing asynchronous fMRI-EEG unilateral limb four-class MI tasks. In addition, based on fMRI-weighted convolutional neural networks, the team used convolution for feature extraction and designed an attention module that calibrates relevant information from three perspectives: frequency, local space, and feature maps. The multi-feature attention module adaptively selects effective band-specific features for individuals; enhances spatial awareness of MI-related leads; and filters out redundant feature maps, thereby further improving decoding performance.

Professor Banghua Yang emphasized that, in the process of building an adaptive transfer EEG recognition algorithm, SHAONAO-TECH focused on constructing an appropriate network structure, selecting suitable parameters, and deeply mining and analyzing time-varying features from the signals. As a result,This algorithm can capture individual differences and time-varying changes in brain waves more accurately and efficiently.

Launch of Brain-Controlled Rehabilitation Wheelchair Reduces Stroke Recovery Time by 1/3

In a recent interview, Professor Yang Banghua expressed that Aunt Ji was a special patient who left a deep impression on her. At the Second Rehabilitation Hospital of Shanghai, this 70-year-old retired primary school math teacher was deeply troubled by the loss of sensation and inability to move her right hand. Compared with other patients, Aunt Ji was proactive in trying brain-computer interface technology. With the help of the brain-computer interface, her right hand gradually regained mobility, improving its motor function by 50% compared to before. She stated, "This technology has restored my confidence in life."

Among the many fields of rehabilitation therapy, stroke rehabilitation is undoubtedly a niche area with strong latent demand and essential necessity. Besides patients recovering from fractures and sports injuries, stroke patients account for the highest proportion of inpatients undergoing rehabilitation. According to the *National Report on Medical Service and Quality Safety in Rehabilitation Medicine*, among the main diseases treated by the rehabilitation departments of 1,897 sample hospitals, the proportion of discharged stroke patients was as high as 31%, second only to patients undergoing fracture rehabilitation. The innovative technology of SHAONAO-TECH holds promise to significantly improve this situation. With the annual incidence of stroke increasing year by year, the admission rate for treatment remains low. According to data from Frost & Sullivan, the number of stroke patients in China exceeded 16 million in 2018, with the incidence showing a trend of affecting younger populations, yet the admission rate remains below 30%.

In response to this contradiction,SHAONAO-TECH Launches Its Flagship Product —— Brain-Controlled Rehabilitation Wheelchair. This wheelchair can be controlled without physical movement, providing a free and reliable rehabilitation and mobility tool for individuals with limited limb function.Breaking it down, this integrated medical device for brain neural rehabilitation and neuromodulation uses a graphene EEG cap as the sensing hardware and an electric wheelchair and exoskeleton as actuators. It drives the actuators to move the user through EEG acquisition, signal processing, and intention decoding.

In simple terms, the main equipment consists of a display screen, a "cap" that can collect brainwave signals, and a wheelchair.

When the button on the display interface blinks, the patient only needs to look at the button with their eyes. The button will then turn red and be selected, and the wheelchair will move forward or stop according to the command. Meanwhile, the patient imagines moving their left or right hand to stimulate the brain nerves into generating corresponding EEG signals. After these signals are successfully detected and decoded, the patient's true intention to turn left or right can be identified.

Zhang Yonghuai, General Manager of SHAONAO-TECH, said in an interview with Artery,After five generations of iteration and optimization, the brain-controlled rehabilitation wheelchair has achieved significant improvements in both comfort and human-machine application parameters. Particularly in clinical applications, the brain-controlled rehabilitation wheelchair can help patients reduce the stroke rehabilitation cycle by one-third.

Build a Clinical Collaboration Matrix to Accelerate Product Implementation

Speaking of future research directions, Zhang Yonghuai told Vbdata that SHAONAO-TECH has reached in-depth cooperation with multiple hospitals, and all the directions are derived from the hospitals. Professor Yang Banghua also believes that only through extensive communication with hospitals and an accurate understanding of the needs of doctors and patients can more personalized and humanized products be developed, truly promoting the industrialization of scientific research achievements.

For example, the use of brain-computer interfaces for in-depth monitoring of patients' anesthesia levels provides safer and higher-quality medical services for perioperative patients. This is also a research direction formed by SHAONAO-TECH based on the needs of the hospital.In addition, SHAONAO-TECH has also made arrangements in multiple fields such as a depression auxiliary assessment system, an intention expression system for critically ill patients, an attention enhancement training system, and a finger joint continuous passive rehabilitation training system.。

In exploring elderly cognition, Alzheimer's monitoring, and attention training, SHAONAO-TECH has never stopped its efforts, dedicating itself to applying advanced brain-computer interface technology in the field of rehabilitation and health.

It is reported that in the future, SHAONAO-TECH will further optimize brain-computer interface technology in three stages.

First, develop dedicated integrated chips for real-time processing and analysis of EEG signals to achieve low power consumption and high efficiency. Research and develop advanced algorithms to improve the speed and accuracy of signal processing and reduce misidentification rates. Secondly, brain-computer information interaction methods are expected to evolve from being primarily electrical to multimodal applications combining electrical, optical, magnetic, and acoustic means. By integrating different interaction methods, the effectiveness of brain-computer interface technology applications will be enhanced. Finally, combined with VR&AR technology, brain-computer interface technology can achieve large-scale, inclusive applications for personalized, precise, and emotionally-driven monitoring and intervention of brain function health, becoming an intelligent digital therapy driven by big data and algorithms.

Currently, SHAONAO-TECH is also actively initiating financing to promote the industrialization of brain-computer interface technology. SHAONAO-TECH will continue to focus on the research, development, and application of brain-computer interface technology, bringing a better life to stroke patients.

Government/Park/Project Co-create and Win-win, Relay Boost

For this acquisitionThe First Brain-Computer Type II Certificate in Shanghai, SHAONAO-TECH has obtainedBaoshan District GovernmentAndBaoshan District Science and Technology CommissionUnder the thoughtful guidance and support. The district government not only provided strong policy support but also offered comprehensive assistance in terms of funding, research, and market. The active aid from the Baoshan District Science and Technology Commission has expedited SHAONAO-TECH's technological breakthroughs and product launches, promoting the company’s innovative development.

At the same time, we would like to express our special thanks to the professional guidance and efficient service provided by the Shanghai Drug Administration and the Baoshan Drug Administration. Their technical strength and business capabilities have provided a solid guarantee for the research and development and product launch of SHAONAO-TECH.

In addition,Shanghai University Sci-Tech ParkWith full support, SHAONAO-TECH has been provided with valuable scientific research resources, incubation services, and industrial chain connections, helping the company grow rapidly and achieve industrial application. The innovative ecosystem and cooperation platform within the Huanshangda Science Park have injected strong momentum into the development of SHAONAO-TECH.

SHAONAO-TECHSHAONAO-TECH will stay true to its original mission, continuing to uphold the principles of technological innovation and the pursuit of excellence in quality, providing the global healthcare industry with smarter and more efficient rehabilitation equipment. Through dedication and effort, SHAONAO-TECH will make a positive contribution to enhancing local medical service standards and promoting the development of the healthcare industry in Baoshan District and even Shanghai, delivering a higher quality rehabilitation experience for patients, and jointly ushering in a new chapter of intelligent rehabilitation.

Part of the content is reproduced from: Arterial Network