Fusing Mind and Machine: A Business Professor's BCI-Driven Personalized Mindfulness Intervention Platform Files for IPO

It is difficult to associate a business school professor with brain-computer interface technology. Even the person involvedChen Liang, Associate Professor in the Department of Business Administration at the School of Business, East China University of Science and Technology, and Founder of Juejue TechnologyHe himself considers this a remarkable achievement. His research focuses on mindfulness and brain-consciousness recognition, and he has fifteen years of experience in research, practice, and teaching in this field.

In 2016, Chen Liang was first introduced to brain-computer interface (BCI) devices. Holding the headband and placing it on his head, he sought to observe his physiological states during different stages of mindfulness practice. When Chen Liang discovered that his electroencephalogram (EEG) patterns varied in accordance with his differing emotional states,He began integrating brain-computer interface technology with mindfulness meditation, striving to achieve breakthroughs in brain-consciousness recognition technology and providing an effective measurement tool for mindfulness-based therapy.

Non-invasive brain-computer interface devices collect signals based on the principles of electroencephalography (EEG). They acquire the brain's electrical activity by placing multiple electrodes on the scalp. Ionic currents within cerebral neurons induce voltage fluctuations, which are detected by the scalp electrodes. These electrodes measure the voltage changes caused by neuronal ionic currents, thereby recording the brain's spontaneous electrical activity over a given period.

Subsequently, the collected electroencephalogram (EEG) signals are transmitted to a computer or other devices for processing and decoding via algorithms. These algorithms analyze the brain signals to extract useful information.

“Brain-computer interface devices can capture all the most fundamental data."Chen Liang noted that the application of brain-computer interfaces has indeed greatly facilitated his data collection efforts."

In fact, mindfulness itself does not directly assess changes in brain signals. The concept of mindfulness originated from Buddhist meditation, emphasizing the intentional and conscious attention to and awareness of the present moment, without making any judgments, analyses, or reactions. As a systematic psychotherapeutic approach, mindfulness-based therapy emphasizes cultivating an individual’s awareness of present-moment experiences with an attitude of acceptance, openness, and non-judgment toward various internal experiences.

What Brain-Computer Interfaces Bring to Mindfulness Therapy Is Scientific Rigor.Although mindfulness does not directly assess changes in brain signals, it influences patterns of brain activity by altering an individual’s psychological state and attentional allocation. Chen Liang told VCBeat that they are conducting experiments to demonstrate the efficacy of mindfulness-based therapy. Brain-computer interfaces can scientifically reflect changes in electroencephalogram (EEG) data before and after treatment. By observing and analyzing these signal changes, researchers can indirectly gain insights into the impact of mindfulness on the brain.

As of now,Chen Liang’s team has accumulated nearly 50,000 sets of EEG data and established a proprietary database, providing robust data support for the monitoring and intervention of brain consciousness.

To delve into the subtle fluctuations of emotions and their nuanced manifestations in electroencephalogram (EEG) signals, Chen Liang’s team is also continuing its research into efficient emotion recognition and modeling techniques. “Emotion recognition primarily relies on human experts; however, when faced with massive datasets, the application of AI can accelerate the speed of manual emotion identification.”

The integration with brain-computer interfaces has brought unprecedented innovation to mindfulness technology. Leveraging non-invasive brain-computer interfaces as a platform, it enables deep insight into the subtle nuances of human emotions, providing users with more precise and sensitive emotional monitoring and intervention services. On the other hand, compared with traditional brain-computer interface technologies, this integration adopts a more intrinsic and fine-grained paradigm for mindfulness research, allowing participants to achieve real-time feedback through brain-computer interface devices and gain a clearer understanding of their emotional states.

Chen Liang revealed that the team has successfully developed three distinctive product pipelines.

Among them, the first product"HuiShang Psychological Monitoring System"By accurately capturing users' mental health data, it identifies over 20 EEG features, including sleep status, fatigue levels, anxiety, depressive tendencies, and obsessive-compulsive symptoms.

Following this successful practice, the team launched its second product—Consumer Insights and Business Opportunity Mining System, on the one hand, assisting the R&D department in objectively evaluating differences in consumer appeal across various products; on the other hand, empowering the marketing department to present empirical data demonstrating how their products influence specific emotions to target customers.

In addition, Chen Liang’s team has also launchedEmotional Insight System. Currently, the team can identify eight types of emotions and display them in real time. Whether it is cognitive anxiety, feelings of powerlessness, dissatisfaction, frustration, mental fatigue, attention level, immersion, or flow, these emotional states can be captured and visualized in real time as long as the user wears the relevant devices.

In addition to existing products and applications, Chen Liang stated that the team will continue to research the integrated innovation of emotion monitoring and brain-computer interfaces, with “"R&D + Customers + Big Data"As a strategy, continuously expand the scope of product applications.

The day before the interview, Chen Liang was still attending the first plenary session of 2024 for the China Brain-Computer Interface Alliance. As a corporate representative, he deeply felt that the integration of brain-awareness technology and brain-computer interface (BCI) technology is mutually reinforcing, jointly driving their practical applications and development.

Chen Liang candidly stated that while there are over 120 brain-computer interface (BCI) companies in China, the vast majority have yet to achieve profitability. In the commercial sector, the few companies that have made significant strides base their success on their choice of business model—either by offering top-tier products or by providing excellent service models. “In addition to our company, two other BCI firms present at the event have also achieved commercial product deployment. They focus on the medical field, developing and manufacturing BCI-based rehabilitation equipment for treatments such as post-stroke rehabilitation.”

Turning to himself, Chen Liang believes that,Brain Consciousness Recognition Technology Is the Key Driver for the Commercialization of Non-Invasive Brain-Computer Interface Devices“Only when underlying technologies can identify an increasing number of physiological biomarkers of brain activity will we be able to develop a diverse range of products, thereby providing personalized solutions for different industries,” said Chen Liang.

Advances in brain activity recognition have undoubtedly provided strong impetus for the commercialization of brain-computer interface (BCI) devices. However, prior to this, although non-invasive BCI devices had entered the market early on, their sales performance remained lackluster. During conference discussions, Chen Liang found resonance with most attendees:Although the country is vigorously promoting the development of brain-computer interfaces, there is still a long way to go before BCI technology can be fully commercialized.

In the field of emotion monitoring, traditional commercial brain-computer interface (BCI) devices are typically limited to recognizing only two metrics: focus and relaxation. These are primarily applied in areas such as attention training for children, meditation, and sleep improvement. Due to the limitations in identifiable metrics, the application scope of BCI devices remains relatively narrow. Chen Liang explained that to accelerate the practical deployment of BCI technology, he has integrated multiple brain state recognition techniques to create various combinations. These combinations can cater to diverse market demands and give rise to a wide range of app applications and solutions.

For both brain-computer interfaces and brain consciousness recognition technologies, such developments are beneficial.