Brain-Computer Interface: A Bridge Connecting the Human Brain to the Digital World

Brain-Computer Interface (BCI) is a technology that establishes a direct communication and control channel between the human brain and computers or other electronic devices. The origins of BCI technology can be traced back to the 1970s when researchers began attempting to interpret brain electrical signals to enable direct human-machine interaction. Since then, BCI technology has evolved from laboratory research to practical applications, gradually becoming a bridge connecting the human brain to the external world. The core of this technology lies in capturing and analyzing the signals emitted by the brain, allowing individuals to directly control computers, robots, or other electronic devices without the need for traditional input devices like keyboards or mice. Initially, BCI was primarily used to assist severely disabled individuals, such as those with tetraplegia, in regaining their ability to communicate with the outside world and live independently. However, as technology has advanced, the application scope of BCI has rapidly expanded to encompass multiple dimensions, including medical rehabilitation, virtual reality, and intelligent control.

In recent years, BCI technology has made significant leaps forward, particularly in the three major areas of hardware, software, and algorithms. In terms of hardware, BCI systems have evolved from initially bulky helmet-mounted devices to today’s minimally invasive or non-invasive devices, significantly improving portability and comfort. Innovations in software algorithms, especially the integration of deep learning and artificial intelligence technologies, have greatly enhanced the accuracy and real-time processing of signal recognition. The advancement of algorithms has made decoding brain intentions more efficient, paving the way for the practical application of BCI systems. At the same time, researchers are actively developing more advanced electrode materials and chips to enhance signal stability and safety and reduce potential harm to the brain.

In 2023, the prestigious international journal “Nature” published two groundbreaking papers in the field of BCI. These papers reported on the successful use of implanted microelectrode arrays to identify neural signals associated with facial movements during speech, enabling participants to achieve a communication speed of 62 words per minute. This marked a significant step forward for BCI technology in the field of speech reconstruction. In China, a collaborative project led by Professor Guoguang Zhao from Xuanwu Hospital of Capital Medical University and Professor Bo Hong from Tsinghua University School of Medicine developed a wireless minimally invasive BCI technology that helped patients with high spinal cord injuries regain control of computer cursors, demonstrating the immense potential of BCI technology in the field of medical rehabilitation. In early 2024, Neuralink announced that its new generation of BCI chips could be implanted in the brain in just 15 minutes. Dr. Hu Tao, a BCI expert from the Shanghai Institute of Microsystems and Information Technology of the Chinese Academy of Sciences, stated that Neuralink’s progress highlights the development trend of BCI technology - focusing on clinical applications and playing an irreplaceable role in the treatment of major neurological diseases.

Overall, the development of BCI technology has not only enhanced communication and control capabilities for individuals with severe physical disabilities but has also demonstrated broad application prospects in multiple fields, including medical rehabilitation, virtual reality, and intelligent control. In the future, with the advancement of core technologies such as neural decoding algorithms, neural probes, and chips, BCI technology is expected to achieve broader applications and deeper research.

BCI technology, acting as a bridge between the human brain and external electronic devices, translates human intentions into executable commands by detecting and interpreting brain activity signals. Recent advancements in hardware, software, and algorithms have enabled BCI to transition from laboratory research to practical applications, opening up vast possibilities. Generally, this is achieved through the following processes and technologies.

The core of BCI technology lies in signal detection and acquisition. This involves capturing electrical signals generated by brain neural activity using either invasive or non-invasive sensors. Invasive devices, such as Neuralink’s BCI chips with 4096 channels, can record deeper brain activity. Increased single-channel stimulation voltage helps more effectively activate neural feedback areas. Non-invasive devices, like EEG, capture signals through the scalp, though the signals are weaker, avoiding invasive procedures.

After signal acquisition, the data undergoes preprocessing, feature extraction, and classification to convert it into recognizable commands. The Institute of Automation of the Chinese Academy of Sciences in Shenyang proposed a brain signal decoding method based on logarithmic Euclidean metric Riemannian geometry, improving decoding efficiency. Additionally, combining multiple signal acquisition methods, such as EEG and functional near-infrared spectroscopy (fNIRS), enhances signal accuracy and reliability. The application of deep learning algorithms, like artificial neural networks, significantly improves signal recognition accuracy.

Adaptive BCI systems can dynamically adjust based on brain state, enhancing adaptability to non-stationary brain activity. Real-time updates to the recognition model improve control precision and robustness. Additionally, recording the entire brain’s activity helps optimize decoding sites, further understanding brain structure, and reducing error rates.

Chinese and American scientists have developed organic electrochemical transistors for low-power micro-sensors, enhancing interaction efficiency and human-machine interaction. Neuralink’s new generation of chips are not only small and easy to implant but also have a battery life of an entire day, similar to an Apple Watch. They can be wirelessly charged for long-term use. The use of special materials ensures minimal brain damage and improves safety.

The latest applications and research progress of BCI technology in medical rehabilitation are primarily reflected in the following aspects:

• Recent breakthroughs in key technologies have ushered in a rapid development period for the BCI industry, making it a focal point of market attention.

• BCI technology has broad application prospects in fields such as medical rehabilitation, education, military, entertainment, and smart homes.

• Latest research progress in the field of BCI is being released consecutively both domestically and internationally. The industry is also accelerating the clinical application and industrialization of this technology.

To balance communication bandwidth and invasiveness, some teams have adopted a compromise semi-invasive technology, similar to placing a microphone inside a room’s wall.

Immersive VR environments for multi-rehabilitation applications are being established, and clinical validation experiments with hundreds of participants are being conducted in multiple tertiary hospitals.

Traditional BCI technology primarily relies on electrode signals. However, in the future, it will develop into a comprehensive application of various methods, including electricity, light, magnetism, sound, and more. This diversification of information interaction methods can improve the sensitivity and accuracy of BCI.

Future BCI technology will further iterate interface materials, developing minimally invasive BCI technology. Implantable electrodes will tend to be flexible, miniaturized, high-throughput, and integrated. These technological advancements will make BCI safer and more comfortable, suitable for more human application scenarios.

BCI technology will deeply integrate with artificial intelligence technology, achieving collaborative development and ultimately achieving brain-computer intelligence. This integration will greatly enhance the intelligence level of BCI, enabling it to better understand and process complex brain electrical signals.

Key technologies for high-speed brain-computer decoding based on mixed EEG features have achieved significant breakthroughs. For example, research teams have designed a new hybrid multi-access coding paradigm and developed a high-speed brain-computer information input system with 216 instructions, where the output time for a single instruction is only 1.2 seconds, and the online average information transmission rate is as high as 302.83 bits/min. This high-speed information transmission capability will provide strong support for real-time brain-computer interaction.

Invasive BCI technology will develop towards higher biocompatibility and safety, adopting more flexible electrode materials and safer design schemes. This will help reduce brain damage and improve the reliability of long-term use.

BCI technology is not only widely used in the medical field but also demonstrates great potential in non-medical fields. For example, combining multi-channel functional electrical stimulation technology on the surface of the upper limb can decode volunteers’ movement intentions and control the grasping movements of their own paralyzed upper limbs.

To address the issues of accuracy and efficiency in neural signal recognition and decoding in BCI technology, the following approaches can be considered:

The Institute of Automation of the Chinese Academy of Sciences in Shenyang has proposed a brain signal decoding method based on logarithmic Euclidean metric Riemannian geometry, which improves the efficiency of brain signal decoding while maintaining accuracy, effectively reducing the delay in executing commands in BCI systems.

Research shows that combining different brain signal acquisition methods (such as EEG and fNIRS) can improve the performance of BCI systems. This approach can better capture changes in brain activity signals and classify them through technologies like pattern recognition, thus improving the accuracy and reliability of decoding.

Using deep learning algorithms to process EEG signals can significantly enhance the accuracy of signal recognition and classification. Common BCI signal analysis algorithms include artificial neural networks, Bayesian-Kalman filtering, and genetic algorithms.

Adaptive BCI systems can dynamically adjust the induction paradigm and update the recognition model in real-time based on the current state of the brain, enhancing the adaptability of brain-control systems to non-stationary brain activity and improving control precision and robustness.

By recording the entire brain’s activity, scientists can determine whether additional electrodes are needed at other decoding sites to improve accuracy. Further understanding of brain structure can also lead to the construction of better decoders, reducing the error rate.

Organic electrochemical transistors developed by Chinese and American scientists for low-power micro-sensors can be used in next-generation human-machine/brain-machine interfaces and bionic mechanisms, enhancing interaction efficiency and improving human-machine interaction methods.

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