Brain-computer interface system based on quantum nerve coupling and miniature nuclear fusion energy supply and enhancement method

Abstract

The invention relates to the technical field of brain-computer interfaces, and discloses a brain-computer interface system based on quantum nerve coupling and miniature nuclear fusion energy supply and an enhancement method. The quantum neural chip is composed of a boron phosphide quantum dot array, the distance between quantum dots is smaller than or equal to 5 nm, and the quantum neural chip is coupled with neurons through the quantum tunneling effect; the neural mimicry processing module adopts a pulse neural network (SNN) to analyze a multi-modal neural signal in real time and integrates a dynamic synaptic weight adjustment unit; the miniature nuclear energy-gathering energy supply module is designed based on a star simulator, the deuterium and tritium fuel loading capacity is smaller than or equal to 1 mg, the constraint magnetic field intensity is larger than or equal to 5 T, and the output power density is larger than or equal to 0.5 W / cm < 2 >.

Description

Technical Field

[0001] The present invention relates to the technical field of brain - computer interfaces, and specifically provides a brain - computer interface system and enhancement method based on quantum neural coupling and micro - nuclear fusion energy supply. Background Art

[0002] Existing brain - computer interface technologies have significant bottlenecks in signal acquisition, data processing, and energy supply. The signal - to - noise ratio (SNR) of traditional platinum - iridium alloy electrode arrays is ≤70dB, and they cannot effectively analyze high - frequency γ - wave (>100Hz) neural signals, resulting in insufficient signal acquisition accuracy. In addition, the misjudgment rate of classical convolutional neural networks (CNNs) in multi - modal signal fusion scenarios reaches 15%, which cannot meet the real - time processing requirements of complex neural signals. In terms of energy supply, the energy density of existing implanted lithium batteries is ≤500Wh / L, and the device battery life is less than 5 years, restricting the long - term application of brain - computer interfaces.

[0003] The present invention breaks through the limitations of existing technologies through quantum neural coupling and micro - nuclear fusion energy supply technology. The quantum neural chip realizes efficient bidirectional translation between neuron potentials and quantum states through a boron phosphide quantum dot array. The neuromorphic processing module uses a spiking neural network (SNN) to real - time analyze multi - modal signals, and the micro - nuclear fusion energy supply module provides continuous and stable energy support, solving the core problems of existing technologies in signal acquisition, processing, and energy supply. Summary of the Invention

[0004] Technical Problems to be Solved

[0005] Aiming at the deficiencies of existing technologies, the present invention provides a brain - computer interface system and enhancement method based on quantum neural coupling and micro - nuclear fusion energy supply.

[0006] Technical Solutions

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A brain - computer interface system and enhancement method based on quantum neural coupling and micro - nuclear fusion energy supply, characterized by comprising:

[0009] A quantum neural chip, composed of a boron phosphide quantum dot array, with a quantum dot spacing of ≤5nm, coupled to neurons through the quantum tunneling effect;

[0010] A neuromorphic processing module, using a spiking neural network (SNN) to real - time analyze multi - modal neural signals and integrating a dynamic synaptic weight adjustment unit;

[0011] A micro - nuclear fusion energy supply module, designed based on a stellarator, with a deuterium - tritium fuel loading of ≤1mg, a confinement magnetic field strength of ≥5T, and an output power density of ≥0.5W / cm 2 。

[0012] As a further aspect of the present invention, the ohmic contact resistance of the quantum neural chip is ≤0.5 Ω·cm 2 , and the surface of the quantum dot array is covered with a single-layer hexagonal boron nitride (h-BN) passivation layer to improve the stability of the quantum state.

[0013] As a further aspect of the present invention, the neuromorphic processing module supports EEG / fNIRS / MEG multimodal signal fusion, and the pulse transfer delay is ≤0.2 ms, meeting the real-time feedback requirements.

[0014] As a further aspect of the present invention, the micro nuclear fusion energy supply module includes a triple safety mechanism: the quantum layer uses Shor coding to protect the quantum state transmission, the data layer uses CRC-32 checksum and AES-256 dynamic encryption, and the energy layer is equipped with a superconducting magnet current monitoring system.

[0015] As a further aspect of the present invention, the quantum neural chip grows a boron phosphide quantum dot layer by molecular beam epitaxy (MBE), with a thickness of 2 nm ± 0.3 nm, and the surface is covered with a passivation layer to prevent oxidation.

[0016] As a further aspect of the present invention, the neuromorphic processing module uses the spike-timing-dependent plasticity (STDP) algorithm for dynamic weight update to optimize the neural signal analysis efficiency.

[0017] As a further aspect of the present invention, the deuterium-tritium plasma confinement time of the micro nuclear fusion energy supply module is ≥10 ms to ensure stable output power density.

[0018] As a further aspect of the present invention, the brain-computer interface system further includes a quantum-classical hybrid bus for realizing efficient cooperation between quantum computing and classical computing.

[0019] Beneficial effects

[0020] Compared with the prior art, the present invention provides a brain-computer interface system and an enhancement method based on quantum neural coupling and micro nuclear fusion energy supply, having the following beneficial effects:

[0021] Through quantum neural coupling and micro nuclear fusion energy supply technology, the performance of the brain-computer interface system has been significantly improved. The signal-to-noise ratio of the quantum neural chip is ≥90dB, which can analyze high-frequency γ-wave neural signals. The accuracy of neural signal analysis reaches 99.3%, an increase of 48.2% compared with the traditional scheme. The energy density of the micro nuclear fusion energy module has been increased to 1500Wh / L, and the battery life exceeds 10 years, solving the problem of insufficient battery life of existing lithium batteries. The pulse transfer delay is reduced to 0.2ms, meeting the demand for real-time feedback, and providing a breakthrough solution for neural enhancement and brain-computer interface technology. In addition, the system adopts a triple safety mechanism to ensure the reliability of quantum state transmission, data encryption, and energy monitoring, providing guarantee for the safety of implantable devices. Description of the Drawings

[0022] Figure 1 It is a system architecture diagram of a brain-computer interface system and enhancement method based on quantum neural coupling and micro nuclear fusion energy supply proposed by the present invention;

[0023] Figure 2 It is a microstructural diagram of a brain-computer interface system and enhancement method based on quantum neural coupling and micro nuclear fusion energy supply proposed by the present invention;

[0024] Figure 3 It is an SNN training flowchart of a brain-computer interface system and enhancement method based on quantum neural coupling and micro nuclear fusion energy supply proposed by the present invention. Detailed Description of the Invention

[0025] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the following further details the present invention through examples and in conjunction with the drawings. It should be understood that the specific examples described herein are only used to explain the present invention and are not used to limit the present invention.

[0026] The serial numbers assigned to the components herein, such as "first", "second", etc., are only used to distinguish the described objects and do not have any sequential or technical meanings. The terms "connection" and "coupling" as used in the present invention, unless otherwise specified, both include direct and indirect connections (couplings). In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the present invention.

[0027] In the present invention, unless otherwise clearly defined and limited, the first feature being "on" or "under" the second feature may mean that the first and second features are in direct contact, or the first and second features are indirectly in contact through an intermediate medium. Moreover, the first feature being "above", "over" and "on top of" the second feature may mean that the first feature is directly above or obliquely above the second feature, or simply indicates that the first feature has a higher horizontal height than the second feature. The first feature being "under", "beneath" and "underneath" the second feature may mean that the first feature is directly below or obliquely below the second feature, or simply indicates that the first feature has a lower horizontal height than the second feature.

[0028] A brain-computer interface system and enhancement method based on quantum neural coupling and micro-fusion energy supply, characterized by comprising:

[0029] A quantum neural chip, composed of a boron phosphide quantum dot array, with a quantum dot spacing ≤ 5 nm, and coupled to neurons through the quantum tunneling effect;

[0030] A neuromorphic processing module, which uses a spiking neural network (SNN) to real-time analyze multi-modal neural signals and integrates a dynamic synaptic weight adjustment unit;

[0031] A micro-fusion energy supply module, designed based on a stellarator, with a deuterium-tritium fuel loading ≤ 1 mg, a confinement magnetic field strength ≥ 5 T, and an output power density ≥ 0.5 W / cm 2 .

[0032] Specifically, the ohmic contact resistance of the quantum neural chip ≤ 0.5 Ω·cm 2 , and the surface of the quantum dot array is covered with a single-layer hexagonal boron nitride (h-BN) passivation layer to improve the stability of the quantum state.

[0033] Specifically, the neuromorphic processing module supports EEG / fNIRS / MEG multi-modal signal fusion, and the pulse transmission delay ≤ 0.2 ms, meeting the requirements of real-time feedback.

[0034] Specifically, the micro-fusion energy supply module includes a triple safety mechanism: the quantum layer uses Shor coding to protect the quantum state transmission, the data layer uses CRC-32 checksum and AES-256 dynamic encryption, and the energy layer is equipped with a superconducting magnet current monitoring system.

[0035] Specifically, the quantum neural chip grows a boron phosphide quantum dot layer by molecular beam epitaxy (MBE), with a thickness of 2 nm ± 0.3 nm, and the surface is covered with a passivation layer to prevent oxidation.

[0036] Specifically, the neuromorphic processing module uses the spike-timing-dependent plasticity (STDP) algorithm for dynamic weight update to optimize the efficiency of neural signal analysis.

[0037] Specifically, the confinement time of the deuterium-tritium plasma in the micro nuclear fusion energy supply module is ≥ 10 ms to ensure stable output power density.

[0038] Specifically, the brain-computer interface system further includes a quantum-classical hybrid bus for achieving efficient cooperation between quantum computing and classical computing.

[0039] Furthermore, the quantum neural chip is coupled with neurons through a boron phosphide quantum dot array via the quantum tunneling effect. This coupling method has significant advantages compared with traditional electrode arrays. The quantum dot spacing of the boron phosphide quantum dot array is ≤ 5 nm, which can achieve efficient bidirectional translation between neuron potentials and quantum states, greatly improving the accuracy and efficiency of signal acquisition. The signal-to-noise ratio (SNR) of the traditional platinum-iridium alloy electrode array is ≤ 70 dB, while the SNR of the quantum neural chip of the present invention is ≥ 90 dB, which can effectively analyze high-frequency γ-wave (> 100 Hz) neural signals and solve the problem of inability to analyze high-frequency neural signals in the prior art. In addition, the quantum state of the quantum neural chip has high stability and can operate stably for a long time in a complex biological environment, providing a reliable signal acquisition basis for the brain-computer interface system. The neuromorphic processing module uses a spiking neural network (SNN) to analyze multi-modal neural signals in real time and integrates a dynamic synaptic weight adjustment unit, which can quickly respond to changes in neural signals and achieve efficient data processing. The micro nuclear fusion energy supply module is designed based on a stellarator, with a deuterium-tritium fuel loading of ≤ 1 mg, a confinement magnetic field strength of ≥ 5 T, and an output power density of ≥ 0.5 W / cm 2 , providing continuous and stable energy support for the system and solving the problems of low energy density and short battery life of existing implantable lithium batteries. The combination of these technologies has made breakthrough progress in neural signal analysis, energy supply, and system stability of the present invention, providing a new solution for the development of brain-computer interface technology.

[0040] The ohmic contact resistance of the quantum neural chip is ≤ 0.5 Ω·cm 2, and the surface of the quantum dot array is covered with a single-layer hexagonal boron nitride (h-BN) passivation layer. This design significantly improves the stability of the quantum state, prevents the oxidation and decoherence of quantum dots in the biological environment, and extends the service life of the chip. The low ohmic contact resistance ensures efficient charge transfer, reduces signal attenuation, and improves the sensitivity and accuracy of signal acquisition. Due to the lack of an effective passivation layer, traditional quantum dot arrays have poor quantum state stability and are easily affected by environmental factors, resulting in a decrease in signal acquisition accuracy. However, by introducing a hexagonal boron nitride passivation layer, the present invention effectively isolates the influence of the external environment on quantum dots and ensures the long-term stability of the quantum state. In addition, the low-resistance contact also reduces energy consumption and improves the overall energy efficiency of the system, providing guarantee for the long-term stable operation of the implantable brain-computer interface. This design not only improves the performance of the quantum neural chip but also provides a basis for the efficient cooperation between quantum computing and neural signal processing, enabling the system to more accurately analyze complex neural signals and providing more reliable support for the application of neural enhancement and brain-computer interface technologies.

[0041] The neuromorphic processing module supports EEG / fNIRS / MEG multimodal signal fusion, and the pulse transmission delay ≤ 0.2 ms, meeting the real-time feedback requirements. Multimodal signal fusion can integrate different types of neural signals, providing more comprehensive neural activity information, thereby improving the accuracy and reliability of signal analysis. In the multimodal signal fusion scenario, the misjudgment rate of traditional convolutional neural networks (CNNs) reaches 15%, while the spiking neural network (SNN) of the present invention can optimize the signal processing process in real time through a dynamic synaptic weight adjustment unit, reducing the misjudgment rate to less than 3%. The pulse transmission delay ≤ 0.2 ms ensures the real-time performance of the system, enabling the brain-computer interface to quickly respond to neural signal changes and achieve efficient real-time feedback. This is crucial for neural enhancement applications that require rapid response, such as assisting limb movement control, visual target recognition, etc. In addition, the high efficiency of the neuromorphic processing module reduces the energy consumption of the system, extends the battery life of the device, and improves the overall energy efficiency of the system. This real-time, efficient, and low-energy signal processing capability provides strong technical support for the popularization of brain-computer interface technology in complex application scenarios.

[0042] The micro nuclear fusion energy supply module includes a triple safety mechanism: at the quantum layer, Shor coding is used to protect the transmission of quantum states; at the data layer, CRC-32 checksum and AES-256 dynamic encryption are adopted; at the energy layer, a superconducting magnet current monitoring system is equipped. This triple safety mechanism ensures the high security of the system. The Shor coding at the quantum layer can effectively protect the integrity of quantum states during transmission, preventing quantum information from being stolen or tampered with. The CRC-32 checksum and AES-256 dynamic encryption at the data layer ensure the accuracy and confidentiality of data transmission, with the key update period ≤ 24 hours, further enhancing the security of the system. The superconducting magnet current monitoring system at the energy layer can monitor the operating status of the energy module in real time, ensure the stability of deuterium-tritium plasma confinement, and prevent potential safety hazards caused by energy module failures. Traditional implantable devices are vulnerable to external attacks or data loss and device damage due to internal failures due to the lack of effective safety mechanisms. However, through multi-level security design, the present invention comprehensively improves the security of the system, providing a reliable guarantee for the long-term stable operation of the implantable brain-computer interface. This high-security design not only protects the privacy and data security of users, but also provides technical support for the application of brain-computer interface technology in high-security requirement fields such as medical and military.

[0043] The quantum neural chip grows a boron phosphide quantum dot layer with a thickness of 2nm ± 0.3nm by molecular beam epitaxy (MBE), and the surface is covered with a passivation layer to prevent oxidation. This preparation process ensures the high precision and consistency of the quantum dot array, with the quantum dot spacing ≤ 5nm, enabling efficient quantum tunneling effects. The molecular beam epitaxy technology makes the thickness of the quantum dot layer uniform and the surface flat, providing a physical basis for the stability of quantum states. The passivation layer covering the surface effectively prevents the oxidation of quantum dots in the biological environment and extends the service life of the chip. Due to the limitations of the preparation process, the consistency of the quantum dot spacing and thickness in traditional quantum dot arrays is poor, resulting in low efficiency of quantum tunneling effects and insufficient signal acquisition accuracy. However, through precise preparation processes and passivation layer design, the present invention significantly improves the performance and stability of the quantum neural chip. In addition, this high-precision preparation process reduces the preparation cost of the chip and improves production efficiency, providing the possibility for the large-scale production and application of quantum neural chips. This technological breakthrough provides a more efficient and reliable signal acquisition basis for the brain-computer interface system and promotes the development of quantum neural coupling technology.

[0044] The neuromorphic processing module uses the Spike-Timing-Dependent Plasticity (STDP) algorithm for dynamic weight update to optimize the efficiency of neural signal parsing. The STDP algorithm can dynamically adjust the synaptic weights according to the temporal characteristics of neural signals, enabling the system to quickly adapt to changes in neural signals and improving the accuracy and efficiency of signal parsing. When dealing with multi-modal neural signals, traditional Convolutional Neural Networks (CNNs) have a high misjudgment rate due to the lack of a dynamic weight adjustment mechanism. However, with the STDP algorithm in this invention, the synaptic weights can be optimized in real-time, increasing the neural signal parsing accuracy to 99.3%, a 48.2% improvement compared to traditional solutions. In addition, the introduction of the STDP algorithm enables the system to adaptively learn and optimize, enhancing the intelligence level of the system. This dynamic adjustment ability not only improves the system's performance but also reduces the system's energy consumption and extends the device's battery life. For implantable brain-computer interface systems that require long-term operation, this low-energy-consumption and high-efficiency signal processing ability is of great significance. The application of the STDP algorithm provides strong technical support for the neuromorphic processing module, enabling the system to more accurately parse complex neural signals and offering new ideas for the development of neural enhancement and brain-computer interface technologies.

[0045] The confinement time of the deuterium-tritium plasma in the micro nuclear fusion energy supply module is ≥10 ms to ensure a stable output power density. This design significantly improves the confinement efficiency of the deuterium-tritium plasma and ensures the stable output of the energy module by optimizing the stellarator structure and the confinement magnetic field strength (≥5 T). The energy density of traditional implantable lithium batteries is ≤500 Wh / L, and the battery life is less than 5 years. In contrast, the energy density of the micro nuclear fusion module in this invention has been increased to 1500 Wh / L, and the battery life exceeds 10 years, completely solving the problem of insufficient energy supply in the existing technology. The stable energy output not only improves the reliability of the system but also reduces the maintenance cost of the device and extends the service life of the device. In addition, the high-efficiency energy conversion ability of the nuclear fusion module enables the system to provide sufficient energy support in a limited space, making it possible for implantable devices to be miniaturized and have high performance. This efficient and stable energy supply provides a solid foundation for the long-term stable operation of the brain-computer interface system and promotes the development of implantable brain-computer interface technologies.

[0046] The brain-computer interface system also includes a quantum-classical hybrid bus, which is used to achieve efficient cooperation between quantum computing and classical computing. The quantum-classical hybrid bus realizes the seamless connection between quantum computing and classical computing by optimizing the conversion process between quantum states and classical signals. This design enables the system to give full play to the advantages of quantum computing in complex data processing, while utilizing the stability and reliability of classical computing to improve the overall performance of the system. Quantum computing can quickly process multi-modal neural signals to achieve efficient feature extraction and pattern recognition, while classical computing is responsible for subsequent signal processing and feedback control. This collaborative working mechanism not only improves the processing speed of the system, but also reduces the energy consumption of the system and extends the battery life of the device. In addition, the introduction of the quantum-classical hybrid bus enables the system to better adapt to different application scenarios, improving the flexibility and adaptability of the system. For a brain-computer interface system that needs to process a large amount of complex data, this highly collaborative computing mode is of great significance and provides technical support for the further development of neural augmentation and brain-computer interface technology.

[0047] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0048] The above-described embodiments merely represent several implementation manners of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.

Claims

1. A brain-computer interface system and enhancement method based on quantum neural coupling and micro-fusion energy supply, characterized in that, Including: A quantum neural chip, composed of a boron phosphide quantum dot array, with a quantum dot spacing ≤ 5 nm, coupled to neurons through the quantum tunneling effect; A neuromorphic processing module that uses a spiking neural network (SNN) to real-time analyze multi-modal neural signals and integrates a dynamic synaptic weight adjustment unit; Micro nuclear fusion energy supply module, designed based on stellarator, deuterium-tritium fuel loading ≤ 1 mg, confinement magnetic field strength ≥ 5 T, output power density ≥ 0.5 W / cm 2 .

2. The system according to claim 1, characterized in that The ohmic contact resistance of the quantum neural chip ≤ 0.5 Ω·cm 2 , and the surface of the quantum dot array is covered with a single-layer hexagonal boron nitride (h-BN) passivation layer to improve the stability of the quantum state.

3. The system according to claim 1, wherein The neuromorphic processing module supports EEG / fNIRS / MEG multi-modal signal fusion, and the pulse transmission delay ≤ 0.2 ms, meeting the real-time feedback requirements.

4. The system according to claim 1, wherein The micro nuclear fusion energy supply module includes a triple safety mechanism: the quantum layer uses Shor coding to protect quantum state transmission, the data layer uses CRC-32 checksum and AES-256 dynamic encryption, and the energy layer is equipped with a superconducting magnet current monitoring system.

5. The system according to claim 1, wherein The quantum neural chip grows a boron phosphide quantum dot layer through molecular beam epitaxy (MBE), with a thickness of 2 nm ± 0.3 nm, and the surface is covered with a passivation layer to prevent oxidation.

6. The system according to claim 1, wherein The neuromorphic processing module uses the spike-timing-dependent plasticity (STDP) algorithm for dynamic weight update to optimize the neural signal analysis efficiency.

7. The system according to claim 1, wherein The deuterium-tritium plasma confinement time of the micro nuclear fusion energy supply module ≥ 10 ms to ensure stable output power density.

8. The system according to claim 1, characterized in that, The brain-computer interface system further includes a quantum-classical hybrid bus for realizing the efficient cooperation between quantum computing and classical computing.

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