Portable brain-computer interface device using cloud computing power

Abstract

The present disclosure relates to a portable brain-computer interface device using cloud computing power, the device comprising: a processing module, an interface and a communication module, the processing module is used to: determine the function of the function module; determine the target processing resources according to the function of the acquisition module and / or the amount of data collected; when the target processing resources at least include cloud processing resources, send at least part of the collected data to the cloud through the communication module. According to the processing device of the embodiment of the present disclosure, it can be determined whether the collected data needs to be sent to the cloud for processing. If necessary, the collected data that is difficult to process locally can be sent to the cloud for processing through the communication module, which can improve the processing efficiency and real-time performance of EEG signals, help to circumvent the limitation of local processing resources, develop more functional signal acquisition and processing functions, and also help to obtain richer EEG signals.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a portable brain-computer interface device using cloud computing power. Background Art

[0002] The implementation of brain-computer interface technology is mainly through steps such as EEG acquisition, signal acquisition and processing, signal output (execution), and signal feedback. The electrodes for collecting EEG signals are different from the electrodes used to generate electrical stimulation, which results in different ways of acquiring EEG signals. Brain-computer interfaces are divided into invasive brain-computer interfaces and non-invasive brain-computer interfaces. Whether it is an invasive brain-computer interface or a non-invasive brain-computer interface, both require hardware equipment that can support EEG signal acquisition and transmission, and software algorithms that can support EEG signal processing and analysis.

[0003] Brain-computer interface devices can be carried by users, so that the user's EEG signals can be obtained in real time. However, the processing resources of portable devices are limited, and the amount of EEG signal data is large, making analysis difficult. The processing power of portable devices is insufficient to analyze complex EEG signals, and thus it is difficult to obtain analysis results in real time, which may have an adverse impact on the monitoring and research of brain diseases. Summary of the invention

[0004] According to one aspect of the present disclosure, a processing device is provided, comprising: a processing module, a plurality of interfaces for connecting functional modules, and a communication module, wherein the processing module is used to: determine the function of the functional module connected to the processing module through the interface, the functional module including an acquisition module; determine a target processing resource for processing the acquired data according to the function of the acquisition module and / or the amount of acquired data transmitted from the acquisition module to the processing module through the interface; and send at least part of the acquired data to the cloud through the communication module when the target processing resource at least includes a cloud processing resource.

[0005] In a possible implementation, the functional module also includes an execution module, and the communication module is further used to: receive a first processing result sent by the cloud; the processing module is further used to: obtain an execution instruction based on the first processing result, and send it to the execution module.

[0006] In a possible implementation, the processing module is further used to: when the function of the acquisition module is to acquire electrocardiogram signals and / or respiratory signals, determine the target processing resource as a local processing resource of the processing module.

[0007] In a possible implementation, the processing module is further used for: when the function of the acquisition module is to acquire EEG signals, the target processing resources at least include the cloud processing resources.

[0008] In a possible implementation, the data volume is determined by the number of acquisition channels, and the processing module is further used for: when the number of acquisition channels of the acquisition module is greater than or equal to a channel number threshold, the target processing resources at least include the cloud processing resources.

[0009] In a possible implementation, the data volume is determined by a sampling rate, and the processing module is further used for: when the sampling rate of the acquisition module is greater than or equal to a sampling rate threshold, the target processing resources at least include the cloud processing resources.

[0010] In a possible implementation, the target processing resources at least include the cloud processing resources, including: the target processing resources only include cloud processing resources; or the target processing resources include a combination of cloud processing resources and local processing resources.

[0011] In a possible implementation, the first processing result includes a physiological state of the target object determined according to the collected data, and the processing module is further used to: obtain the execution instruction according to the physiological state.

[0012] In a possible implementation manner, the processing module is further used to: determine the first processing result as the execution instruction.

[0013] In one possible implementation, the target processing resources include a combination of cloud processing resources and local processing resources of the processing module, the collected data sent to the cloud includes first collected data, the collected data processed by the local processing resources includes second collected data, and the processing module is further used to: process the second collected data by the local processing resources to obtain a second processing result; obtain the physiological state of the target object based on the first processing result and the second processing result; and obtain the execution instruction based on the physiological state.

[0014] In one possible implementation, the target processing resources include a combination of cloud processing resources and local processing resources of the processing module, the collected data sent to the cloud include first collected data, and the collected data processed by the local processing resources include second collected data, and the processing module is further used to: process the second collected data by the local processing resources to obtain a second processing result; send the second processing result to the cloud through the communication module, so that the cloud obtains the first processing result based on the second processing result and the first collected data; and determine the first processing result sent by the cloud as the execution instruction.

[0015] In a possible implementation, the processing module is further used to: process the collected data through local processing resources to obtain the physiological state of the target object; send the physiological state to the cloud, so that the cloud determines the first processing result according to the physiological state; and determine the first processing result as the execution instruction.

[0016] According to the processing device of the embodiment of the present disclosure, it can be determined whether the collected data needs to be sent to the cloud for processing. If necessary, the collected data that is difficult to process locally can be sent to the cloud for processing through the communication module. This can improve the processing efficiency and real-time performance of EEG signals, help to circumvent the limitations of local processing resources, develop more functional signal collection and processing functions, and also help to obtain richer EEG signals to provide data support for brain research.

[0017] It should be understood that the above general description and the following detailed description are exemplary and explanatory only and do not limit the present disclosure. Other features and aspects of the present disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings herein are incorporated into the specification and constitute a part of the specification, and these drawings illustrate embodiments consistent with the present disclosure and are used together with the specification to illustrate the technical solutions of the present disclosure;

[0019] Figure 1 A block diagram showing a processing device according to an embodiment of the present disclosure;

[0020] Figure 2 A block diagram showing a processing device according to an embodiment of the present disclosure;

[0021] Figure 3 A block diagram showing an interface according to an embodiment of the present disclosure;

[0022] Figure 4 A block diagram showing an interface according to an embodiment of the present disclosure;

[0023] Figure 5 A schematic diagram showing processing by local processing resources according to an embodiment of the present disclosure;

[0024] Figure 6 A schematic diagram showing an application of a processing device according to an embodiment of the present disclosure;

[0025] Figure 7 A schematic diagram showing processing by cloud processing resources according to an embodiment of the present disclosure;

[0026] Figure 8 A schematic diagram showing an application of a processing device according to an embodiment of the present disclosure;

[0027] Fig. 9 A schematic diagram of an application of a processing device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0028] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0029] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0030] The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set consisting of A, B, and C.

[0031] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0032] In the related technology, brain-computer interface devices mainly include electroencephalographs, which can be used to collect EEG signals for analysis. However, EEG machines are mainly used in clinical research and are difficult for users to carry around. Portable devices have limited processing capabilities and are difficult to analyze EEG signals with large amounts of data, complex analysis methods, and high analysis difficulty.

[0033] In response to the above problems, the present disclosure relates to a processing device, including a processing module, and providing multiple interfaces for connecting with other functional modules. At the same time, it may also include a communication module for communication, for example, it can be used to communicate with the cloud. If the local processing resources of the portable device are difficult to process complex EEG signals, the communication module can be used to send the EEG signals to the cloud for processing by the processing resources of the cloud. The cloud can be configured with a large number of computing nodes, which can be used to analyze and process signals with large data volumes, and can return the processing results to the cloud, thereby improving the processing efficiency and real-time performance of the EEG signals.

[0034] Figure 1 A block diagram of a processing device according to an embodiment of the present disclosure is shown as follows: Figure 1 As shown, the device comprises: a processing module 11, a plurality of interfaces 12 for connecting functional modules, and a communication module 13.

[0035] The processing module 11 is used for:

[0036] Determine the function of a functional module connected to the processing module through the interface, the functional module including a collection module;

[0037] Determine a target processing resource for processing the collected data according to the function of the collection module and / or the amount of collected data transmitted by the collection module to the processing module through an interface;

[0038] In the case where the target processing resources at least include cloud processing resources, at least part of the collected data is sent to the cloud via the communication module.

[0039] According to the processing device of the embodiment of the present disclosure, it can be determined whether the collected data needs to be sent to the cloud for processing. If necessary, the collected data that is difficult to process locally can be sent to the cloud for processing through the communication module. This can improve the processing efficiency and real-time performance of EEG signals, help to circumvent the limitations of local processing resources, develop more functional signal collection and processing functions, and also help to obtain richer EEG signals to provide data support for brain research.

[0040] In one possible implementation, the processing device may be a brain-computer interface device, the processing module in the device may be a processor for processing and analyzing physiological signals such as EEG signals and / or generating execution instructions, and the interface may be used to connect an acquisition module for acquiring a variety of physiological signals and a communication module for communication. Furthermore, the functional module also includes an execution module, and the communication module is also used to: receive the first processing result sent by the cloud; the processing module is also used to: obtain an execution instruction based on the first processing result, and send it to the execution module. The present disclosure does not limit the application field of the processing device.

[0041] In a possible implementation, the processing module may include a controller and a computing unit, the controller is used to call various interfaces and call various functions, and the computing unit can be used to execute the called functions, that is, to provide computing power, for example, according to the called EEG signal analysis function, the EEG signal is analyzed and processed. The processing device may include multiple interfaces, which can be used to connect multiple functional modules (for example, acquisition modules, execution modules and communication modules, etc.) to achieve multiple functions. The interface may include a wired interface and / or a wireless interface, which can be used to connect to the functional module by wire and / or wirelessly, and to interact with the functional module for data, so as to control the functional module to perform various functions. The processing device may also include a basic communication unit, which is used to support the communication function of the interface, for example, the wired communication function and / or the wireless communication function. For example, the basic communication unit includes a Bluetooth unit, which can support the function of the Bluetooth interface, so as to connect to other functional modules by Bluetooth. For example, the basic communication unit includes a USB unit, which can be used to support the function of the USB interface, so as to connect to other functional modules by USB. Furthermore, the processing device may also include a power management module for providing power to the processing device and to the connected functional modules. Furthermore, when it is necessary to output an electrical signal (for example, when the electrode needs to output electrical stimulation), it can also provide the power required to output the electrical signal, for example, to provide various types of level signals.

[0042] Figure 2 A block diagram of a processing device according to an embodiment of the present disclosure is shown as follows: Figure 2 As shown, the processing device may include a controller, a computing unit, a power management module, a basic communication unit and n (n is a positive integer) interfaces for connecting with functional modules (e.g., an acquisition module and an execution module). The interface may include a wired interface or a wireless interface, and the basic communication unit may be a functional unit supporting a wired interface and / or a functional unit supporting a wireless interface.

[0043] In an example, the acquisition module may include an acquisition module for acquiring physiological signals, such as an EEG acquisition module, a respiratory signal acquisition module, an ECG signal acquisition module, etc. The execution module may include a module for executing a certain instruction, such as an electrical stimulation module and an action execution mechanism. The present disclosure does not limit the types of acquisition modules and execution modules.

[0044] Figure 3 A block diagram showing an interface according to an embodiment of the present disclosure is shown as follows: Figure 3As shown, taking a wired interface as an example, the interface may include multiple groups of data lines connected to the functional modules, such as a power line, a ground line (GND), and differential data lines (data+, data-). The number of differential data lines may be determined according to the data protocol. For example, the minimum number of differential data lines in the USB protocol is 1 group of differential data lines.

[0045] Figure 4 A block diagram showing an interface according to an embodiment of the present disclosure is shown as follows: Figure 4 As shown, if the number of interfaces is insufficient, the interfaces can be expanded. For example, a USB interface can be expanded into multiple interfaces, for example, k (k is a positive integer greater than 1) interfaces, through hardware devices such as an expander.

[0046] In a possible implementation, the processing device can be connected to a variety of functional modules through an interface to perform various functions. For example, it can be connected to the acquisition module to obtain various physiological signals, and it can also obtain execution instructions based on the physiological signals, so as to control the execution module to perform corresponding processing through the execution instructions. As described above, the processing module in the processing device may have a computing unit, that is, a local processing resource, which can be used to process and analyze data and generate execution instructions. However, the processing device can be a portable device that is convenient for the target object to carry, and the processing capacity of the local processing resources in its processing module may be insufficient when processing complex signals (for example, a large amount of data and a complex processing method). Therefore, the processing device can be connected to the communication module, and it can determine whether the processing capacity of the local processing resources is insufficient. If the processing capacity of the local processing resources is insufficient, the acquired data can be sent to the cloud, and the computing power of the cloud can be used for processing and analysis. On the contrary, if the processing capacity of the local processing resources is sufficient, the data can be processed and analyzed by the local processing resources without sending it to the cloud.

[0047] In a possible implementation, the processing module can determine the target processing resource based on at least one of the function of the acquisition module (i.e., what kind of physiological signal the acquisition module can collect) and the amount of data collected by the acquisition module. The target processing resource can be a local processing resource, a cloud processing resource, or a combination of local processing resources and cloud processing resources. In other words, the complexity of data processing can be determined by the function of the acquisition module and the amount of data collected. If the amount of data is large and the processing method is complex, it can be sent to the cloud for processing through the communication module. If the complexity is not high and the local processing resources can complete the processing, it does not need to be sent to the cloud for processing.

[0048] Figure 5 A schematic diagram showing processing by local processing resources according to an embodiment of the present disclosure is shown as follows: Figure 5As shown, the controller can control the acquisition module to collect physiological signals through control instructions. The physiological signals collected by the acquisition module can be sent to the processing device through the interface. The controller in the processing device can call local processing resources to process and analyze the physiological signals to determine the physiological state of the target object being collected. The controller can also call local processing resources to generate execution instructions based on the physiological state, which are then sent to the execution module to control the execution module to perform corresponding processing.

[0049] In one possible implementation, the above situation in which physiological signals are processed and analyzed only through local processing resources is applicable to situations with low complexity and small data volume. The processing module can judge whether to use only local processing resources for analysis and processing based on at least one of the data volume and the functions of the functional module.

[0050] In an example, the processing module may determine the target processing resource according to the correspondence between the function of the acquisition module and the target processing resource. For example, the correspondence between the functions of different acquisition modules and the target processing resource (e.g., local processing resource, cloud processing resource, or a combination of local processing resource and cloud processing resource) may be preset.

[0051] In the example, the processing module is further used to: when the function of the acquisition module is to acquire ECG signals and / or respiratory signals, determine the target processing resource as the local processing resource of the processing module. For example, the processing module can make a judgment based on the function of the acquisition module. For example, ECG signals and respiratory signals usually have a small amount of data and a low degree of processing complexity. Therefore, if the function of the acquisition module is to acquire ECG signals and / or respiratory signals, the physiological signals acquired by the module can be processed by the local processing resources.

[0052] In the example, the processing module can also be judged by the amount of data. For example, if the function of the acquisition module is to collect EEG signals, the EEG signals are usually more complex. However, if the amount of EEG signal data collected by a certain acquisition module is not large, and the complexity of the analysis data is not high, that is, the local processing resources are capable of processing it, then the local processing resources can also be used to process the EEG signals.

[0053] Figure 6 A schematic diagram of an application of a processing device according to an embodiment of the present disclosure is shown. Figure 6As shown, the processing module can be connected to the EEG acquisition module, the respiratory signal acquisition module and the ECG acquisition module through different interfaces. In the example, the EEG acquisition module may include an 8-channel EEG acquisition module, which may include multiple electrodes arranged in the brain, and can collect 8-channel EEG signals through multiple electrodes. The present disclosure does not limit the number of channels of EEG signals. The ECG signal acquisition module can be used to collect ECG signals. For example, the ECG signal acquisition module may include multiple electrodes arranged in the heart for collecting multi-channel ECG signals. The respiratory signal acquisition module may include a respiratory sensor, which may be arranged in the respiratory tract, lungs and other positions, and can be used to collect respiratory signals, such as respiratory frequency, blood oxygen content and other signals. Multiple modules can perform simultaneous acquisition and send the collected physiological signals to the processing module through the interface. Since the function of the ECG acquisition module is to collect ECG signals, the function of the respiration acquisition module is to collect respiration signals, and the number of acquisition channels of the EEG acquisition module is small, the amount of data of the collected EEG signals is also small. Therefore, the processing module can use local processing resources to process the three signals, so as to analyze the sleep state (physiological state) of the target object, for example, light sleep or deep sleep state, or whether the sleep quality is good, etc. The present disclosure does not limit the type of sleep state and the specific method of determining the sleep state.

[0054] In one possible implementation, after determining the sleep state, local processing resources can be used to generate corresponding execution instructions according to the sleep state. For example, the functional module may also include an electrical stimulation module, which can be used to output electrical stimulation of corresponding frequency and / or intensity according to the electrical stimulation instruction. The functional module may include an electrode arranged at the position of a nerve center, which can be used to adjust the sleep state. For example, an execution instruction can be generated based on the sleep state and the current time to adjust the sleep quality of the target object. For example, if it is detected that the target object cannot enter deep sleep for a long time after falling asleep, and the current time is midnight, an execution instruction (e.g., an electrical stimulation instruction) can be generated to stimulate the nerve center, assist the target object to sleep, make it easier for the target object to enter deep sleep, and improve the sleep quality of the target object. In another example, if it is detected that the target object is in deep sleep for a long time, and the current time is dawn, an execution instruction (e.g., an electrical stimulation instruction) can be generated to stimulate the nerve center, assist the target object to sleep, and make the target object go from deep sleep to shallow sleep, so that the target object will feel less tired after waking up, thereby improving the sleep quality of the target object. The above-mentioned process of generating execution instructions based on the physiological state (sleep state) can also be performed by local processing resources.

[0055] In a possible implementation, when the functions are complex, the data volume is large, and / or the processing difficulty is high, it may be difficult to process only by local processing resources, and the target processing resources used for processing at least include cloud processing resources, that is, the target processing resources are at least cloud processing resources. The target processing resources at least include the cloud processing resources, including: the target processing resources only include cloud processing resources; or the target processing resources include a combination of cloud processing resources and local processing resources.

[0056] In a possible implementation, the processing module is further used for: when the function of the acquisition module is to acquire EEG signals, the target processing resources at least include the cloud processing resources. In the example, the processing complexity of EEG signals is relatively high, and the amount of data is usually large. When it is determined that the function of the acquisition module is to acquire EEG signals, the target processing resources can be directly determined as cloud processing resources, or a combination of cloud processing resources and local processing resources of the processing module. That is, at least part of the EEG signals are sent to the cloud for processing, and the local processing resources may or may not participate in the processing of part of the EEG signals, and the present disclosure does not limit this.

[0057] In a possible implementation, in addition to the above-mentioned determination method of the target processing resource by the function of the acquisition module, the target processing resource can also be determined by the amount of data. In the example, the amount of data is determined by the number of acquisition channels, and the processing module is further used to: when the number of acquisition channels of the acquisition module is greater than or equal to the channel number threshold, the target processing resource at least includes the cloud processing resource.

[0058] In the example, if the number of acquisition channels is large, for example, greater than or equal to a preset channel number threshold, it can be determined that the amount of data of the acquired physiological signals is large, and it is difficult to process a large amount of data through local processing resources, that is, the amount of data exceeds the processing capacity of the local processing resources. It can be sent to the cloud through the communication module, processed by the cloud, and the processing results obtained are returned to the processing device through the communication module.

[0059] In a possible implementation, in addition to the acquisition channel, the amount of data can also be determined by the sampling rate, the data amount is determined by the sampling rate, and the processing module is further used to: when the sampling rate of the acquisition module is greater than or equal to the sampling rate threshold, the target processing resource at least includes the cloud processing resource. In the example, the sampling rate of the data is large, for example, greater than or equal to the sampling rate threshold, which may also cause a large amount of data, and it is difficult for local processing resources to process a large amount of data.

[0060] In the example, when the sampling rate of the data is greater than or equal to the sampling rate threshold, the amount of data exceeds the processing capacity of the local processing resources, and can be sent to the cloud through the communication module for processing by the cloud, and the processing results obtained are returned to the processing device through the communication module.

[0061] In one possible implementation, a combination of the above-mentioned multiple conditions may be used to determine the target processing resource. For example, when the sampling rate of the data is greater than or equal to the sampling rate threshold and the number of acquisition channels is greater than or equal to the channel number threshold, the target processing resource may be determined as a cloud processing resource. For another example, when the function of the acquisition module is to acquire EEG signals, and the sampling rate of the data is greater than or equal to the sampling rate threshold, and the number of acquisition channels is greater than or equal to the channel number threshold, the target processing resource may be determined as a cloud processing resource. The present disclosure does not limit the specific conditions for determining the target processing resource as a cloud processing resource.

[0062] Figure 7 A schematic diagram showing processing by cloud processing resources according to an embodiment of the present disclosure is shown as follows: Figure 7 As shown, the controller can control the acquisition module to collect physiological signals through control instructions. The physiological signals collected by the acquisition module can be sent to the processing device through the interface. The controller in the processing device can call local processing resources to process and analyze the physiological signals. However, if the data volume and / or complexity of the physiological signals exceed the processing capacity of the local processing resources, at least part of the physiological signals can be sent to the cloud for processing through the communication module, and the cloud controls the computing power node (i.e., the cloud processing resources) to process and analyze the physiological signals to determine the physiological state of the target object being collected, and send the physiological state back to the processing device. The controller can also call the local processing resources to generate execution instructions based on the physiological state, and then send them to the execution module to control the execution module to perform corresponding processing. Of course, the execution instructions can also be directly generated by the computing power node in the cloud and sent to the processing device, and the processing device can directly send them to the execution module to control the execution module to perform corresponding processing.

[0063] In a possible implementation, as described above, when a cloud processing resource is used as a target processing resource, the local processing resource may or may not participate in the processing of the data. Further, when a cloud processing resource is used as a target processing resource, the cloud processing resource may process the physiological signal to obtain the physiological state of the target object, and send the result back to the processing device. The processing device may generate an execution instruction based on the physiological state, or the cloud processing resource may directly obtain the execution instruction and send it back to the processing device.

[0064] In a possible implementation, if the local processing resources do not participate in the processing of data, nor in the processing process of determining the execution instruction based on the physiological state, the cloud processing resources can directly obtain the execution instruction. The processing module is further used to: determine the first processing result as the execution instruction. In this case, the cloud can complete all processing processes of the physiological signal, that is, the cloud can determine the physiological state of the target object based on the physiological signal, and generate an execution instruction based on the physiological state, so that the execution instruction can be sent back to the processing device as the first processing result, and the processing device directly sends the received first processing result to the execution module as the execution instruction for controlling the execution mechanism, thereby controlling the execution module to complete the corresponding processing.

[0065] In one possible implementation, if the local processing resources do not participate in the processing of the data, but can perform the processing process of determining the execution instruction based on the physiological state, the cloud processing resources can send the physiological state back to the processing device after obtaining the physiological state. That is, the first processing result includes the physiological state of the target object determined according to the collected data, and the processing module is further used to: obtain the execution instruction according to the physiological state. In this case, the cloud can complete the processing of the physiological signal, obtain the physiological state, and send the physiological state back to the processing device as the first processing result. The processing device can determine the execution instruction based on the physiological state (i.e., the first processing result) and send it to the execution module, thereby controlling the execution module to complete the corresponding processing.

[0066] In a possible implementation, if the local processing resource participates in the processing of data but does not participate in the process of determining the execution instruction based on the physiological state, the local processing resource and the cloud processing resource can process the physiological signal respectively, and the cloud performs the process of obtaining the execution instruction based on the physiological state, and sends the execution instruction back to the processing device. The target processing resource includes a combination of the cloud processing resource and the local processing resource of the processing module, the collected data sent to the cloud includes the first collected data, and the collected data processed by the local processing resource includes the second collected data, and the processing module is further used to: process the second collected data by the local processing resource to obtain the second processing result; send the second processing result to the cloud through the communication module, so that the cloud obtains the first processing result according to the second processing result and the first collected data; determine the first processing result sent by the cloud as the execution instruction. In this case, the cloud processing resources can process the first collected data (i.e., part of the physiological signal), the local processing resources can process the second collected data (i.e., part of the physiological signal), and the second processing result of the second collected data can be sent to the cloud. The cloud processing resources can obtain the physiological state of the target object based on the second processing result and the processing result of the first collected data, thereby generating an execution instruction based on the physiological state and sending it to the processing device as the first processing result. After receiving the first processing result, the processing module in the processing device can directly send the first processing result as an execution instruction to the execution module to control the execution module to perform corresponding processing. Among them, the specific content or division method of the first collected data and the second collected data can be selected as needed. For example, data with complex processing methods and large computational complexity can be used as the first collected data and processed using cloud processing resources.

[0067] In a possible implementation, if the local processing resource participates in the processing of data and also participates in the process of determining the execution instruction based on the physiological state, the local processing resource and the cloud processing resource can process the physiological signal respectively, and the local processing resource can perform the process of obtaining the execution instruction based on the physiological state, thereby obtaining the execution instruction. The target processing resource includes a combination of the cloud processing resource and the local processing resource of the processing module, the collected data sent to the cloud includes the first collected data, and the collected data processed by the local processing resource includes the second collected data, and the processing module is further used to: process the second collected data by the local processing resource to obtain the second processing result; obtain the physiological state of the target object according to the first processing result and the second processing result; obtain the execution instruction according to the physiological state. In this case, the cloud processing resource can process the first collected data (i.e., part of the physiological signal) to obtain the first processing result, and send it to the processing device. The local processing resource can process the second collected data (i.e., part of the physiological signal) to obtain the second processing result, and can comprehensively judge the physiological state of the target object based on the first processing result and the second processing result, thereby determining the execution instruction based on the physiological state of the target object. Then, the execution instruction can be sent to the execution module to control the execution module to perform the corresponding processing. Of course, the processing module can also send the second processing result to the cloud, and the cloud determines the physiological state of the target object based on the first processing result and the second processing result, and sends it back to the processing device, and the processing module can obtain the execution instruction based on the physiological state. The present disclosure does not limit whether the physiological state is obtained through local processing resources or cloud processing resources.

[0068] In a possible implementation, if the process of generating an execution instruction is relatively complex, while the process of analyzing the physiological state is relatively simple or the amount of data is relatively small, the local processing resources can complete the processing of the collected data, but do not participate in the processing process of determining the execution instruction based on the physiological state. The processing module is further used to: process the collected data through the local processing resources to obtain the physiological state of the target object; send the physiological state to the cloud, so that the cloud determines the first processing result according to the physiological state; and determine the first processing result as the execution instruction. That is, the physiological state of the target object can be obtained through the local processing resources, but the process of obtaining the execution instruction based on the physiological state is relatively complex. Therefore, the physiological state can be sent to the cloud, and the cloud processing resources can obtain the execution instruction based on the physiological state, and send it to the processing device as the first processing result. The processing module can then send the first processing result as the execution instruction to the execution module to control the execution module to perform the corresponding processing.

[0069] Figure 8 A schematic diagram of an application of a processing device according to an embodiment of the present disclosure is shown. Figure 8 As shown, the processing module can be connected to multiple EEG acquisition modules (acquisition modules) and electrical stimulation modules (execution modules) through different interfaces, and can also be connected to the communication module. In the example, the EEG acquisition module may include a 64-channel EEG acquisition module, which may include electrodes set at multiple preset positions in the brain, and can collect 64-channel EEG signals through multiple electrodes. The present disclosure does not limit the number of channels of EEG signals. The electrical stimulation module can be used to output electrical stimulation, and the electrical stimulation module may include a 16-channel electrical stimulation module, which may include electrodes set at multiple preset positions in the brain, and can apply 16-channel electrical stimulation to preset positions of the brain through multiple electrodes. The present disclosure does not limit the number of channels of the EEG acquisition module and the electrical stimulation module.

[0070] In the example, when detecting the pathological state of the target object (for example, physiological states such as epilepsy, depression and other brain diseases), the number of data channels required is relatively large. For example, multiple 64-channel EEG acquisition modules are required to jointly collect EEG signals of multiple channels, for example, EEG signals of hundreds of data channels. If only abnormal brain signals need to be alarmed, less than 8 acquisition channels are usually required (which can be performed by local processing resources). The sampling rate of each channel can be set according to demand. For example, to collect low-frequency EEG signals, such as local field potential signals in the deep brain, a lower sampling rate of 500 to 1000Sps can be used for sampling (which can be performed by local processing resources). If the electrical activity signal of a single neuron in the brain is collected, a high sampling rate of 10k to 30kSps is required for sampling to capture the instantaneous discharge of neurons. After sampling, analog-to-digital conversion processing can also be performed. For example, the analog-to-digital conversion processing can be a 16Bit / 24Bit analog-to-digital conversion processing, and the amount of data generated per second by each channel is the sampling rate × (16Bit / 24Bit) / second.

[0071] In the example, in the process of detecting pathological conditions, the required number of acquisition channels and sampling rate are higher than the threshold, then the local processing resources cannot meet the processing requirements, and at least part of the EEG signals need to be sent to the cloud, so that they can be processed by the processing resources in the cloud. All EEG signals can be sent to the cloud, and the cloud can determine the physiological state of the target object. Part of the EEG signals can also be sent to the cloud, and the cloud processing resources and local processing resources can jointly determine the physiological state of the target object. For example, identify the type of brain disease of the target object (for example, epilepsy or depression, etc.), the location of the onset, and the severity of the disease.

[0072] In the example, the corresponding electrical stimulation can also be output according to the pathological state determined above, so as to reduce the severity of the disease. For example, brain diseases such as epilepsy and depression are related to abnormal electrical signals in the brain. Appropriate electrical stimulation can be performed on the preset position of the brain to adjust the electrical signals in the brain, thereby reducing the severity of the brain disease. For example, a comprehensive analysis can be performed based on the category, severity, and location of the brain disease identified above, so as to generate an electrical stimulation instruction (execution instruction) and transmit it to the electrical stimulation module, so that the electrical stimulation module applies electrical stimulation of appropriate frequency and intensity to the appropriate position of the brain, thereby adjusting the electrical signals in the brain and reducing the severity of the brain disease. The electrical stimulation instruction can be generated by the cloud, that is, the cloud can generate a corresponding electrical stimulation instruction based on the physiological state and send it back to the processing device, and the processing device can send the electrical stimulation instruction to the electrical stimulation module, thereby outputting the corresponding electrical stimulation. The electrical stimulation instruction can be generated by a local processing resource, that is, the local processing resource can generate a corresponding electrical stimulation instruction based on the physiological state, and send the electrical stimulation instruction to the electrical stimulation module, thereby outputting the corresponding electrical stimulation.

[0073] In the example, after applying electrical stimulation, the EEG signal of the brain can be collected again through the EEG acquisition module to determine the pathological state after applying electrical stimulation, and then electrical stimulation can be further applied according to the pathological state at this time, so as to further adjust the electrical signal of the brain and reduce the severity of the disease. The above process can be executed repeatedly, and the generated electrical stimulation instructions are continuously feedback-adjusted through the collected EEG signals, so as to continuously generate more appropriate electrical stimulation to adjust the electrical signal of the brain, so that the electrical signal of the brain returns to a normal state or a near-normal state, and the severity of the brain disease is reduced.

[0074] Fig. 9 A schematic diagram of an application of a processing device according to an embodiment of the present disclosure is shown. Fig. 9 As shown, the processing module can be connected to multiple EEG acquisition modules (acquisition modules) and actuators (execution modules) through different interfaces, and can also be connected to the communication module. In the example, the EEG acquisition module may include a 64-channel EEG acquisition module, which may include electrodes arranged at multiple preset positions in the brain, and can collect 64-channel EEG signals through multiple electrodes. The present disclosure does not limit the number of channels of EEG signals. The actuator may include actuators such as prostheses and robots, which can be used to perform corresponding actions based on execution instructions. The present disclosure does not limit the number of channels of the EEG acquisition module.

[0075] In the example, when detecting the action requirement state of the target object (i.e., the target object's demand for performing a certain action), multiple data channels may be required, for example, multiple 64-channel EEG acquisition modules are required to jointly collect EEG signals of multiple channels. Each channel can be connected to electrodes arranged at different positions of the brain, so that EEG signals at various positions of the brain can be obtained, so that the action requirement state of the target object can be comprehensively analyzed. For example, the action requirement state of the target object can be analyzed based on a variety of information such as the location where the EEG signal is generated, the amplitude of the EEG signal, the frequency of the EEG signal, and the phase of the EEG signal. In the example, the processing process of the analysis and processing is more complicated and the amount of data is also larger. The EEG signal can also be sent to the cloud and processed by cloud processing resources. At least part of the above-mentioned EEG signal can be sent to the cloud, and the action requirement state can be obtained based on the EEG signal through cloud processing resources, or a combination of cloud processing resources and local processing resources. Local processing resources or cloud processing resources can generate corresponding execution instructions based on the action requirement status, such as walking, grabbing and other instructions. After obtaining the execution instructions (receiving the execution instructions sent by the cloud, or generating the execution instructions through its own local processing resources), the processing module can send the action instructions to the execution agency, so that the corresponding action can be executed.

[0076] According to the processing device of the embodiment of the present disclosure, it can determine whether the collected data needs to be sent to the cloud for processing based on at least one of the data volume and the function of the collection module. If necessary, the collected data that is difficult to process locally can be sent to the cloud for processing through the communication module, or the collected data can be processed together with local processing resources and cloud processing resources, thereby increasing the available processing resources, improving the processing efficiency and real-time performance of EEG signals, and helping to circumvent the limitations of local processing resources and develop more functional signal collection and processing functions. It is also beneficial to obtain richer EEG signals and provide data support for brain research.

[0077] It can be understood that the above-mentioned various device embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment without violating the principle logic. Due to space limitations, the present disclosure will not repeat them. It can be understood by those skilled in the art that in the above-mentioned method of the specific implementation method, the specific execution order of each step should be determined according to its function and possible internal logic.

[0078] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A processing device, characterized in that: include: processing module, multiple interfaces for connecting functional modules and communication module, The processing module is used for: Determine the function of a functional module connected to the processing module through the interface, the functional module including a collection module; Determining target processing resources for processing the collected data according to the function of the collection module and the amount of collected data transmitted by the collection module to the processing module through the interface; In a case where the target processing resources include at least cloud processing resources, sending at least part of the collected data to the cloud via the communication module; Wherein, the processing module is further used for: In the case where the function of the acquisition module is to acquire electrocardiographic signals and / or respiratory signals, the target processing resource is determined to be a local processing resource of the processing module; When the function of the acquisition module is to acquire EEG signals, the target processing resources at least include the cloud processing resources.

2. The device according to claim 1, characterized in that The functional module also includes an execution module, and the communication module is further used for: Receiving a first processing result sent by the cloud; The processing module is also used for: According to the first processing result, an execution instruction is obtained and sent to the execution module.

3. The device according to claim 1, characterized in that The amount of data is determined by the number of acquisition channels. The processing module is further configured to: When the number of acquisition channels of the acquisition module is greater than or equal to a channel number threshold, the target processing resources at least include the cloud processing resources.

4. The device according to claim 1, characterized in that The amount of data is determined by the sampling rate, The processing module is further configured to: When the sampling rate of the acquisition module is greater than or equal to a sampling rate threshold, the target processing resources at least include the cloud processing resources.

5. The device according to any one of claims 1, 3-4, characterized in that: The target processing resources at least include the cloud processing resources, including: The target processing resources include only cloud processing resources; or The target processing resources include a combination of cloud processing resources and local processing resources.

6. The device according to claim 2, characterized in that The first processing result includes a physiological state of the target object determined according to the collected data, The processing module is further configured to: The execution instruction is obtained according to the physiological state.

7. The device according to claim 2, characterized in that The processing module is further configured to: The first processing result is determined as the execution instruction.

8. The device according to claim 2, characterized in that The target processing resource includes a combination of a cloud processing resource and a local processing resource of the processing module, the collected data sent to the cloud includes first collected data, and the collected data processed by the local processing resource includes second collected data, The processing module is further configured to: Processing the second collected data by the local processing resource to obtain a second processing result; Obtaining a physiological state of a target object according to the first processing result and the second processing result; The execution instruction is obtained according to the physiological state.

9. The device according to claim 2, characterized in that The target processing resource includes a combination of a cloud processing resource and a local processing resource of the processing module, the collected data sent to the cloud includes first collected data, and the collected data processed by the local processing resource includes second collected data, The processing module is further configured to: Processing the second collected data by the local processing resource to obtain a second processing result; Sending the second processing result to the cloud through the communication module, so that the cloud obtains the first processing result according to the second processing result and the first collected data; The first processing result sent by the cloud is determined as the execution instruction.

10. The device according to claim 2, characterized in that The processing module is further configured to: Process the collected data through local processing resources to obtain the physiological status of the target object; sending the physiological state to a cloud, so that the cloud determines the first processing result according to the physiological state; The first processing result is determined as the execution instruction.

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