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    • 专利摘要:
    Neural sensing channel and neural sensing procedure. The present invention describes a sensing channel (10000) for the acquisition, digitalization and processing of neuronal signals captured by an intracranial electrode (20000) and the associated procedures. The proposed channel (10000) comprises means and mechanisms for the compression of real-time data comprising discrimination and compression by parameterizing a neuronal signal (1) to obtain a simplified representation of the detected action potentials, ie a signal neuronal tablet (15401). The present invention is part of the field of physical technologies and more, in particular, in the field of information technology and communications applied to bioengineering. (Machine-translation by Google Translate, not legally binding)
    公开号:ES2564999A1
    申请号:ES201431253
    申请日:2014-08-25
    公开日:2016-03-30
    发明作者:Manuel DELGADO RESTITUTO；Alberto RODRÍGUEZ PÉREZ；Ángel Rodríguez Vázquez
    申请人:Consejo Superior de Investigaciones Cientificas CSIC；Universidad de Sevilla；
    IPC主号:
    专利说明:

    NEURONAL
    D E S C R I P C I O N
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    OBJECT OF THE INVENTION
    The present invention discloses a neuronal sensing channel whose function is the acquisition, digitalization and processing of neuronal signals captured by an intracranial micro-electrode and associated procedures.
    In particular, the sensing channel object of the present invention comprises means for real-time data compression that allow a simplified representation of the detected action potentials.
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    BACKGROUND OF THE INVENTION
    Various types of neuronal data acquisition and transfer systems are known, such systems have the task of monitoring and transferring the electrical activity captured from a plurality of intracranial micro-electrodes.
    In general, these systems comprise a plurality of sensing channels, also referred to as bioelectric sensors, which individually amplify and condition the neuronal signal captured from each of the intracranial micro-electrodes. These analog signals are subsequently subjected to a scanning process and, once digitized, are processed in the digital domain and coded with a view to their subsequent transmission, preferably by wireless means.
    As an integral part of the digital processing, the implantable systems for acquiring and transferring known neuronal data comprise means and procedures for the compression of information with a view to reducing the rate of sending data abroad and thus reducing the power consumption of the system by as a whole
    The current trend is to integrate all the electronics included in a system
    Implantable acquisition and transfer of neural data in a single micro-electronic circuit, referred to as SoC from the acronym in English, "System-on-Chip", preferably on a silicon substrate for reasons of cost.
    5 In known embodiments of this type of implantable neuronal data acquisition and transfer systems, the sensing channels included in said system do not include all the necessary functionality. For example, in documents US2009157141 "Wireless neural recording and stimulating system" and US8090674 and title "Integrated system and method for multichannel neuronal recording with spike / LFP 10 separation, integrated aid conversion and threshold detection", the sensing channels comprise a header analogue amplification and signal conditioning, but do not contemplate the implementation of means for data conversion and compression. In another case, documents US2010106041 "Systems and methods for multichannel wireless implantable neural recording” and US2012302856 "Distributed, 15 minimally-invasive neural interface for wireless epidural recording”, describe implantable neural data acquisition and transfer systems where the conversion of Data is performed locally on the sensing channels. However, none of these proposals contemplates the use of data compression techniques.
    20 On the other hand, document US2013090706 "Methods and associated neural prosthetic devices for bridging brain areas to improve function" proposes means and procedures for the compression of the digitized outputs of eight sensing channels. According to this proposal the system transmits frames of data formed by a preamble and an eight-bit digital word, each of which is associated with a channel.The logical value of said bits depends on whether or not an action potential has been detected in the corresponding channel. a possible realization is briefly mentioned which comprises a compression stage in which, the processor used for data compression, unique to the entire system, must use an operating frequency 28 times higher than the output data rate of the converters of data.
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    Consequently, it is clear that none of the embodiments known in the prior art have a system and / or a method of acquisition and transfer of neural data that has applicable data compression mechanisms when multiple sensing channels are available.
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    The present invention solves the aforementioned problems. In accordance with the present invention, a sensing channel is provided for an implantable neuronal data acquisition and transfer system comprising means for amplifying and filtering the captured neural signal from an intracranial micro-electrode, means for automatically adjusting the levels maximum voltage and the frequency band of the signal conditioned by said amplification and filtering means; means for converting the conditioned neuronal signal from analog to digital domain; means for detecting in real time the appearance of neuronal impulses; means for characterizing the time-voltage morphology of the detected impulses to thereby compress the captured information; and, preferably, means for temporarily storing said information.
    Additionally, the present invention may comprise various methods for the compression of data such as, for example, the extraction of main components (PCA), the calculation of parameters derived from a Hanning filtering, or the conformation with model waves (in English, "wavelet analysis"), in a preferred configuration of the present invention, the compression of the neuronal impulses is carried out in real-time and uses techniques of linear approximation to sections in the digital domain.
    According to the described means, a sensing channel according to the present invention offers four modes of operation:
    i. Configuration mode, by which the operation parameters of the different means included in said channel are defined and the boundary conditions for the execution of the other modes of operation are established.
    ii. Calibration mode, by which the deviations of the means involved in the amplification and filtering of the neuronal signal captured from an intracranial electrode are automatically corrected.
    iii. Senal Tracking Mode, through which it is acquired,
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    conditions, filters and digitizes the brain activity acquired by the sensing channel. This mode of operation is, in fact, the only one available in most bioelectric sensors reported in the literature.
    iv. Data Compression Mode, whereby the action potentials contained in the neural signal captured by the sensing channel are detected and processed. In accordance with the present invention, the sensing channel remains in a dormant state and, therefore, does not transmit any information abroad, as long as it does not detect a neuronal impulse. In this way, the activity of the data compression means is determined by the events that occur. Once an action potential has been detected, the compression means operate at the same speed as the data rate of the data converter included in the sensing channel.
    The sensing channel, according to the present invention, offers a modular solution that facilitates the integration of implantable neuronal data acquisition and transfer systems with an arbitrary number of intracranial micro-electrodes and simplifies data serialization procedures. In addition, despite the superiority in means, procedures and functionality over conventional solutions, the sensing channel does not incur a substantial increase in area and power consumption. In fact, according to an example of realization, the complete sensing channel occupies an area of 0.016 mm2 and the dissipation of the specific circuitry for data compression, when active, is of the order of 200 nW.
    In particular, the present invention discloses a neuronal sensing channel comprising:
    • connection means to at least one electrode;
    • means for conditioning and / or acquiring the signal captured by means of the electrode that have as its output a neuronal signal;
    • an analog-digital converter that transforms the neuronal signal into a digitized neuronal signal;
    • means of data transmission; Y
    • at least one local data processor
    in which the local data processor comprises a data compression module and
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    wherein said data compression module comprises a first signal discrimination sub-module that discriminates the sections of the neuronal signal or the digitized neuronal signal that fall within a given discrimination range and a second sub-module of digital compression comprising means of 5 parameterization of at least part of the digitized neuronal signal having as output a compressed signal. Preferably, said compressed signal is a binary and / or serial signal.
    In particular embodiments of the present invention, the parameterization means 10 parameterize the part of the digitized neuronal signal that has not been discriminated by the signal discrimination sub-module.
    The expression “parametrization” should be interpreted in its broadest sense, that is, to convert a set of data into a smaller amount of data that are representative of that set. For example, it is possible to parameterize a set of values taken in different time spaces in a linear manner by means of the lowest value data, the highest value data, and the time that has elapsed between the taking of said data. In this way, less information is used to define the data set. Likewise, there are different parameterization techniques in the art that could be applied analogously to the invention without departing from the inventive concept described here.
    As for the signal discrimination sub-module, it has an upper threshold and a lower threshold that define the aforementioned discrimination range. When the amplitude of a signal received by the signal discrimination sub-module is greater than the threshold 25 higher or lower than the lower threshold, said discrimination sub-module confirms the existence of an action potential or, in other words, parts of the neuronal signal that have information of interest for the sensing channel and that contain information that must be transmitted. These thresholds can be analog thresholds (the discrimination sub-module operates with analog signals before the analog-digital converter) or 30 digital thresholds (the discrimination sub-module operates with digital signals after the analog-digital converter).
    In the case in which the signal discrimination sub-module operates in the digital domain, said signal discrimination sub-module has at its input the neuronal signal
    digitized and the digital compression sub-module provides the input signal of the signal discrimination sub-module at its input. However, in other embodiments of the present invention it is contemplated that the signal discrimination sub-module operates in the analog domain for which the neuronal signal will be arranged at its input and its output 5 will be connected to the digital analog converter which, after your conversion to the digital neuronal signal, proceed to transmit this signal to the digital compression sub-module for parameterization.
    In a particular embodiment of the present invention, the parameterization means are 10 linear parameterization means in sections and, preferably, the signal compressed by means of the parameterization means contains amplitude values and temporal values. These values correspond to the coordinates in the time-tension plane of significant milestones for the linear representation to sections of action potentials such as voltage peaks or threshold crossings.
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    In a particular embodiment, said milestones may be amplitude values. In this case, a first amplitude value (for example, a maximum peak), a second amplitude value (for example, a minimum peak) and a time-dependent value, or temporal value, may be the elapsed time. between the maximum peak and the minimum peak. 20 However, the amplitude values do not necessarily have to be amplitude peaks, in fact, in embodiments of the present invention, some of the amplitude values may be one of the threshold values that define the discrimination range.
    In addition, in a particularly preferred embodiment, the temporal values of a linear parameterization along sections of an action potential are calculated by counting the pulses of a clock associated with the sensing channel.
    Finally, the compressed signal can be sent to a signal reception device by wireless means for which the sensing channel must comprise 30 wireless transmission means or be connected to a wireless transmission means external to the channel.
    On the other hand, the present invention discloses a method of sensing neuronal activity comprising the steps of:
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    a) capture, by means of an electrode, of the bioelectric signals generated by the neuronal activity of a subject;
    b) acquisition and conditioning of the signal captured by an electrode in stage a);
    c) digitalization, by means of an analog-digital converter, of at least part of the signal acquired and conditioned in step b);
    d) discrimination of at least part electrical signals of stages b) or c) that are in a previously defined range of discrimination;
    said method also comprising a step e) in which the digitized signal obtained after the completion of steps c) or d) is compressed by means of a parameterization giving as output a compressed signal. This parameterization can be a real-time parameterization.
    Preferably, the discrimination of the electrical signals of step d) can be performed in the analog or digital domain.
    More preferably, the parameterization of step e) is a linear parameterization to sections in the time-tension plane. In a particular embodiment, the parameterization provides as input the digitized signal and the compressed signal comprises values of amplitude (such as, for example, maximum and minimum peak values) and at least one time-related value (such as, for example, the time elapsed between the maximum and minimum peak values) that approximate the morphology in the time-tension plane of the detected action potential.
    The amplitude values do not necessarily have to be amplitude peaks, in fact, in embodiments of the present invention, some of the amplitude values may be one of the threshold values that define the discrimination range. In this case, the time dependent value may be a value associated with the time elapsed to the amplitude value object of the parameterization and the discrimination range 30
    In particular, the time dependent value is calculated by the pulse count of a clock of the channel of the sensing channel.
    In addition, the present invention contemplates the possibility of including a stage f) in which
    The compressed signal is sent to at least one device external to the sensing channel.
    DESCRIPTION OF THE DRAWINGS
    5 To complement the description that is being made and in order to help a better understanding of the characteristics of the invention, according to a preferred example of practical realization thereof, a set of said description is attached as an integral part of said description. Drawings where the following has been illustrated and not limited to:
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    Figure 1 shows the structure of a sensing channel for the capture, digitalization and processing of neuronal signals according to the present invention.
    Figure 2 shows an exemplary embodiment of a data processor of type 15 comprised of the sensing channel of Figure 1.
    Figure 3 shows an example of embodiment of the data compression module of the type included in the data processor of a sensing channel, in accordance with the present invention.
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    Figure 4 shows the linear representation to sections in the time-tension plane of a neuronal action potential.
    Figures 5a, 5b and 5c show a flow chart of an example of the data compression procedure 25 that could be carried out in an example of a compression module of a sensing channel according to the present invention. The flowchart does not fit completely on a single page, so, for clarity, a first phase has been represented in Figure 5a, as well as a second phase, then the first phase, in Figure 5b and a third phase, following the second phase, in Figure 5c.
    Figure 6a shows an example of a neuronal signal obtained by an electrode as well as a graphic representation of said compressed neuronal signal.
    Figure 6b shows examples of possible compressed signals obtained by a device according to the present invention.
    PREFERRED EMBODIMENT OF THE INVENTION
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    Figure 1 shows a sensing channel (10000) for the capture, digitalization and processing of neuronal signals, in accordance with the present invention. Said sensing channel (10,000) comprises a low noise amplifier (11,000) (called LNA, for its acronym in English, "Low Noise Amplifier") to amplify the potential difference between the electrical signals coming, on the one hand, from a reference electrode (30,000) and a biopotential electrode (20,000); a circuit (12,000) to estimate artifacts due, for example, to alterations in the impedance of the interface between tissue and electrode or to the application of electromodulation therapies, that can potentially contaminate the signal captured by the low noise amplifier 15 (11000); a variable gain amplification element (13000) to adjust the
    voltage levels of the signal provided by the low noise amplifier (11000) once the signal generated by the circuit (12000) is subtracted for the estimation of artifacts; an analog-to-digital converter (14000) (called ADC), "Analogue-to-Digital Converter") to digitize the signal provided by the variable gain amplification element (13000); a local data processor (15000) that identifies the operation mode of the sensing channel (10000), configures the parameters of the low noise amplifier (11000) and the variable gain amplification element (13000), processes the data digitized by said converter (14000) and transfers the results abroad; and, additionally, it can comprise a reference unit that can, for example, fulfill functions for the calibration of the band pass transfer characteristic of the header of a sensing channel (10000).
    The expression "header" should be interpreted as the subset of elements of a sensing channel comprising a low noise amplifier (11000) and a variable gain amplification element (13000).
    As for the variable gain amplification element (13000) this may be comprised of a programmable gain amplifier (13001) (of the known type
    as PGA, for its acronym in English, "Programmable Gain Amplifier") and an offset cancellation loop (13002) (called OCL, for its acronym in English, "Offset Cancellation Loop") to eliminate the components in direct current at the analog-to-digital converter input (14000) caused by imbalances at inputs 5 of both the variable gain amplification element (13000) and the low noise amplifier (11000).
    It is important to note that the configuration shown in figure 1 of connection between the low noise amplifier (11000) and circuit (12000) for the estimation of artifacts 10 form a conditioning circuit and sensor of a neuronal signal whose function is, mainly, the to adapt the signal so that it can be used in the rest of the procedure. In some embodiments of the present invention, some type of filtering can be included to aid in the conditioning of said neuronal signal.
    According to the present invention, the output signal of the biopotential electrode (20,000) is the electrical response captured by said biopotential electrode (20,000), which serves as an interface between the tissue whose electrical activity is to be monitored and the channel of sensed (10000). The output signal of the reference electrode (30000) is a voltage extracted from said reference electrode 20 (30000) that offers a lower input impedance than the biopotential electrode
    (20000) and which serves as a reference for the operation of the low noise amplifier (11000) and the circuit for the estimation of artifacts (12000).
    In accordance with a preferred embodiment of the present invention, the sensing channel (10000) includes protection elements against electrostatic discharges (called ESD, for its acronym "electrostatic discharge") at the points of connection with the electrode of biopotential (20000) and the reference electrode (30000).
    The means and procedures associated with the calibration of a sensing channel 30 (10,000) in accordance with the present invention can be activated by changing
    status of an internal signal, so that the inputs of the low noise amplifier (11000) are disconnected from the biopotential (20000) and reference electrodes (30000), and are connected to analog calibration signals with preset characteristics. These means and calibration procedures are
    widely known in the prior art.
    In accordance with the present invention, both the low frequency cutoff frequency and the high frequency cutoff frequency of the header of the sensing channel 5 (10000), as the gain of the variable gain amplification element (13000)
    they are programmable in the sense that they can be modified electronically, for example, by means of a configuration module and / or by an instruction reading module that will be explained in greater detail by referring to Figure 2.
    Additionally, according to the present invention, the output of the analog-to-digital converter (14000) is a digitized signal (14001) that has the shape of a vector and is stored in an internal register of said analog-to-digital converter (14000 ). With each update of said register, corresponding to a new digitalization of the output signal of the variable gain amplifier (13000), the
    15 converter (14000) emits an end pulse (14002), representative of the end of the conversion process.
    As examples of possible digital inputs (10001) to the sensing channel (10000), inputs such as a periodic pulse train can be highlighted to sequence the
    20 operation of said sensing channel (10000) (i.e. clock pulses), a programming input for sequential loading of a configuration vector, an enable signal to execute said configuration vector once loaded, a vector of data to select and activate the sensing channel (10000), etc.
    25 At the output, the sensing channel (10000) can have a serial data output (10002) of the sensing channel (10000). As will be seen below, the structure of said serial data output signal (10002) may depend on the mode of operation specified in the configuration vector.
    In accordance with the present invention, a sensing channel (10000) offers four modes of operation, in accordance with the type of instruction received through the configuration vector:
    i. Configuration Mode, by which the parameters of
    operation of the different means included in the sensing channel (10000) and the boundary conditions for the execution of the other modes of operation are established.
    ii. Calibration mode, by which they adjust automatically
    5 the band pass transfer characteristics of the header of a
    sensing channel (10000).
    iii. Senal Tracking Mode, through which brain activity acquired through the sensing channel is acquired, conditioned, filtered and digitized (10,000).
    10 iv. Data Compression Mode, by which they are detected and processed
    the action potentials contained in the neuronal signal captured by the sensing channel (10000). Unlike other techniques available for the characterization of the time-tension morphology of neuronal action potentials (combination of base functions, extraction of
    15 PCA main components - from English, "Principal Component
    Analysis ”-, calculation of parameters of a Hanning filter, or the conformation with model waves - of the English,“ wavelet analysis ”-), the present invention proposes, by way of example, the use of techniques of linear approximation to sections in time real.
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    Figure 2 shows the block diagram of an example of a local data processor (15000) comprised in a sensing channel (10000). Said local data processor (15000) comprises: an instruction reading module (15100); a parameter store (15200) where the configuration parameters are archived
    25 (15101) of the sensing channel (10000) contained in the instructions received by
    said instruction reading module (15100); a configuration module (15300); a data compression module (15400); and a data transmission module (15500) that has various outputs of the sensing channel (10000) and generates, among others, an enable signal.
    The digitized output (14001) and the final pulse (14002) provided by the analog-digital converter (14000) of the sensing channel (10000), among others, represent some of the most representative inputs to the local data processor (15000).
    According to the present invention, the instruction reading module (15100) can have an input for a vector for the activation of the sensing channel (10000). In another aspect, the instruction reading module (15100) breaks down the contents of an example configuration vector, on the one hand, identifying the mode of operation of the sensing channel (10000) from among the four possible modes, on the other , identifying the qualifiers (15102) of said commands and, on the other, identifying the associated configuration parameters (15101).
    In accordance with the present invention, the qualifiers (15102) are particularized depending on the mode of operation of the sensing channel (10000) being different for the Configuration Mode, Calibration Mode or Data Compression Mode (151021) (illustrated in Figure 2). Senal Tracking Mode has no qualifiers (15102) associated. Likewise, only the frames associated with Configuration and Calibration operation modes contain configuration parameters 15 (15101) in that, for example, the Data Compression Mode (151021) is
    an instruction for enabling said module.
    According to one aspect of the present invention, the configuration module (15300) is enabled when the instruction reading module (15100) identifies a command linked to the configuration mode in which case an enable signal (15301) is issued. of the configuration module (15300). In any other mode of operation, said configuration module (15300) remains disabled. The purpose of the configuration module (15300) is the validation of the correct writing in the parameter store (15200) of the parameters contained in the configuration vector. Once the instruction reading module (15100) records in the parameter store (15200) the configuration parameters (15101) associated with the received command, the configuration module (15300) receives a validation signal (15302) and conforms a vector representative of the configuration mode and a specific pattern that informs the correct writing of said configuration parameters 30 (15101) in the parameter store (15200).
    In particular embodiments of the present invention, the local data processor (15000) comprises an additional calibration module (not shown) that is enabled when the instruction reading block (15100) identifies a command
    linked to the Calibration Mode that can be activated and deactivated by means of signals from the instruction reading module (15100) in a similar way to the data compression module (15400).
    5 The purpose of the calibration module is the automatic programming of the set of values of cut-off frequencies and variable gain so that the passing band of the header of the sensing channel (10000) only includes the spectral content of the signal being monitored and that the amplification level provided by the variable gain amplifier (13000) is adjusted to the desired value.
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    An example of automatic cut-off frequency calibration can be performed using a closed loop system that uses a frequency synthesizer as a reference unit. The programming of the tone frequency generated by said synthesizer is done through representative digital words. The generated tone 15 is matched to the desired value for the cutoff frequency. The calibration process of said cutoff frequency begins after the time necessary to stabilize the feedback loop. In the case of the amplification level, the automatic adjustment can be made by monitoring, for a period of time, the signal amplitude peaks provided by the variable gain amplifier.
    On the other hand, the purpose of the data compression module (15400) is the extraction and processing of some of the characteristics of brain activity with a view to reducing the bandwidth of the signal transmitted by the sensing channel 25 (10000) . Consequently, the data compression module (15400) is responsible for
    the detection and characterization of the time-tension morphology of the neuronal action potentials captured by the head of the sensing channel (10000). The transfer of information from the data compression module (15400) through the compressed output (15401) only occurs when it has been detected and the extraction of the characteristics of an action potential has been completed. Under any other circumstance, said compressed output (15401) remains inactive. Consequently, the data transfer is based on events, linked to the presence of action potentials in the captured neural signal, and is not carried out continuously. This implies, in comparison with the operation mode of
    Senal Tracking, a considerable reduction in the rate of data sent from a sensing channel (10,000) in accordance with the present invention.
    In Figure 3 it can be seen that the detection of the action potentials is performed by comparison with thresholds and the extraction of the characteristics of said potentials is based on obtaining linear approximations to sections in the time-tension plane. Accordingly, the data compression module (15400) monitors the digitized signal (14001) generated by the analog-digital converter (14000) comprised in the sensing channel (10000) and identifies the presence of 10 neuronal action potentials by detecting the moments in which said sequence exceeds the band of values between a higher threshold
    (15563) and a lower threshold (15564) that define a range of discrimination. At the moment when the detection of an action potential occurs, the data compression module (15400) activates a real-time procedure by means of which
    15 the tension values and the time intervals necessary to construct the linear representation are determined by sections of the detected potential.
    In short, it can be understood that the present invention has, on the one hand, a discrimination sub-module in charge of verifying the presence of action potentials 20 in the captured neuronal signal, which happens when said signal exceeds a range of discrimination defined by an upper threshold (15563) and a lower threshold
    (15564); and said discrimination sub-module can be digital, as explained above, or analog, by means of prior processing to the analog-digital converter (14000). That is, the discrimination sub-module is responsible for
    25 eliminate signals that do not correspond to action potentials from the compression process (or discriminate). With this objective, two thresholds are defined that define said discrimination range and that can be previously configured by the user or come defined by automatic calibration techniques known in the art.
    On the other hand, the present invention has a digital compression sub-module in which a digital compression of data is performed by compression techniques such as, for example, by linear approach to sections. Said sub-modules are contained in the block of linear approximation to sections (15570).
    Additionally, in a register (15580) the data of the linear approach to sections made by said block of linear approach to sections (15570) is temporarily stored; and two structurally identical entities, a positive threshold adaptation structure (15501) and a negative threshold adaptation structure (15502), which when enabled allow dynamic adjustment of said threshold tensions against potential variations in the background of signal noise captured by the header of the sensing channel (10000).
    The data compression module (15400) can additionally comprise a configuration sub-module for the activation and establishment of the operating parameters of the different elements of the structure.
    Also according to the present invention, as inputs to the linear approach block section (15570), in addition to the aforementioned external values of the 15 threshold voltages (15563, 15564) and the digitized signal (14001), a SPD parameter (155700) that indicates the number of clock periods comprising the estimated duration of a neuronal action potential, said parameter can be used by the linear approach block section (15570) for the detection and linear approach to sections of action potentials.
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    As for the threshold adaptation structures (15501, 15502), they operate, respectively, with the positive and negative values of the digitized signal (14001) provided by the analog-digital converter (14000). As shown in Figure 3, the distinction between positive values (14011), and negative values (14012) of said digitized signal (14001), is made by a digital comparator (14030) and a first demultiplexer (14010 ). The same output of said digital comparator (14030) is used to distinguish, with the help of a second demultiplexer (14020), both the positive conversion end pulse (14021) and the negative conversion end pulse (14022) linked, respectively , to the positive values (14011), and the negative values (14012) of said digitized signal (14001).
    According to the present invention, both structures for adapting threshold voltages (15501, 15502) can operate simultaneously. Except for the input data (positive values (14011) and negative values (14012)) and
    corresponding outputs (the upper threshold (15563) and the lower threshold (15564)), there are no structural or operational differences between both adaptation structures of the threshold voltages (15501, 15502) so, for simplicity, it will only be described the detail of the adaptation structure of the positive threshold voltage 5 (15501).
    As Figure 3 shows, the positive threshold voltage adaptation structure (15501) comprises: a programmable pulse counter (15520) whose period, controlled by a "UPR" parameter, is configurable by the user; a digital accumulator 10 formed by an adder (15530) and a register (15540); and an output stage formed by a conformation block (15550) and an adder (15560) The operation of the positive threshold voltage adaptation module (15501) is like After this structure is enabled, each new value of the positive values (14011) of the digitized signal (14001) accumulates in the register (15540), while the value stored in the pulse counter is increased by one programmable (15520) at the end of the positive conversion end pulse (14021) associated with said positive value (14011) When the count of said positive conversion end pulses (14021) reaches an equal value two raised to UPR (2UPR) , the pulse counter p Programmable (15520) triggers a DUMP signal (15522) that dumps the contents of the register (15540) into the forming block (15550). The length of both the adder (15530) and the register (15540) is N + UPR, where N is the size of the input vectors of the positive values (14011). Then, after a clock cycle, the contents of said register (15540) are canceled and the described accumulation and counting procedure begins again. The conformation module 25 (15550) extracts from the register (15540) the N bits between the N + UPR-2 position
    and the UPR-2 position, which essentially amounts to shifting the register content (15540) in two positions (multiplying by four) and taking the most significant N bits. The result of the extraction is saved in another register included in the conformation module itself (15550). According to state document 30 of the technique "Michael Rizk and Patrick D. Wolf (2009). Optimizing the automatic selection of spike detection thresholds using a multiple of the noise level. Med Biol Eng Comput (2009) 47: 955- 966 ”and" R. Quian Quiroga, Z. Nadasdy and Y. Ben-Shaul (2004). Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering. Neural Computation 16, 1661-1687 ”, is simple
    operation performed in the conformation module (15550) provides an estimate of the value of the upper threshold voltage (15563) necessary for the detection of action potentials. In order to allow a user-controlled fine adjustment of the optimum value of the upper threshold voltage (15563), a digital adder 5 (15560) adds or subtracts the shaped digital vector (15562) provided by the forming module (15550) by an amount (15561) configurable that has been transferred to the adaptation structure of the upper threshold voltage (15563) from a configuration block. The transfer of the final value of the upper threshold voltage (15563) to the linear approach block section (15570) for the detection and linear approximation of the action potentials only occurs in the absence of action potentials.
    As examples of outputs of the linear approximation block in sections, an amplitude value vector (15572), a temporal value vector 15 (15573), a threshold vector (15574) composed of threshold voltages can be mentioned
    upper (15563) and lower (15564), and / or an approach termination signal (15575), on the other hand, another of the outputs may be an operating pulse (15571) to indicate that the linear approach block to sections (15570) are in use and, finally, after the passage of said outputs through the register (15580), the compressed signal (15401) is generated. The meaning of these values will be explained in greater detail by referring to Figure 4.
    In accordance with another aspect of the present invention, Figure 4 illustrates the real-time representation procedure in the time-tension plane of the action potentials performed by the linear approach block section (15570). Said representation procedure is activated each time the digitized signal (14001) generated by the analog-digital converter (14000) exceeds the range of discrimination between an upper threshold (15563) and a lower threshold (15564). Although Figure 4 shows a case in which the action potential begins with a transition towards values above the upper threshold (15563), this aspect is in no way limiting the present invention, and transitions in the opposite direction are equally susceptible to representation using the same procedure.
    In particular, this figure shows a graphic representation of the neuronal signal (1), in a discontinuous line, as well as a representation of an example of linear approximation to sections, in continuous line, for said neuronal signal (1).
    10
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    In an example of realization and assuming biphasic action potentials (such as the one illustrated in Figure 4) for which there are two peaks, one above and one below the range of discrimination defined by the upper (15563) and lower threshold tensions (15564), the representation in the time-tension plane made by the linear approach block section (15570) comprises two amplitude values (a first voltage value Vp1 (155721) and a second voltage value Vp2 (155722)) and three temporal values (A1 (155731), A2 (155732) and A3 (155733)). In this exemplary embodiment, the two amplitude values correspond to the peak values of the action potential detected by the linear approach block (15570), where Vp1 (155721) is the value of the amplitude of the first peak (independently of whether it is positive or negative) and Vp2 (155722) the amplitude value of the second peak. On the other hand, the three temporal values report the duration of the following intervals:
    1. A first interval A1 (155731) elapsed from the detection of the action potential (when the neuronal signal (1) exceeds one of the thresholds) until the moment of the first voltage value Vp1 (155721).
    2. A second interval A2 (155732) elapsed between the instant of the first voltage value Vp1 (155721) and the instant of the second voltage value Vp2 (155722).
    3. A third interval A3 (155733) elapsed between the instant of the second voltage value Vp2 (155722) and the end of the action potential (that is, until the signal is again in the discrimination range). This termination is determined either by the crossing of the action potential by the upper threshold (15563) or lower (15564), closer to the second peak value Vp2 (155722) or when the time limit defined by an SPD value ( 155700) configurable by the user and transferred to the linear approach block to sections (15570) of the action potentials from a configuration block. Said SPD value (155700) is expressed in clock cycles and is preferably between 2 ms and 3 ms, according to neurophysiological studies. In the case where the action potential reaches the time limit defined by
    An SPD value, without having previously crossed a threshold of the discrimination range, the interval A3 (155733) takes the value:
    A3 = SPD - A1 - A2.
    5
    In another example of realization and assuming single-phase action potentials with a single peak, either above or below the range of discrimination defined by the upper (15563) and lower (15564) threshold tensions, the representation in the time plane- The tension made by the linear approach block section (15570) 10 also comprises two amplitude values (a first tension value and a second tension value) and three temporal values. In this case, the first voltage value would be the value of the amplitude of the peak detected by the linear approximation block (15570) (regardless of whether it is positive or negative) and the second voltage value would be the value of the tension threshold that marks the return of the potential of 15 action to the discrimination range (which is the same threshold whose crossing originated the detection of the action potential). On the other hand, the three temporal values provide information on the duration of the following intervals:
    twenty
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    1. A first interval elapsed from the detection of the action potential to the first voltage value.
    2. A second interval elapsed between the instant of the first voltage value and the crossing of the action potential by the threshold closest to said first voltage value.
    3. A third interval elapsed between the moment of the crossing of the action potential by the threshold closest to the first voltage value and the finalization of the defined action potential, such as in the case of biphasic action potentials, by a defined time limit by an SPD value (155700) configurable by the user and transferred to the linear approach block to sections (15570) of the action potentials from a configuration block.
    In all cases, whether biphasic or monophasic action potentials, the time intervals A1 (155731), A2 (155732) and A3 (155733) are calculated by counting the pulses of a clock enabled in the sensing channel 10000.
    Additionally, some of the auxiliary signals and their determination from the graph shown are shown in Figure 4. In particular, the approach finalization signal (15575) is shown, this is a pulse that is executed during a certain time to signal that the analysis has been completed, a pulse of 5 operation (15571) indicating that it is currently being performed an analysis and a pulse of detection of action potentials (15576) that indicates the presence of an action potential and this pulse is maintained at 1 as long as the neuronal signal (1) remains outside the range of the discrimination range and is 0 while The neuronal signal (1) remains within the range of discrimination.
    10
    Also in accordance with another aspect of the present invention, the linear approach block (15570) of the action potentials operates in real time so that both the amplitude value vector (15572), and the temporal value vector (15573) are available at the end of the detected action potential 15, an aspect that said linear approach block to sections (15570) communicates by issuing an approximation end signal (15575), as shown in Figure 3. The procedure by which the linear approach block to sections (15570) of the action potentials dynamically determines the parameters of the representation in the time-tension plane, is described in the flow chart of Figure 5. The Duration of the procedure for each action potential detected is SPD (155700). Although figures 5a, 5b and 5c show, by way of example, a case in which the detection of the action potential is caused by a crossing with the upper threshold (15563), the procedure for a crossing with the lower threshold (15564) is completely dual, so it is not detailed for simplicity.
    As shown in the flowchart of Figures 5a, 5b and 5c, three phases can be distinguished in the procedure for the real-time determination of the parameters for the linear representation to sections of action potentials. A first phase (5000) (shown in Figure 5a) comprises the time interval from the detection of the action potential to the return to the discrimination range defined by the upper (15563) and lower (15564) thresholds (crossing by the upper threshold (15563) in the present example). In this phase the parameters A1, Vp1 associated with the position of a first voltage peak of the action potential are calculated
    detected. In a second phase (6000) (shown in Figure 5b) it is determined whether the action potential detected is monophasic or biphasic. In the first case, monophasic potential, the values of the parameters Vp2, A2 and A3 are provided and the linear characterization procedure is concluded. Otherwise, 5 biphasic potential, proceed to the third phase (7000) of the procedure (shown in Figure 5c). In said third phase (7000), the position of a second voltage peak of the detected action potential is determined and the values of the pair A2, Vp2 associated to said second peak are provided. Likewise during the third phase (7000) the parameter A3 is calculated, whether a return to the discrimination range 10 occurs or not, and the linear characterization procedure is concluded.
    As for the first phase (5000), in this phase it starts (5001), arranging as inputs the values of the digitized signal (14001) which, hereafter referred to as TR_DATA and is analyzed for values in which TR_DATA is found , in accordance with the example in Figure 5, on the positive threshold (15560) that will be referred to as VTH +. Subsequently, values are given to the variables A1, SPK, and PEAK (5002). Where A1, as explained above, corresponds to the time interval until the first peak value Vp1 is reached (maximum value of the action potential in Figure 5); SPK corresponds to an auxiliary variable that indicates that the linear approach block section (15570) is in operation and corresponds to the operating pulse (15571); and PEAK corresponds to an auxiliary variable that indicates that the block of linear approximation to sections (15570) is detecting a peak.
    25
    Once these values have been given, the first peak value is defined as the current value of TR_DATA and a CNT counter (5003) is started. A new TR_DATA data (5004) is then analyzed. Subsequently, a first decision operator (5007) analyzes whether the new value of TR_DATA is below the positive threshold VTH +. If so, it is considered that the first peak value has already been reached, so the auxiliary variable PEAK (5009) is reset, the peak value Vp1 and the interval A1 (5010) are stored and the second phase is carried out ( 6000) after giving values to a set of control variables (5011).
    If it is determined in the first decision operator (5007) that the value of TR_DATA is still above the positive threshold VTH +, a second decision operator (5008) is taken in which it is determined whether the value of TR_DATA is less than the amplitude value Vp1. If this is the case, the counter (5005) is increased by adding 5 one to the CNT variable and a new TR_DATA data (5004) is returned and, otherwise, A1 (5006) is increased in the value of the CNT counter plus 1, the CNT variable is reset to zero and the peak value Vp1 (5003) is redefined as the current value of TR_DATA.
    10 After completing the first phase (5000), a second phase (6000) is carried out, whose task is to distinguish between monophasic and biphasic action potentials and, in the first case, provide the parameters Vp2, A2 and A3 of the approximation Linear to sections. These actions will be shown with reference to Figure 5b.
    15 Initially the value of A2 (6001) is started, for which it is given the remaining value of the CNT variable, of the first phase (5000). Subsequently, the values of Vp2 and the counter (6002) are assigned for which the current value of the digitized signal TR_DATA is assigned to the variable Vp2 and the CNT counter is reset to zero. Then, the next value of the digitized signal TR_DATA (6003) is analyzed.
    20 By means of a third decision operator (6006) it is analyzed whether this value of TR_DATA is below the negative threshold value that will be referred to as VTH-. If so, the auxiliary variable PEAK is assigned a value of one (6007) and the third stage (7000) is continued.
    25 If the value of TR_DATA is above the threshold VTH-, a fourth decision operator (6008) analyzes whether the value of A2 + CNT is lower than the SPD parameter (which is a pre-configured parameter, such and as explained above). If this value is lower, using a fifth decision operator (6009), it is analyzed if the value of the current digitized signal TR_DATA is greater than the
    30 Vp2 value, if so, the CNT counter (6004) is increased by one and the next value of the digitized signal TR_DATA (6003) is analyzed and, otherwise, the value of A2 is increased by the value of the CNT counter plus one (6005) and the variable Vp2 is updated with the value of TR_DATA and the CNT counter is reset to zero (6002).
    If in the fourth decision operator (6008) it is determined that the value of A2 + CNT is greater than the SPD parameter, it is determined that the digitized signal corresponds to a monophasic potential and the corresponding values of the linear approximation 5 to sections Vp2 are stored. , A2 and A3 (6010) in which the value of Vp2 corresponds to the threshold value closest to the peak (ie the upper threshold value (15563)). Subsequently, the SPK value (6011) is reset to indicate that the compression procedure is finished and the END pulse or END pulse is emitted and the procedure is terminated (6012).
    10
    As for the third stage (7000), which is explained by referring to Figure 5c and, in which it has already been determined that there is a negative peak value Vp2 and, therefore, a potential for biphasic action, the value is initiated of A2 and the CNT counter is reset
    (7001). The value of A2 is defined as the remainder of the CNT variable of the second phase (6000), the current value of the digitized signal TR_DATA is assigned to the peak value
    Vp2 and the CNT counter is reset to zero.
    Subsequently, the next TR_DATA value (7002) is analyzed. Using a sixth decision operator (7005) if the current value of TR_DATA is greater than 20 negative threshold, the auxiliary variable PEAK (7006) is reset, the values of Vp2 and A2 (7007) are stored, the value of A3 is stored ( 7008) which will be the current CNT value, a zero is assigned to the SPK variable (7009) to indicate the completion of the process and the procedure is completed (7010).
    25 If in the sixth decision operator (7005) it is determined that the current value of TR_DATA is less than the negative threshold, a seventh decision operator (7011) is determined in which it is determined whether the value of A2 + CNT is less than the value of the pre-configured parameter SPD, if so, is carried out to an eighth decision operator (7012) in which it is determined whether the value of TR_DATA is greater than the value 30 negative peak Vp2. If TR_DATA is greater than the negative peak value, the CNT counter (7003) is increased by one and the next TR_DATA value is analyzed
    (7002), otherwise A2 is increased in the value of the CNT counter plus one (7004) and the variable Vp2 is updated with the value of TR_DATA and the CNT counter is reset to zero (7001).
    Finally, if in the seventh decision operator (7011) it is determined that the value of A2 + CNT is greater than the value of the pre-configured parameter SPD, the data of Vp2 and A2 (7013) are stored, A3 is assigned to SPD-A1-A2 (7014), a 5 zero is assigned to the SPK variable (7015) to indicate the completion of the process and the procedure is completed (7016).
    In another aspect of the present invention, when the linear characterization procedure is concluded by sections of each action potential detected by the block of linear approximation to sections (15570), the different calculated parameters and the value of the thresholds VTH + and VTH- they are turned into a register (15580) at the time of the pulse of completion of the procedure. Said register (15580) forms a compressed signal (15401), which preferably comprises the values of Vp1, Vp2, A1, A2, A3, VTH + and VTH-, derived from the procedure of Figures 5a, 5b and 5c. When said compressed signal (15401) is ready to be sent through the data transmission module (15600), the register (15580) of the data compression module (15400) triggers a transmission pulse so that the transmission means proceed to send it to an external device to the sensing channel.
    20 By way of illustration, Figure 6a shows a typical sequence of compressed signals (15401) together with the underlying neuronal signal. In particular, the differences between the neuronal signal (1) captured by the electrode and what is intended to be sent in the compressed neuronal signal (3) can be observed. Although, of said compressed neuronal signal (3) only some representative parameters are stored.
    25
    It should be noted that the compressed signal (15401) remains inactive as long as an action potential is not detected. In case of a detection, the volume of data transmitted (essentially formed by the parameters of the linear characterization by sections, as exemplified for a first potential (21) and a second potential (22) in Figure 6 ) is much lower than if the detail of the action potentials was transmitted. Together, the data compression module (15400) provides a reduction in the signal bandwidth to be transmitted that can reach several orders of magnitude.
    As an example, Figure 6b shows a digital representation of an example of a compressed signal (15401). In particular, the signals for the first potential (21) and the second potential (22) of Figure 6a are shown. From this figure it can be seen that the parameters considered as representative of the compressed neuronal signal 5 (3) are Vp1 (155721), Vp2 (155722), A1 (155731), A2 (155732) and A3 (155733).
    In accordance with the present invention, the data transmission module (15500) comprised of the local data processor (15000) multiplexes the data outputs of the 10 sensing channel (10000). When the data transmission module (15500) receives the data download pulse, said data transmission module (15500) receives, as a minimum, the compressed signal (15401) and transfers it to the serial output port (10002) of the local data processor (15000).
    In a possible application of the present invention, a plurality of sensing channels (10,000) will be used in an implantable neuronal activity acquisition and transfer system as the head of a brain-machine interface (BMI), "brain -machine interface ”) According to this possible application example, the system will acquire the signals coming from the motor cortex and send them abroad for further processing with a view to controlling automata or other mechanisms that allow to alleviate some type of motor deficiency of the patient .
    In another possible example of the application of the present invention, a plurality of sensing channels (10,000) will be used in an implantable neuronal activity acquisition and transfer system together with an electrical neuromodulation mechanism for the prediction and treatment of epileptic attacks. In this application, the activation of neurostimulators will be dictated by the analysis of the signals provided by the implantable neuronal activity acquisition and transfer system.
    30
    It is important to emphasize that the concepts and specifications described in the present invention are general and are not strictly linked to any particular type of standard, neither in relation to the acquisition of signal nor in relation to wireless communications.
    Throughout this specification, the term "comprises" and its derivatives should not be construed in an exclusionary or limiting sense, that is, should not be construed to exclude the possibility that the element or concept to which it refers includes 5 Additional elements or stages.
    权利要求:
    Claims (24)
    [1]
    R E I V I N D I C A C I O N E S
    10
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    1. Neural sensing channel (10000) of the type comprising:
    • connection means to at least one electrode (20000);
    • means for conditioning and / or acquiring the signal captured by means of the electrode (20000) which have as its output a neuronal signal (1);
    • an analog-digital converter (14000) that transforms the neuronal signal (1) into a digitized neuronal signal (14001);
    • means of data transmission (15500); Y
    • at least one local data processor (15,000)
    wherein the local data processor (15000) comprises a data compression module (15400) characterized in that said data compression module (15400) comprises a first signal discrimination sub-module that discriminates the signal sections neuronal (1) or of the digitized neuronal signal (14001) that are included in a certain discrimination range and a second digital compression sub-module comprising means for parameterization of at least part of the digitized neuronal signal having as output an Compressed signal (15401).
    [2]
    2. Sensing channel (10000), according to claim 1, characterized in that the parameterization means parameterize the part of the digitized neuronal signal that has not been discriminated by the signal discrimination sub-module.
    25 3. Sensing channel (10000), according to claim 1, characterized in that the
    Compressed signal (15401) is a binary signal.
    [4]
    4. Sensing channel (10000), according to claim 1, characterized in that the compressed signal (15401) is a serial signal.
    30
    [5]
    5. Sensing channel (10000), according to claim 1, characterized in that the signal discrimination sub-module has an upper threshold (15563) and a lower threshold (15564) that define the discrimination range.
    5
    10
    fifteen
    twenty
    25
    30
    35
    [6]
    6. Sensing channel (10000), according to claim 1, characterized in that the signal discrimination sub-module is a digital signal discrimination sub-module.
    [7]
    7. Sensing channel (10000), according to claim 6, characterized in that the signal discrimination sub-module has at its input the digitized neuronal signal (14001).
    [8]
    8. Sensing channel (10000), according to claim 7, characterized in that the digital compression sub-module has at its input the output signal of the signal discrimination sub-module.
    [9]
    9. Sensing channel (10000), according to claim 6, characterized in that the signal discrimination sub-module has at its entrance the neuronal signal (1).
    [10]
    10. Sensing channel (10000), according to claim 1, characterized in that the parameterization means are linear parameterization means in sections.
    [11]
    11. Sensing channel (10000) according to claims 5 and 10, characterized in that the compressed signal (15401) by means of the parameterization means comprises a first voltage value (155721), a second voltage value (155722) of the digitized signal (14001) and a first time interval defined by the time elapsed between the moment in which the neuronal signal (1) exceeds one of the thresholds until the instant of the first voltage value (155721).
    [12]
    12. Sensing channel (10000), according to claim 11, characterized in that the compressed signal (15401) by means of the parameterization means comprises a second time interval defined by the time elapsed between the instant of the first voltage value (155721) and the instant of the second tension value (155722).
    [13]
    13. Sensing channel (10000), according to claims 11 or 12, characterized in that the compressed signal (15401) by means of the parameterization means comprises a third time interval defined by the time elapsed between the instant of the second voltage value ( 155722) and the moment in which the neuronal signal (1) is again within the range of discrimination.
    5
    10
    fifteen
    twenty
    25
    30
    35
    [14]
    14. Sensing channel (10000), according to revindication 12, characterized in that the compressed signal (15401) by means of the parameterization means comprises a third time interval defined by the subtraction of the first interval and the second time interval at a value predetermined.
    [15]
    15. Sensing channel (10000), according to claim 11, characterized in that at least one of the first and second voltage values corresponds to the value of an upper threshold (15563) or a lower threshold (15564) defining said upper thresholds and lower the range of discrimination.
    [16]
    16. Sensing channel (10000), according to claims 11 to 14, characterized in that the time intervals are calculated by means of the pulse count of a clock associated with the channel of the sensing channel (10000).
    [17]
    17. Sensing channel (10000), according to claim 1, characterized in that it comprises wireless transmission means of the compressed signal (15401).
    [18]
    18. Neural activity sensing method in a sensing channel (10000) comprising the steps of:
    a) capture, by an electrode (20,000), of the bioelectric signals generated by the neuronal activity of a subject;
    b) acquisition and conditioning of the signal captured by an electrode in stage a) resulting in a neuronal signal (1);
    c) digitalization, by means of an analog-digital converter (14000), of at least part of the signal acquired and conditioned in step b);
    d) discrimination of at least part of the electrical signals of stages b) or c) that are in a previously defined range of discrimination;
    characterized in that it also comprises a stage e) in which the digitized signal (14001) obtained after the realization of steps c) or d) is compressed by means of a parameterization giving as output a compressed signal (15401).
    [19]
    19. Procedure, according to claim 18, characterized in that the discrimination of the electrical signals of step d) is carried out in the analog domain.
    [20]
    20. Procedure, according to revindication 18, characterized in that the discrimination of the electrical signals of step d) is performed in the digital domain.
    [21]
    21. Procedure, according to revindication 18, characterized in that the
    5 parameterization of stage e) is a linear parameterization to sections.
    [22]
    22. Procedure, according to revindication 21, characterized in that the
    Parameterization has as input the digitized signal (14001), as well as the compressed signal (15401) comprises, at least, a first amplitude value, (155721), a
    10 second amplitude value (155722) and a time dependent value.
    [23]
    23. Procedure, according to revindication 22, characterized in that at least one of the first or second amplitude value corresponds to the value of an upper threshold (15563) or a lower threshold (15564) defining said upper and lower thresholds the
    15 discrimination range.
    [24]
    24. Procedure, according to revindication 22, characterized in that the value
    time-dependent is calculated by the pulse count of a clock on the channel of the
    sensing channel
    twenty
    [25]
    25. Procedure, according to revindication 18 characterized in that it comprises a step f) in which the compressed signal (15401) is sent to at least one device external to the sensing channel (10000).
    25 26. Procedure, according to revindication 25, characterized in that said delivery of
    The compressed signal (15401) is made by wireless means of communication.
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    同族专利:
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    ES2564999B1|2017-03-24|
    WO2016030560A1|2016-03-03|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US7853329B2|1998-08-05|2010-12-14|Neurovista Corporation|Monitoring efficacy of neural modulation therapy|
    US7187968B2|2003-10-23|2007-03-06|Duke University|Apparatus for acquiring and transmitting neural signals and related methods|
    WO2008042900A2|2006-10-02|2008-04-10|University Of Florida Research Foundation, Inc.|Pulse-based feature extraction for neural recordings|
    US20110307079A1|2010-04-29|2011-12-15|Board Of Trustees Of Michigan State University, The|Multiscale intra-cortical neural interface system|US20190053725A1|2017-08-15|2019-02-21|Qualcomm Incorporated|Neural signal data capture|
    法律状态:
    2021-09-29| FD2A| Announcement of lapse in spain|Effective date: 20210929 |
    优先权:
    申请号 | 申请日 | 专利标题
    ES201431253A|ES2564999B1|2014-08-25|2014-08-25|NEURONAL SENSING CHANNEL AND NEURONAL SENSING PROCEDURE|ES201431253A| ES2564999B1|2014-08-25|2014-08-25|NEURONAL SENSING CHANNEL AND NEURONAL SENSING PROCEDURE|
    PCT/ES2015/070622| WO2016030560A1|2014-08-25|2015-08-13|Neural sensing channel and neural sensing method|
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