Apparatus and method for real-time closed-loop brain stimulation

Abstract

To provide brain stimulation techniques that utilize: a brain state monitoring and stimulating component having at least one processor and memory; and a digital signal processing unit configured and operable to predict at least one brain state about to occur in a brain of a treated subject based on data or signals indicative of at least one brain state of the treated subject.SOLUTION: A digital signal processing unit 12 can be configured to generate, based on predicted at least one brain state 13s / 13s', stimulation data 14s for adjusting, regulating or triggering brain stimulation signals 15s applied to the brain of a treated subject. In some embodiments, at least one processor 11u, memory 11m, and the digital signal processing unit are embedded in a single hardware device.SELECTED DRAWING: Figure 1A

Description

[Technical Field]

[0001] The present invention relates generally to the field of brain stimulation. [Background technology]

[0002] Transcranial magnetic stimulation (TMS) therapy can be used to treat a variety of mental / psychological, neurological (CNS), and / or physiological disorders. TMS implementation is based on Faraday's law of electromagnetic induction, which allows brief magnetic pulses directed at specific brain regions of a subject to induce depolarization of targeted neurons in brain tissue. When TMS is performed in a repetitive manner, known as rTMS, or with other pulse patterns, it can produce effects such as long-term potentiation or long-term depression of neuroplasticity. The availability of this technology is dramatically changing the practice of psychiatry and neurology and the perception of psychiatric disorders.

[0003] TMS systems typically follow an open-loop paradigm, in which preprogrammed stimulation parameters are used throughout a treatment session, regardless of the subject's brain's chemical and electrical state. Closed-loop TMS systems monitor the subject's brain state through sensor feedback and adjust stimulation parameters accordingly. Such closed-loop systems can automatically adjust the duration, timing, intensity, and stimulation pattern of applied stimulation pulses to achieve the desired therapeutic outcome. Monitoring and analyzing brain activity requires precise and accurate measurements of features such as instantaneous phase, amplitude, and spectrum to accurately determine stimulation parameters. Therefore, closed-loop brain stimulation applications require powerful and fast processing resources, including high-temporal resolution acquisition systems with unbuffered data transmission and fast signal analysis capabilities, making real-time closed-loop brain stimulation applications currently impractical or too expensive.

[0004] A. Gharabaghi ​​et al. ("Coupling brain-machine interfaces with cortical stimulation for brain-state dependent stimulation: enhancing motor cortex excitability for neurorehabilitation," Frontiers in Human Neuroscience, 2014) reported a study of brain-state dependent stimulation (BSDS) combined with tactile feedback provided by a robotic hand orthosis, in which TMS of the motor cortex and tactile feedback to the hand were controlled by sensorimotor desynchronization during motor imagery, within a brain-machine interface (BMI) environment. BSDS significantly increased the excitability of the stimulated motor cortex in both healthy and post-stroke subjects, an effect not observed with non-BSDS protocols. This feasibility study suggests that closing the loop between brain states, cortical stimulation, and tactile feedback may offer a novel neurorehabilitation strategy for stroke patients lacking residual hand function, and further investigation is warranted in larger cohorts of stroke patients.

[0005] C. Zrenner et al. ("Real-time EEG-defined excitability states determine efficacy of TMS-induced plasticity in human motor cortex", Brain Stimulation 2018, 11(2):374-389) describe state-dependent, electroencephalographically evoked transcranial magnetic stimulation (EEG-TMS) applied to the negative and positive peaks of the EEG of the sensorimotor μ-rhythm in healthy subjects using a real-time digital signal processing system with millisecond resolution. In this study, corticospinal excitability was indexed by the amplitude of motor-evoked potentials in hand muscles.

[0006] U.S. Patent Publication No. 2016 / 0220836 describes an apparatus and method for phase-locking brain stimulation to electroencephalographic rhythms, which improves the accuracy, specificity, and effectiveness of non-invasive brain stimulation devices by timing brain stimulation pulses to occur synchronously with naturally occurring brain rhythms measured at the patient's scalp for treating various neurological and psychiatric conditions. The apparatus and method aim to improve non-invasive brain stimulation techniques by time-locking the onset of brain stimulation to the phase of naturally occurring rhythmic oscillations of brain activity, which can be recorded by electroencephalography (EEG). The apparatus and method perform real-time signal analysis of a specified EEG rhythm, extract frequency-domain phase information to estimate the next occurrence of a desired EEG rhythm phase, and trigger brain stimulation pulses to precisely coincide with this predicted EEG phase. Summary of the Invention

[0007] To date, brain state-dependent noninvasive brain stimulation (BSDS) has been primarily used for two main applications: stroke recovery, in which TMS pulses are triggered by beta-band event-related asynchrony during motor imagery tasks (see, e.g., Gharabaghi ​​et al., 2014); and phase-locking in the alpha band during repetitive TMS protocols (see, e.g., Zrenner et al., 2018). However, these applications rely on predictive models that predict current brain states based on immediately preceding brain rhythms. This treatment scheme is necessary for such applications due to processing and computational delays that prevent real-time identification of the target's true instantaneous brain state. Furthermore, prior art brain stimulation techniques rely on communication between the brain state analysis and acquisition / recording units via relay components, which significantly impacts timing limitations.

[0008] Prior art systems are not only slow, but can only trigger TMS pulses for stimulation without changing their characteristics and / or pattern in real time. That is, they cannot adjust the stimulation signal while it is being applied to the treatment target. This limitation is due in part to the limited processing and data communication speeds of state-of-the-art brain stimulation systems and the fact that the applicator devices used by them are not designed to allow for real-time adjustment of the applied signal, but rather to apply stimulation signals with fixed shapes and temporal patterns.

[0009] The present application provides a fast, real-time, and accurate closed-loop brain stimulation system configured and operable to continuously, periodically, or intermittently receive / read brain state signals / data indicative of at least one brain state measured or induced in a treatment subject, analyze the brain state signals / data to extract various features therefrom, predict at least one likely brain state in the treatment subject's brain based on the brain state signals / data and / or the extracted features, and generate stimulation parameters and / or timing data for use by a brain stimulation applicator. The generation of the brain stimulation parameters and / or timing data used by the brain stimulation applicator can be based entirely or at least in part on the predicted at least one brain state. Optionally, the generation of the brain stimulation parameters and / or timing data used by the brain stimulation applicator is based entirely or at least in part on the brain state signals / data and / or the features extracted therefrom.

[0010] In a possible application, the brain stimulation system generates brain stimulation parameters and / or timing data based entirely or at least in part on at least one brain state that has previously occurred in the brain of the treatment subject, without predicting a future brain state that is about to occur. For example, the brain stimulation system can be configured to apply one or more brain stimuli (e.g., a TMS pulse, a series of TMS pulses, visual stimuli, auditory stimuli, or any combination thereof) to the treatment subject, read brain state signals / data indicative of at least one brain state measured in the treatment subject in response to the applied brain stimuli, and generate new brain stimulation parameters and / or timing data for use in the brain stimulation applicator based at least in part on the measured brain state signals / data and / or on one or more other brain state signals / data previously measured in the treatment subject in response to one or more previously applied past brain stimuli.

[0011] The brain stimulation applicator is configured to receive the brain stimulation parameter and / or timing data and, based thereon, generate and apply one or more stimulation signals (e.g., by a TMS coil and / or current stimulation electrodes) to at least one region / area of ​​the brain to be treated and / or apply one or more sensory stimuli (e.g., by a sound / sound generator / player, a 2D / 3D display / holographic device, and / or a sensory / tactile stimulator transducer). The brain stimulation applicator is configured to receive new brain stimulation parameter and / or timing data from the system and thereby adjust in real time the stimulation signals applied according to the newly received brain stimulation parameters and / or timing data, thereby enabling the brain stimulation system to adapt in real time the stimulation signals being applied to current brain state data / signals measured by the system.

[0012] The brain stimulation system is configured to repeatedly perform these steps to continuously stimulate at least one brain region / area to be treated for a determined period of time. Optionally, such continuous brain stimulation is performed using a brain state measurement algorithm or according to predefined limits. In this manner, the stimulation signal applied by the brain stimulation applicator can be dynamically adjusted / synchronized to features of the received / read brain state signal / data measurement to obtain a biofeedback brain stimulation process. Thus, in some embodiments, the brain stimulation signal generated by the brain stimulation applicator is modulated / adjusted by at least one feature extracted from the brain state signal / data to dynamically adjust the frequency, amplitude, stimulation pattern, current polarity, or precise location of the brain stimulation signal.

[0013] Prior art brain stimulation systems often use transistor-transistor logic triggers that limit output stimulation to a single stimulation pattern. The brain stimulation system of the present application provides more complete integration with a brain stimulation applicator (e.g., a TMS and / or sensory stimulator), thereby enabling the application of different stimulation patterns and real-time adaptation of the applied stimulation signal in response to various brain state signals / data (e.g., EEG signals) measured by the system, instead of applying a single output pattern each time a pre-set target signal is identified.

[0014] To achieve the desired stimulation in at least one brain region / area of ​​interest, the data processing and analysis, feature extraction, brain state prediction, and / or stimulation signal parameter / timing / shaping data generation steps must be performed very quickly and accurately. These goals are achieved in the brain stimulation system of the present application using one or more processors (central processing units - CPUs) configured and operable by a real-time operating system (RTOS, e.g., Abassi, AMOS, NI Linux Real-Time) to operate a digital signal processing (DSP) unit, all of which are implemented entirely in hardware (e.g., FPGA / GPU). Therefore, the application of the brain stimulation system disclosed herein is suitable for system-on-chip (SoC) implementation, which can contribute to improved performance and miniaturization of the system's geometric dimensions.

[0015] To further improve the processing speed of the brain stimulation system, one or more processors and / or DSP units are configured to exchange data using a fast communication protocol (FCP). Additionally or alternatively, one or more processors and / or DSP units are configured for direct memory access (DMA), allowing processing units of the system to read and write data from / in memory (e.g., RAM, SDRAM, FLASH) without interrupting the ongoing operation of the system.

[0016] Another advantage of the present application is its ability to communicate directly with brain state measurement equipment (e.g., EEG units / systems, fMRI, cognitive task applications) without intermediate components. Conventional brain stimulation systems typically utilize software modules (i.e., supplied by the manufacturer) of the measurement equipment to communicate measured brain state data, for example, by streaming over a network or other API software tools. However, such software modules typically add another layer of complexity and uncertainty and increase the latency of receiving brain state data / signals measured by the brain stimulation system. The brain stimulation system disclosed herein is configured to communicate directly with brain state measurement equipment without an intermediate software layer of communication, thereby significantly reducing the acquisition time of measured brain state data / signals by the system and improving the system's ability to control and shape the stimulation signals applied in response to therapeutic targets.

[0017] Hardware implementations of the brain stimulation systems disclosed herein can substantially reduce processing and computation time, e.g., by tens of milliseconds, compared to conventional brain stimulation systems, thereby allocating substantially more time and resources to predicting likely future brain states and forming appropriate stimulation signals accordingly in real time, significantly improving the accuracy of the predictive models and the effectiveness of the applied stimulation signals.

[0018] In some embodiments, the brain state signals / data are received from electroencephalography (EEG) electrodes, and the brain stimulation system is configured to perform spectral analysis to identify dominant frequency bands of the brain state signals / data and perform time-frequency analysis. The system can then extract various features related to the frequency (e.g., frequency data, phase data, amplitude data) and / or time (amplitude, time delay, etc.) domains and / or perform brain connectivity analysis of either the frequency or time domain features of the brain state signals / data, and accordingly perform brain state prediction and generate respective brain stimulation parameters and / or timing data.

[0019] Alternatively or additionally, the brain stimulation system is configured and operable to use transcranial alternating current stimulation (tACS) signals / data received from a tACS device and / or electrodes used to induce a desired brain state in at least one region / area of ​​the treated brain. The tACS signals / data can be similarly processed by the brain stimulation system to predict one or more brain states about to occur in the treated brain and generate corresponding operating parameters and / or timing data for operating a brain stimulation applicator to apply respective stimuli to the subject accordingly.

[0020] Because the predictive power of a model degrades over time, reducing the latency of a brain stimulation system can improve the performance of a predictive model. The fast, real-time implementation of the brain stimulation system disclosed herein may expand the system's usefulness for treating subjects previously classified as unsuitable for closed-loop BDSS. For example, in Zrenneret et al. (2018), the algorithm used for phase locking has a limitation that phase locking of alpha-band EEG is only possible if the power in the alpha band is at least 25% of the total power of the measured signal. This limitation excludes 50% of potential subjects and precludes phase locking to other bands for all subjects. However, this limitation of prior art systems can be overcome through the real-time data processing of the brain stimulation system of the present application. This can also be addressed by including additional algorithms that are inapplicable to prior art problem solvers due to limitations in their data / signal acquisition and processing capabilities, such as, but not limited to, backpropagation (BP) feedback models.

[0021] In particular, a major problem with low-power EEG signals is the accuracy of filtering applied to them (which is exacerbated by the fact that EEGs are not purely sinusoidal). Therefore, in some embodiments, BP feedback is used to improve the brain stimulation system's ability to reliably process and respond to such low-power brain state measurement signal situations by using feedback from offline assessments of stimulation phases and / or EEG signals measured in response to previously applied stimulation signals to correct for inaccuracies in the online algorithms. In this way, BP feedback can be used to determine which of the measured brain state values ​​most accurately indicates the time to apply and / or adjust the stimulation signal by the applicator.

[0022] The brain stimulation systems disclosed herein generally include a brain state monitoring and stimulation component including at least one processor, memory, and digital signal processing unit embedded in a single integrated circuit device (e.g., SoC) configured and operable to receive and process data / signals, and configured to generate stimulation data indicative of one or more brain states of a treated subject and, based thereon, for adjusting, modulating, or triggering real-time brain stimulation signals applied to the treated subject's brain. The digital signal processing unit, in some embodiments, is configured to predict at least one brain state occurring or about to occur in the treated subject's brain, and, based on the predicted at least one brain state, for generating stimulation data for adjusting, modulating, or triggering real-time brain stimulation signals applied to the treated subject's brain. Optionally, but preferably in some embodiments, the at least one processor and / or digital signal processing unit is operated utilizing a real-time operating system embedded in the integrated circuit device.

[0023] A stimulus generator electrically connected / coupled to the integrated circuit device can be used to generate brain stimuli to be applied to the brain of the treated subject based on stimulus data generated by the digital signal processing unit of the integrated chip device. The stimulus generator can be electrically connected to the integrated chip device by conductors (e.g., using a serial or parallel bus data communication protocol) and / or wirelessly connected to the integrated circuit device for wireless data communication therewith (e.g., WiFi, Zigbee, Bluetooth, etc.). In some embodiments, the stimulus generator is configured to adjust at least one of the shape, timing, and / or frequency of the generated brain stimuli in real time based on the stimulus data generated by the digital signal processing unit of the integrated chip device. In this way, the brain stimulation system can rapidly acquire and process data / signals indicative of at least one brain state of the treated subject, generate and transmit corresponding stimulus data to the stimulus applicator, and instantaneously generate and adjust the brain stimuli to be applied to the brain of the treated subject.

[0024] One inventive aspect of the subject matter disclosed herein relates to a brain stimulation system including a brain state monitoring and stimulation component having at least one processor and memory, and a digital signal processing unit configured and operable to predict at least one brain state about to occur in the brain of a treated subject based on data / signals indicative of at least one brain state of the treated subject (e.g., EEG electrodes, tACS electrodes, fMRI, and / or associated with a cognitive task performed by the treated subject). The digital signal processing unit is configured to generate stimulation data for adjusting, modulating, or triggering brain stimulation signals applied to the brain of the treated subject based on the predicted at least one brain state and / or data / signals. Optionally, but preferably in some embodiments, the at least one processor, memory, and / or digital signal processing unit are embedded in a single hardware device (e.g., an SoC). In some embodiments, the at least one processor and / or digital signal processing unit operates using a real-time operating system.

[0025] Optionally, but preferably in some embodiments, the digital signal processing unit is configured and operable to repeatedly receive data / signals indicative of a current brain state of the subject, predict at least one brain state that is about to occur in the subject based on the received data / signals, and generate stimulation data / signals for applying a new stimulation cycle, thereby providing a closed-loop brain stimulation mechanism that can adjust the brain stimulation signal applied to the subject according to the generated stimulation data. Optionally, adjusting the brain stimulation signal includes adjusting the brain stimulation signal according to the generated stimulation data.

[0026] In some embodiments, the at least one processor and the digital signal processing unit are configured to exchange data using a high-speed communication protocol. Alternatively or additionally, the at least one processor and the digital signal processing unit are configured and operable to read and write data from / into the system's memory via a direct memory access scheme. In some embodiments, to further improve performance, the at least one processor, the digital signal processing unit, and their memory are implemented on a single integrated circuit chip device, for example, as an SoC (e.g., at least one processor is implemented in an FPGA and the digital signal processing device is implemented in a GPU on the same semiconductor substrate).

[0027] In some embodiments, the digital signal processing unit comprises at least one digital filter module configured and operable to remove interferences from the data / signals indicative of the at least one brain state of the subject, and / or at least one analysis module configured and operable to process the received data / signals indicative of the at least one brain state of the subject and perform at least one of a time-frequency analysis and a spectral analysis thereof, and / or at least one feature extraction unit configured and operable to extract a feature indicative of the at least one brain state of the subject and / or its spectral and / or time-frequency analysis data from the received data / signals indicative of the at least one brain state of the subject, and / or at least one prediction module configured and operable to predict at least one brain state about to occur in the brain of the subject based on the received data / signals and to generate stimulation data based on the predicted at least one brain state.

[0028] The digital signal processing unit, in some embodiments, comprises at least one artifact removal module configured and operable to automatically remove artifacts from data or signals indicative of at least one brain state of the subject. Optionally, the at least one artifact removal module is configured and operable to use an automated independent component analysis (ICA) real-time process for artifact removal.

[0029] The brain state monitoring and stimulation component, in some embodiments, includes a communications module configured and operable to exchange data with at least one other device / system. Thus, the brain state monitoring and stimulation component may be configured and operable to communicate via the communications module treatment data related to a treatment protocol implemented by the system. The system, in some embodiments, includes a database system for storing treatment data communicated by the brain state monitoring and stimulation component via the communications module. The database system may be configured and operable to analyze the stored treatment data and generate statistical data related thereto. Optionally, but preferably in some embodiments, the database system is configured and operable to utilize artificial intelligence tools in analyzing the stored data.

[0030] The brain stimulation signals applied to the brain of the treated subject, in some embodiments, relate to at least one of audio, visual, TMS, tDCS, tACS, and / or tactile stimuli.

[0031] Another inventive aspect of the subject matter disclosed herein relates to a method for generating brain stimulation, the method comprising the steps of receiving data or signals indicative of at least one brain state of a subject, analyzing the received data or signals to identify at least one characteristic associated with the at least one brain state, predicting at least one brain state that is about to occur (or has occurred) in the brain of the subject based on the identified at least one characteristic and / or the received data / signals, and generating stimulation data for applying a stimulation signal to the brain of the subject based on the predicted at least one brain state.

[0032] Optionally, the method includes at least one of filtering and processing the received data or signals indicative of the at least one brain state to remove at least one of interferences and artifacts therefrom, respectively. Processing the received data or signals indicative of the at least one brain state may include an independent component analysis process configured to automatically remove artifacts from the received data or signals in real time.

[0033] In some embodiments, the method includes continuously, periodically, or intermittently repeating the prediction of at least one brain state and the generation of stimulation data for newly received data or signals indicative of at least one brain state of the subject throughout at least one treatment session. Optionally, but preferably in some embodiments, the generated stimulation data is configured to adjust / modulate the stimulation signal according to changes in at least one characteristic identified from the received data or signals. In some possible embodiments, the generated stimulation data is associated with at least one of the frequency, amplitude, stimulation pattern, current polarity, and / or precise location of the brain stimulation signal. In this manner, the generated stimulation can be used to adjust the stimulation signal applied to the subject in real time based on data or signals indicative of at least one brain state newly received by the system during operation.

[0034] The method, in some embodiments, includes storing treatment data indicative of the received data or signals and the generated treatment data in a database, analyzing the treatment data stored in the database, and generating analysis data indicative thereof, wherein analyzing the treatment data, in some embodiments, includes at least one of statistical analysis, machine learning, deep learning, or any combination thereof.

[0035] In some embodiments, functional magnetic resonance imaging (fMRI) is used to monitor brain activity of the treated subject. Data signals from the fMRI system can be utilized in addition to, or instead of, EEG and / or tACS data or signals to identify brain states of the treated subject via the brain stimulation schemes described herein.

[0036] The stimulation data can be generated based on brain states previously induced in the treated subject's brain by a stimulation signal. Optionally, the stimulation signal includes at least one of an audio signal, a visual signal, a TMS signal, a tDCS signal, or a tACS signal. For example, without limitation, this method can be used to induce some activation in the treated subject's brain by applying brain stimulation, and then processing the acquired brain state data to identify at least one response that occurred in response to the applied brain stimulation and generate new stimulation data accordingly to induce a desired brain state. The observed response in the processed brain state data can be predictive of at least one future brain state in the treated subject's brain and / or indicative of a current brain state that can be used in the prediction process. For example, the prediction process can be implemented by a pattern matching process configured to compare patterns identified in the acquired brain state data with known responses and, accordingly, identify future brain states that may occur in the treated subject's brain in response.

[0037] Another inventive aspect of the present application relates to an integrated circuit (IC) system comprising at least one processor, at least one memory, and at least one digital signal processing unit configured and operable to receive and process brain state data or signals indicative of at least one brain state of a subject, and to generate stimulation data for adjusting, modulating, or triggering, in real time, brain stimulation signals applied to the subject's brain, based thereon. In some embodiments, the integrated circuit has embedded therein a prediction module configured and operable to predict, based on the brain state data or signals, at least one brain state occurring or about to occur in the subject's brain, and to generate stimulation data based on the predicted at least one brain state. Furthermore, a real-time operating system can be incorporated into the integrated circuit system for operating the at least one processor and its digital signal processing unit.

[0038] The IC system, in some embodiments, includes at least one digital filtering module incorporated therein and configured and operable to remove interference from the brain state data or signals. At least one artifact removal module may also be incorporated into the IC system to apply an automated independent component analysis process to remove artifacts from the brain state data. Optionally, at least one analysis module embedded in the IC system is used to process the brain state data or signals to perform at least one of time-frequency analysis and spectral analysis thereof. Also, at least one feature extraction module is embedded in the IC system of some embodiments to extract at least one feature from the brain state data or signals that is indicative of at least one brain state to be treated.

[0039] Optionally, but preferably in some embodiments, a communication interface module is further embedded in the IC system for communicating data via at least one of a wireless data communication and a bus conductor data communication channel. Optionally, at least one processor or digital signal processing unit of the integrated circuit system is implemented as an FPGA or a GPU.

[0040] In this manner, a brain stimulation system can be implemented using an IC system and a stimulus generator electrically connected or coupled to the IC system and configured to receive stimulation data from at least one digital signal processing unit and, based thereon, generate or adjust real-time brain stimulation to be applied to the brain of a subject to treatment. [Brief explanation of the drawings]

[0041] In order to understand the invention and to see how it may be carried out in practice, embodiments will now be described, by way of non-limiting example only, with reference to the accompanying drawings, in which the illustrated features are intended, unless otherwise implicit, to illustrate only some embodiments of the invention, and in which like reference numerals have been used to indicate corresponding parts. [Figure 1A] FIG. 1A is a block diagram that schematically illustrates a brain stimulation system according to some possible embodiments. [Figure 1B] FIG. 1B is a block diagram that schematically illustrates a brain stimulation system according to some possible embodiments. [Figure 1C] FIG. 1C is a block diagram that schematically illustrates a brain stimulation system according to some possible embodiments. [Figure 2] FIG. 2 is a block diagram that schematically illustrates a brain stimulation system, according to some possible embodiments, that utilizes transcranial alternating current stimulation to identify target brain conditions. [Figure 3]FIG. 3 is a block diagram that illustrates a schematic representation of a brain stimulation system, according to some possible embodiments, that utilizes EEG signals and transcranial alternating current stimulation signals / data to identify brain conditions of interest. [Figure 4] FIG. 4 is a block diagram that illustrates a schematic representation of a brain stimulation system, according to some possible embodiments, that utilizes fMRI data / signals to identify brain conditions to be treated. [Figure 5] FIG. 5 is a block diagram of an applicator unit configured to adapt in real time the shape and / or timing of the stimulation signal applied by the system, according to some possible embodiments. [Figure 6] FIG. 6 is a flow chart illustrating a closed-loop brain stimulation process according to some possible embodiments. [Figure 7] FIG. 7 is a block diagram of a data processing and analysis system according to some possible embodiments. [Figure 8] FIG. 8 shows angular histograms of experimental results obtained using a brain stimulation system according to an embodiment of the present application. DETAILED DESCRIPTION OF THE INVENTION

[0042] This application discloses closed-loop brain stimulation technologies and implementations thereof configured to trigger and / or modulate / regulate brain stimulation according to real-time brain states identified based on EEG signals, and / or brain state frequency and phase rhythms obtained by transcranial alternating current stimulation (tACS), and / or task-dependent brain states achieved by the input of external triggers (e.g., TMS pulses that induce specific brain states, and / or sensory stimuli, such as, but not limited to, auditory and / or visual and / or tactile stimulation) (all of which are collectively referred to herein as brain state signals / data). Such real-time implementations are difficult to achieve due to the amount of data processing required to analyze the brain state signals / data, including filtering and cleaning of raw signals, determining source localization, spectral analysis, phase locking, statistical operations, and / or predictive model estimation, and the high data processing rates required to perform these operations. Therefore, the design of real-time closed-loop brain stimulation solutions requires special and careful consideration to optimize system performance and accuracy.

[0043] In some possible embodiments, the various data processing modules of the real-time closed-loop brain stimulation system are implemented in hardware (e.g., FPGA / GPU) configured for direct memory access (DMA) or any other suitable high-speed communication protocol (FCP), such as, but not limited to, serial peripheral interface (SPI), two-wire interface (TWI), universal synchronous and asynchronous receiver-transmitter (USART), and use one or more processing units that operate these hardware data processing modules using a real-time operating system (RTOS), thereby significantly improving system performance / speed. The data processing hardware modules may include a pre-processing module configured for referencing, source localization, and signal filtering, a time-frequency analysis module, and a brain state prediction module. The brain state prediction module may be configured to use various features extracted from the brain state signals / data, such as, but not limited to, signal power spectrum, signal strength, signal timing, and / or brain connectivity measures such as phase-lock values, to determine at least one parameter of the stimulation signal applied by the system and generate a trigger signal based thereon. An autoregressive forward prediction model can be used to update the prediction model over time based on peak frequencies identified by the system in the brain state signal / data.

[0044] In some embodiments, the user interface unit is used to set various operating parameters (power threshold, target frequency / phase, analysis type / time window, predictive model, processing steps) and / or conditions / states (target electrodes, stimulation pattern). In this way, a particular stimulation elicitation process using the determined stimulation pattern can be tailored to a particular subject for treating a particular neurological / psychiatric condition.

[0045] In some embodiments, the subject interface unit is used to interact with the treatment subject to induce a desired brain state in the treatment subject. The subject interface unit can be configured to present audio, visual, or audiovisual content to the user, and / or to provide tactile stimulation to induce a desired brain state, and / or to stimulate the brain using tACS electrodes to induce a desired brain state. Alternatively or additionally, the subject interface unit can be configured to present specifically designed tasks (cognitive tasks) to the treatment subject and optionally receive corresponding input / feedback from the treatment subject, which can be used by the system to induce a dependent brain state, achieved, for example, by input of an external trigger.

[0046] One or more specific embodiments of the present application are described below with reference to the drawings. These drawings are to be considered in all respects only as illustrative and not limiting in any way. In order to provide a concise description of these embodiments, not all features of an actual implementation are described herein. Instead, emphasis is placed on clearly explaining the principles of the present invention so that those skilled in the art can, once they understand the principles of the subject matter disclosed herein, manufacture and use brain stimulation applications.

[0047] The embodiments described below may be embodied in other specific forms without departing from the essential characteristics described herein. The embodiments shown generally and diagrammatically in the figures are provided as exemplary implementations illustrating some of the functions, processes, and principles used to provide real-time, closed-loop brain stimulation, but which are also useful for other applications and may be made in numerous variations. Accordingly, while this description will proceed with reference to the illustrated examples, it should be understood that the invention as claimed below can be embodied in countless other ways once the principles are understood from the descriptions, explanations, and drawings provided herein. All such variations, as well as any other modifications that are obvious to those skilled in the art and useful for brain stimulation applications, may be utilized as appropriate and are intended to fall within the scope of the present disclosure.

[0048] 1A is a block diagram of a brain stimulation system 1A, configured with several possible embodiments, for closed-loop triggering and adjustment of non-invasive brain stimulation based on real-time brain state indications. The system 10 includes a real-time brain state monitoring and stimulation system 11 configured and operable to continuously, periodically, or intermittently read electroencephalogram (EEG) data / signals 13s / 13s′ (also referred to herein as brain state data) indicative of brain waves measured by EEG electrodes 17 positioned on a subject's head 10s from an electroencephalogram (EEG) unit 18, process the received EEG data / signals 13s / 13s′, and generate corresponding brain stimulation parameter and / or timing data 14s (collectively referred to herein as stimulation data) for use by a stimulation generator 14. The stimulation generator 14 is configured and operable to generate stimulation signals 15s used by a stimulation applicator 15 to apply stimulation to at least one region / area of ​​the brain being treated. The real-time brain state monitoring and stimulation system 11 comprises one or more processors 11u, memory 11m configured and operable to use a real-time operating system (RTOS) for operating a digital signal processing (DSP) unit 12 incorporated within the brain state monitoring and stimulation system 11.

[0049] The DSP unit 12 is configured and operable to process the brain state data / signals 13s / 13s' and, based thereon, generate brain stimulation parameters and / or timing data 14s that are provided to the stimulation applicator. FIG. 1B shows components of the DSP unit 12 according to some possible embodiments, including a filter unit 12e configured to remove interferences / artifacts from the brain state data / signals 13s / 13s', an analysis unit 12f configured to process and analyze the brain state data / signals 13s / 13s', and a feature extraction unit 12x configured to identify one or more brain state features based on the brain state data / signals 13s / 13s'. The DSP unit can be configured to generate the brain stimulation parameters and / or timing data 14s based on the one or more brain state features from the feature extraction unit 12x.

[0050] In some embodiments, the real-time brain state monitoring and stimulation system 11 and all of its components are implemented in a single integrated circuit device.

[0051] 1C is a block diagram of a brain stimulation system 10 configured as some possible embodiments for closed-loop triggering and adjustment of non-invasive brain stimulation based on real-time brain state indications. The system 10 comprises a real-time brain state monitoring and stimulation system 11 configured and operable to continuously, periodically, or intermittently read electroencephalogram (EEG) data / signals 13s / 13s′ from an electroencephalogram (EEG) unit 18 indicative of brain waves measured by EEG electrodes 17 positioned on a subject's head 10s, process the received EEG data / signals 13s / 13s′, and generate corresponding brain stimulation parameter and / or timing data 14s (generally referred to herein as stimulation data) for use by a stimulus generator 14. The stimulus generator 14 is configured and operable to generate stimulation signals 15s used by the stimulus applicator 15 to apply stimulation to at least one region / area of ​​the brain of the treatment subject 10s (e.g., by a TMS coil, and / or a sound / voice generator / player, and / or current stimulation electrodes, and / or a 2D / 3D display / holographic device, and / or a tactile stimulation transducer).

[0052] In some embodiments, the real-time brain state monitoring and stimulation system 11 is configured to identify a brain state of the subject (10s) by analyzing EEG rhythms reflected from the received EEG data / signals 13s / 13s', predict at least one brain state that is about to occur in the brain of the subject (10s) based on the identified brain state rhythm, and generate one or more brain stimulation parameters and / or timing data 14s (e.g., frequency, and / or amplitude, and / or timing) based on the identified and / or predicted brain state, which is used by the stimulus generator 14 to generate a stimulation signal 15s for stimulating the brain (10s) of the subject (10s) by the stimulation applicator 15.

[0053] The brain state signals 13s' from the EEG unit 18 are digitized by an analog-to-digital converter (ADC) 13, which may be an integrated unit incorporated into the real-time brain state monitoring and stimulation system 11 or an external unit to the real-time brain state monitoring and stimulation system 11. In this particular, non-limiting example, the brain stimulation generator 14 is configured to generate stimulation signals 15s used by one or more electromagnetic coils 15c of an applicator 15, such as a TMS coil configured to induce a magnetic field in brain tissue of the treatment subject (10s). However, other brain stimulation technologies, such as those described above and below, can similarly be used for the brain stimulation generator 14 and applicator 15 units when operating in a closed loop coordinated by the real-time brain state monitoring and stimulation system 11.

[0054] The real-time brain state monitoring and stimulation system 11 comprises one or more processors 11u and memory (e.g., RAM, SDRAM, Flash) 11m configured and operative to use a real-time operating system (RTOS) to operate a digital signal processing (DSP) unit 12 embedded within the brain state monitoring and stimulation system 11. The DSP unit 12 is configured and operative to perform data filtering, spectral analysis functions, feature extraction, brain state prediction, and to determine operating parameters and / or timing data used by the brain stimulation generator 14 to generate stimulation signals 15s.

[0055] Because processing speed is essential for accurately identifying brain states of treatment subjects (10s) in real time, one or more processing units 11u and memory 11m of the real-time brain state monitoring and stimulation system 11 operated by an RTOS are implemented in a single hardware device, and the DSP unit 12 is implemented as a separate hardware unit, such as an FPGA / GPU implementation. To optimize system performance, the one or more processors 11u and / or DSP unit 12 are configured to utilize a high-speed data communication protocol (e.g., FCP) and / or use direct memory access (DMA), thereby preventing the unavoidable "handshake" delays typically required in conventional systems where a processing unit must handle memory access operations.

[0056] For example, when implemented using DMA, the one or more processors 11u and the DSP unit 12 can independently perform data read and write operations to the memory 11m without one unit interrupting the other's continuous data processing operations. Therefore, using DMA and / or a high-speed communication protocol in the brain state monitoring and stimulation system 11 relieves the one or more processors 11u, which are typically responsible for acquiring the brain state data / signals 13s / 13s'. This configuration of the brain state monitoring and stimulation system 11 allows the one or more processors 11u to be more efficient and relay data in real time with low latency. The DSP unit 12 is further configured to determine operating parameters and scheduling / timing data 14s and output them to the brain stimulation generator 14.

[0057] Table 1 shows the improvements in data processing speed that can be achieved with the above hardware implementation of the brain state monitoring and stimulation system 11, according to some possible embodiments. This design significantly speeds up data collection and processing operations, allowing more data to be collected in a shorter period of time. This allows predictive algorithms to make more accurate long-term predictions of brain waves, allowing for precise triggering and modulation of brain stimulation pulses.

[0058] (Table 1) TIFF0007821507000001.tif47170 (from C. Cullinan et al., Math Works 2012)

[0059] The improved processing speed and prediction accuracy provided by brain stimulation system 10 may extend the system's usefulness to subjects whose measured power in alpha or other EEG bands is relatively low and who are therefore ineligible for treatment with conventional techniques. For example, the algorithm used for phase locking in the system of Zrenner et al. (2018) required that the measured power in the alpha EEG band be at least 25% of the total measured power in all EEG bands in order to achieve phase locking. This power limitation can be overcome by improving the processing speed of brain stimulation system 10, thereby enabling better prediction accuracy even for weak or noisy signals.

[0060] The DSP unit 12, in some embodiments, includes a digital filter module 12e configured and operable to remove interfering signals from the EEG signals / data 13s′ / 13s digitized by the ADC 13, e.g., using an infinite impulse response (IIR, e.g., Butterworth or elliptic filter), a finite impulse response (FIR) filter, for example. The DSP unit 12 also includes a time-frequency (TF) analysis module 12f configured and operable to perform spectral analysis (e.g., utilizing a fast Fourier transform—FFT) and time-frequency analysis on the filtered EEG signals / data from the filter module 12e and generate data indicative thereof, and a feature extraction module 12x configured and operable to analyze the processed and filtered measurement data generated by the analysis module 12f and, based thereon, identify one or more brain state features (e.g., dominant frequency bands, phase data, amplitude data, etc. of the filtered measurement data) and generate feature data indicative thereof. The brain state prediction module 12p is configured and operable to process the feature data generated by the feature extraction module 12x, predict at least one brain state about to occur in the brain of the subject (10s), and generate, based on the predicted at least one brain state, operating parameters and / or timing data 14s to be used to generate one or more brain stimuli by the stimulus generator 14.

[0061] Optionally, but preferably in some embodiments, the DSP unit 12 comprises an artifact removal module 12r configured and operable to process the EEG signals / data 13s / 13s′ received from the EEG unit 18. The artifact removal module 12r is particularly advantageous in embodiments utilizing an electromagnetic coil 15c (e.g., a TMS coil) in the applicator 15, because the magnetic field generated by the coil 15c introduces strong artifacts into the EEG data / signals 13s / 13s′ measured by the EEG unit 18, thus complicating analysis of the EEG data / signals 13s / 13s′. It should be noted, however, that artifacts may be introduced into the EEG data / signals 13s / 13s′ from multiple different sources, such as, for example, electrical power, eye blinking, movement, muscle contractions, heartbeats, electrode movement, mechanical pressure on the electrodes, etc.

[0062] In some embodiments, the analysis module 12f is configured and operable to analyze the EEG data / signals 13s / 13s' measured by the EEG unit 18 within a time window of up to 100 milliseconds after the application of brain stimulation by the applicator 15, such as a TMS pulse by the coil 15c. This can be practically difficult to perform due to induced artifacts. Typically, such artifacts are removed in conventional brain stimulation systems by a manual post-processing analysis stage aimed at eliminating the described artifacts by removing specific components extracted using independent component analysis (ICA). While ICA is considered robust and provides good results, it is a time-consuming process / algorithm and is not suitable for the real-time brain stimulation system disclosed herein. Thus, in some embodiments, the artifact removal module 12r is implemented (by hardware, software, or a combination of hardware and software) utilizing a real-time ICA process configured to remove artifacts from the EEG data / signals 13s / 13s′ in real time, for example, within a time window of about 100-600 ms, optionally about 150-500 ms, and in some embodiments about 200 ms, from acquisition of the brain state data / signals 13s / 13s′.

[0063] The artifact removal module 12r is configured and operable to remove artifacts from the EEG signal / data 13s' / 13s and pass the substantially artifact-free EEG signal / data to the filter unit 12e for signal filtering. Alternatively, in some possible embodiments, the artifact removal module 12r is configured and operable to receive the filtered EEG signal / data generated by the filter module 12e, process the received filtered EEG signal / data to remove artifacts therefrom, and pass the substantially artifact-free filtered EEG signal / data to the analysis module 12f.

[0064] In some embodiments, various components of the real-time brain state monitoring and stimulation system 11 operated by an RTOS are implemented on a single chip device (e.g., on a single semiconductor substrate as an SoC). For example, in an SoC implementation of the real-time brain state monitoring and stimulation system 11, one or more processing units 11u may be implemented in an FPGA, and the DSP unit 12 may be implemented by the SoC's GPU. In this manner, the DMA and all electrical connections are formed / patterned internally within the chip, without the need for separate components or printed circuit boards that would normally be required. Because all components of the system 11 are implemented and interconnected on the same semiconductor substrate of the chip, such an SoC implementation improves data communication speeds and significantly reduces the system's geometric size.

[0065] Non-invasive brain stimulation has been shown to have therapeutic benefits for a range of neurological and psychiatric indications, each with its own protocol, and thus each may be optimized for different brain conditions. Accordingly, in some embodiments, the operating parameter data 14s determined by the prediction module 12p for adjusting the stimulation signal 15s generated by the stimulation generator 14 may include one or more of the stimulation signal strength, and / or stimulation signal frequency and / or timing, and / or the therapeutic brain region / area to be stimulated.

[0066] In some embodiments, the system 10 includes a user interface unit 11z configured to present information to a user / practitioner and receive various data inputs from the user / practitioner. For example, but not by way of limitation, the user interface unit 11z may be configured to allow the user / practitioner to specify at least one of the following parameters / options: (1) The input sources / electrodes (e.g., EEG electrodes 17, and / or specific tACS electrodes 17', and / or fMRI signals / data, and / or cognitive task responses from the subject interface unit 11s) used by the analysis module 12f and feature extraction module 12x to identify the subject's brain state, including the option to target electrodes across multiple brain regions / areas to measure brain connectivity. (2) Pre-processing steps performed by DSP unit 12, such as but not limited to lookup, filtering, and source localization. (3) The target frequency band and phase used by the stimulus generator 14. (4) The power threshold for the target frequency band used by the stimulus generator 14. (5) A time window for analysis by analysis module 12f and feature extraction module 12x. (6) The type of analysis performed by analysis module 12f and feature extraction module 12x. (7) The prediction model used by the prediction module 12p. (8) The output stimulus pattern applied by the stimulus generator 14 .

[0067] Thus, the prediction module 12p is configured and operable to determine the operating parameters / stimulation data 14s used to adjust the stimulation signal 15s generated by the generator 14 based on features extracted from the input data / signals and / or the above parameters / options (1)-(8) set by the operator / practitioner via the user interface unit 11z. In some embodiments, user / practitioner specification is essential because different applications require not only identifying specific brain states but also varying balances between speed and accuracy. By definition, phase is continuously changing, so instantaneous phase locking must be fast. Embodiments of the brain stimulation system herein are configured to operate at such high speeds to analyze instantaneous phase with the required high temporal precision. However, it should be noted that the brain stimulation system can also utilize analysis types requiring less temporal precision, such as event-related desynchronization (ERD), which increases the stability of the analysis and opens the ability to add other aspects to the analysis pipeline. For example, this can be reflected in the choice of source localization process used by feature extraction module 12x, which, when ERD is used, may utilize a more accurate but slower process, such as utilizing a Laplacian transform, or, for example, utilizing a Hjorth transform.

[0068] Another non-limiting example is the selection of the prediction model employed by the prediction module 12p. In some possible embodiments, the stimulation system 10 is configured to update the prediction module 12p over time using a range of options, including, but not limited to, an autoregressive forward prediction model, modeling based on the peak frequency of the FFT transform, and a backpropagation feedback model. The prediction module 12p can be configured to identify correlations between the brain state data / signals 13s / 13s′ from the EEG unit 18 and brain state patterns previously measured by the system in the current or previous session, and based on these correlations, predict future brain states likely to occur in the treated brain, thereby determining the stimulation data 14s provided to the stimulation generator 14.

[0069] The stimulation data 14s provided by the brain state monitoring and stimulation system 11 to the brain stimulation generator 14, in some embodiments, includes instructions for triggering predetermined stimulation patterns that match a target brain state and / or instructions for applying different patterns designated for various brain states. Thus, the stimulation generator 14 can be configured to be compatible with transcranial magnetic stimulation (TMS) treatment, transcranial direct current stimulation (tDCS) treatment, and / or transcranial electrical stimulation (TES) treatment.

[0070] In some embodiments, the brain stimulation system comprises a subject interface unit 11s configured and operable to interact with the subject to induce a desired brain state in the subject. This subject interface unit 11s can be configured to play audiovisual content configured to induce a desired brain state in the subject and / or to present specially designed (e.g., cognitive) tasks to the subject and, optionally, receive corresponding input / feedback usable by the system 10 to induce an addictive brain state, e.g., achieved by input of an external trigger.

[0071] 2 is a block diagram of a possible embodiment of a brain stimulation system 10′ that is substantially similar to the brain stimulation system 10 shown in FIG. 1 , but that uses tACS data 19s instead of EEG data / signals to identify a brain state of a treated subject. In this example, the brain stimulation system 10′ further includes a tACS unit 19s configured and operable to induce a desired brain state in the treated subject's brain (e.g., entrain the subject's brain rhythm to a given frequency and phase) via tACS electrodes 17′. Optionally, the brain stimulation system 10′ can be configured to receive a tACS signal 19s′ applied to the treated subject via the tACS electrodes 17′ and digitize the received tACS signal 19s′ via an ADC 13. The tACS data / signal 19s / 19s' is similarly processed by the brain state monitoring and stimulation system 11 as described above, with artifact removal and / or interference filtering by the artifact removal module 12r and the filter module 12e, the filtered and / or artifact-free tACS data / signal analyzed by the analysis module 12f, and at least one likely brain state of the treated subject's brain predicted by the brain state prediction module 12p, generating corresponding stimulation parameters and / or timing data 14s for adjusting / regulating the stimulation signal 15s generated by the stimulation generator 14. The subject interface unit 11s can similarly be used to input external triggers, such as, but not limited to, behavioral performance, thereby making brain state triggering task-dependent.

[0072] 3 is a schematic block diagram of a brain stimulation system 10'' that utilizes EEG signals / data 13s' / 13s and tACS signals / data 19s and / or 19s' to identify a brain state of a subject (10s). The brain stimulation system 10'' can be configured to select only the EEG data / signals 13s / 13s', only the tACS signals / data 19s and / or 19s', or both the EEG and tACS signals / data to predict at least one brain state about to occur in the subject's brain and use this to generate corresponding stimulation parameters and / or timing data 14s for adjusting / regulating the stimulation signal 15s applied by the stimulation generator 14, as described above.

[0073] 4 is a schematic block diagram of a brain stimulation system 10''' configured to use fMRI data 44s and / or signals 44s' from an fMRI system 44 to identify a brain state of a subject to be treated. The fMRI system 44 is configured to measure brain activity of a subject (10s), and the brain stimulation system 10''' is configured and operable to predict at least one likely brain state of the subject's brain based on the signals / data from the fMRI system 44 and use this to generate corresponding stimulation parameters and / or timing data 14s for adjusting / regulating the stimulation signal 15s applied by the stimulation generator 14, as described above.

[0074] As shown in FIG. 4 , the brain stimulation system 10′″ combines signals / data 13s′ / 13s received from EEG electrodes 17 and / or tACS signals / data 19s′ / 19s with fMRI data / signals 44s / 44s and uses signals / data received from various sources to extract features of brain states for prediction and / or generation of stimulation data 14s.

[0075] 1-4 , real-time brain state monitoring and stimulation system 11 may include a communication interface module 11i configured and operable to handle data communication with EEG unit 18, and / or tACS unit 19, and / or fMRI system 44, and / or user interface unit 11z, and / or subject interface unit 11s, and / or stimulus generator 14. Communication interface module 11i may be configured to communicate data with the various units / systems via conductive wiring, for example, using serial (e.g., USB, UART, etc.) or parallel (ISA, ATA, SCSI, PCI, etc.) bus data communication protocols and / or wireless data communication (e.g., WiFi, Bluetooth, Zigbee, etc.). Thus, in embodiments in which the components of real-time brain state monitoring and stimulation system 11 are embedded in a single integrated circuit (IC) device, communication interface module 11i is also embedded in the IC of system 11.

[0076] 5 is a block diagram of a possible stimulation generator 14 configured to adjust, in real time, the signals applied by the system to a treated brain based on brain stimulation parameter and / or timing data 14s generated by the brain state monitoring and stimulation system 11. In this particular, non-limiting example, the stimulation generator 14 comprises a control unit 14u configured and operable to receive the brain stimulation parameter and / or timing data 14s and, based thereon, generate control signals 14w and / or 14m for adjusting, in real time, the shape and / or scheduling and / or pattern of the stimulation signals 15s delivered to the stimulation applicator 15.

[0077] The stimulus generator 14 may include a plurality of signal sources g1, g2, g3, ..., gn, each associated with a respective switch unit w1, w2, w3, ..., wn and configured to generate a particular waveform having a desired shape and frequency. The frequency of the waveform generated by each signal source g1, g2, g3, ..., and gn may be determined by a control signal (not shown) generated by a control unit 14u based on brain stimulation parameters and / or timing data 14s. The control unit 14u is configured and operable to select the waveform of the stimulus signal 15s by generating a respective control signal 14w to change the state of each switch unit w1, w2, w3, ..., wn, thereby selecting the waveform provided to the signal summing unit 14t.

[0078] The signal sum produced by summing unit 14t may be input to signal modulation unit 14d, which is configured and operable to modulate it with a signal pattern provided via signal / data 14m from control unit 14u. Note that modulation of the signal from summing unit 14t is not essentially required in all brain stimulation sessions. Thus, signal / data 14m from control unit 14u may be used to instruct modulator 14d to pass the signal from summing unit 14t through unchanged, without modulation.

[0079] 6 is a flowchart illustrating a closed-loop brain stimulation process 40 according to some possible embodiments. The brain stimulation process 40 begins at step S1, where the brain stimulation system receives session input from the user / practitioner via the user interface unit 11z (e.g., parameters / options (1)-(8) above). In step S2, based on the input received in step S1, the system determines the brain state (BS) data / signal source (e.g., EEG signals, tACS signals, and / or fMRI data) to be used in the session, the device to use for applying brain stimulation (e.g., coil and / or electrode selection for targeting the desired brain region / area, and / or sensory applicator), and / or the signal pattern to be applied to the treatment target.

[0080] In step S3, the system receives and filters and / or removes artifacts from brain state signals / data. The filtered and / or artifact-free signals / data are then analyzed (e.g., spectral analysis and / or time-frequency analysis) in step S4, and in step S5, the system extracts one or more brain state features from the analyzed filtered, processed, and / or artifact-free brain state signals / data. Next, in step S6, the system predicts one or more brain states about to occur in the treated brain, and in step S7, the system determines operating parameters and / or timing data that the stimulation applicator will use to apply the stimulation signals in step S8.

[0081] In step S9, a check is made to see if the brain stimulation session is to be terminated. If further brain stimulation is to be applied, steps S3-S8 are repeated as many times as necessary until step S9 determines that the session should be terminated by ceasing the stimulation applied by the applicator and transferring control to step S10. Note that the application of the brain stimulation signal in step S8 may continue continuously as steps S3-S8 are repeated, and the applied brain stimulation signal may be dynamically changed during these steps as new stimulation parameters and / or timing data are generated in step S7 to adjust the applied brain stimulation to changes occurring in the therapy user's brain state.

[0082] It should also be understood that in process 40 or any method described herein, the process / method steps can be performed in any order or simultaneously, unless it is clear from the context that a step is dependent on another step being performed first. Process 40, or one or more steps thereof, can be implemented as computer-executable code created using a structured programming language (e.g., C), an object-oriented programming language such as C++, or any higher- or lower-level programming language (including assembly language, hardware description languages, database programming languages ​​and technologies) that can be stored, compiled, or interpreted for execution on a computing device, as well as combinations of heterogeneous processors, processor architectures, or combinations of different hardware and software.

[0083] The data processing of the brain stimulation systems disclosed herein may be distributed across several computing devices that are functionally integrated into one dedicated, independent stimulation system, and all such permutations and combinations are intended to be within the scope of the present disclosure.

[0084] FIG. 7 illustrates a data processing and analysis system 50 according to some possible embodiments. The system 50 includes multiple real-time brain state monitoring and stimulation systems 11, each having a communication module 11c configured and operable to communicate data over a data network 51, e.g., the Internet. In some embodiments, a data server 52 is used to receive and record treatment data associated with treatment protocols implemented by the multiple real-time brain state monitoring and stimulation systems 11, as well as treatment progress and results obtained using the treatment protocols. Data received from the multiple real-time brain state monitoring and stimulation systems 11 can be stored in a database 52d maintained by the data server 52. The data server 52 can be implemented in a computer system having one or more processors (not shown), memory (e.g., volatile and non-volatile—not shown), and a data communication module (not shown), as is commonly used in data servers.

[0085] In some embodiments, the analysis module 52a is used by the data server 52 to process the protocol and treatment outcome data stored in the database 52d and identify treatment protocols that have demonstrated satisfactory results from data accumulated over time. Accordingly, the analysis module 52a may be configured to utilize statistical analysis and / or machine learning tools to identify treatment protocols for which a high percentage of treatment sessions demonstrated beneficial treatment effects. Optionally, but preferably in some embodiments, the analysis module 52a is configured to utilize artificial intelligence tools, such as, but not limited to, neural networks, machine / deep learning algorithms, etc., to analyze the data accumulated in the database 52d. Accordingly, analysis of the treatment data accumulated in the database 52d may be used to generate statistics of condition and predictive biomarkers and / or protocols and / or their outcomes.

[0086] The data server 52 can be configured to transfer analysis data from multiple real-time brain state monitoring and stimulation systems 11 indicating treatment protocols with the highest scores for treating a particular neurological / psychiatric condition. The real-time brain state monitoring and stimulation system 11 can be configured to generate and send queries to the data server 52d to scan the database 52d and look for treatment protocols that have been reported to provide satisfactory results in treating a particular neurological / psychiatric condition. Accordingly, the real-time brain state monitoring and stimulation system 11 can be configured to receive response data from the data server 52 along with statistical analysis in the analysis module 52a regarding the issued queries and present the response data to the user / practitioner on the user interface unit 11z.

[0087] Figure 8 shows an angle histogram (rose plot) of experimental stimulation applications performed using the exemplary implementation of the brain stimulation system shown in Figure 1. This plot shows the actual phase angle distribution at trigger time when set to 0° (zero degrees) for a representative raw EEG trace. Despite the alpha band's percentage of measured total power being less than 1%, the standard deviation here is approximately 38°. This is therefore an improvement over the prior art (Zrenner et al., 2018), where the algorithm was limited to subjects requiring the alpha band's percentage of total measured power to be 25% or greater of total power, resulting in a standard deviation of 53°. Thus, the brain stimulation process and its implementation disclosed herein are clearly more accurate and more effective for a larger subject population.

[0088] The functions of the brain stimulation system described above can be controlled via instructions executed by a computer-based control system. A control system suitable for use in the above embodiments can include, for example, one or more processors connected to a communication bus, one or more volatile memories (e.g., random access memories—RAMs) or non-volatile memories (e.g., flash memories). Secondary memories (e.g., hard disk drives, removable storage drives, and / or removable memory chips such as EPROMs, PROMs, flash memories, etc.) can be used to store data, computer programs, or other instructions that are loaded into the computer system.

[0089] Those skilled in the art will understand that the various illustrative blocks, modules, elements, components, methods, operations, steps, algorithms, and other items described herein may be implemented as hardware or as a combination of hardware and computer software.

[0090] In the embodiments disclosed herein, features of the present invention are implemented primarily in hardware using, for example, hardware components such as application specific integrated circuits (ASICs) and / or field programmable gate arrays (FPGAs), and / or system-on-chip implementations. Implementation of hardware state machines to perform the functions described herein will be apparent to those skilled in the relevant art. In yet other embodiments, features of the present invention may be implemented using a combination of both hardware and software.

[0091] As described above and shown in the associated figures, the present invention provides a closed-loop brain stimulation system and related methods. While specific embodiments of the present invention have been described, the present invention is not limited thereto, and modifications may be made by those skilled in the art, particularly in light of the foregoing teachings. As will be appreciated by those skilled in the art, the present invention can be implemented in a wide variety of ways using the techniques described above without departing from the scope of the claims.

Claims

1. In brain stimulation systems, a stimulus generator configured to receive stimulation data and generate TMS signals based on the stimulation data, the stimulus generator applying the TMS signals to a region of the head to be treated with one or more TMS coils; a brain state monitoring and stimulation component configured to generate the stimulation data, the brain state monitoring and stimulation component having an integrated circuit device electrically connected to the stimulus generator; the integrated circuit device a digital signal processing (DSP) unit implemented in a graphics processing unit (GPU) of the integrated circuit device to improve data communication speed, the DSP unit configured to process brain state signals from a plurality of electrodes connected to the head of the subject to generate the stimulation data in real time, and provide the stimulation data to the stimulus generator to generate the TMS signal, the DSP unit comprising: (i) at least one digital filtering module configured to remove artifacts in brain state signals resulting from the TMS signals applied by the stimulus generator; and (ii) at least one analysis module configured to apply at least one of a time-frequency analysis and a spectral analysis to the brain state signals from the at least one digital filtering module; (iii) at least one feature extraction module configured to determine, based on the analysis of the at least one analysis module, at least one feature indicative of a brain state to be treated; and (iv) at least one prediction module for predicting in real time at least one upcoming brain state of the brain of the subject to be treated based on at least one feature determined by the at least one feature extraction module to determine at least one parameter of the stimulation data; at least one processor and memory, the at least one processor being implemented in a field programmable gate array (FPGA) of the integrated circuit device and configured to operate the DSP unit implemented in a GPU of the integrated circuit device in real time; 1. A brain stimulation system, comprising: a processor and a DSP unit configured to exchange data using a fast communication protocol (FCP) to increase processing speed of the brain stimulation system and direct memory access (DMA) to the memory to enable continuous, uninterrupted operation of the processor and the DSP unit;

2. 10. The system of claim 1, wherein the DSP unit is configured to repeatedly receive the brain state signals indicative of at least one brain state of the subject, predict the at least one brain state in real time, and immediately after the prediction, generate corresponding stimulation data in real time for adjusting or modulating a TMS signal based on the predicted at least one brain state, thereby providing a closed-loop brain stimulation mechanism that can adjust or modulate a TMS signal in real time according to the generated stimulation data.

3. 3. The system of claim 2, wherein the brain state monitoring and stimulation component is configured to modulate TMS signals with stimulation data generated by the DSP unit.

4. 4. The system of claim 1, wherein the DSP unit comprises at least one artifact removal module configured to automatically remove artifacts due to the TMS signal using an automated independent component analysis process.

5. 5. The system of claim 1, further comprising a communication module configured to communicate treatment data associated with one or more treatment protocols implemented by the system.

6. 6. The system of claim 5, comprising a database system for storing therapy data communicated by the brain state monitoring and stimulation component via the communication module, the database system configured to analyze the stored therapy data and generate statistical data associated therewith.

7. 7. The system of claim 6, wherein the database system is configured to utilize artificial intelligence tools to analyze the stored treatment data and generate the statistical data or biomarkers.

8. 8. The system of claim 1, wherein the brain state signals are associated with at least one of EEG electrodes, tACS electrodes, fMRI equipment, and cognitive tasks performed by the subject.

9. A system described in any one of claims 1 to 8, characterized in that it utilizes at least one brain stimulation selected from the group consisting of audio stimulation, visual stimulation, tDCS signals, and tACS signals.

10. In an integrated circuit system, a digital signal processing (DSP) unit implemented in a graphics processing unit (GPU) of an integrated circuit device to improve data communication speed, the DSP unit configured to receive and process brain state signals measured in real time by a plurality of electrodes connectable to a head of a subject to be treated to generate stimulation data in real time and provide the stimulation data to a stimulus generator for generating TMS signals, the TMS signals being applied to the head of the subject to be treated from one or more TMS coils, the measured brain state signals being in response to one or more TMS signals previously applied to the head of the subject to be treated during treatment and indicative of at least one brain state of the subject, the DSP unit (i) at least one digital filtering module configured to remove artifacts in brain state signals resulting from the TMS signals applied to the subject's head by the TMS coil; (ii) at least one analysis module configured to apply at least one of a time-frequency analysis and a spectral analysis to the brain state signals from the at least one digital filtering module; (iii) at least one feature extraction module configured to determine, based on the analysis of the at least one analysis module, at least one feature indicative of at least one brain state of the subject; (iv) at least one prediction module for predicting in real time at least one upcoming brain state in the brain of the treated subject based on the at least one feature determined by the at least one feature extraction module, and for determining at least one parameter of a new TMS signal to be applied to the head of the treated subject by the one or more TMS coils; at least one processor and memory, the at least one processor being implemented in a field programmable gate array (FPGA) of the integrated circuit device and configured to operate the DSP unit implemented in the GPU in real time to generate the stimulation data in real time and adjust, adjust, or trigger TMS signals applied to the subject's head during treatment in real time; 1. An integrated circuit system, comprising: the at least one processor and the DSP unit configured to exchange data using a fast communication protocol (FCP) to increase processing speed of the integrated circuit system, and direct memory access (DMA) to the memory to enable continuous, uninterrupted operation of the at least one processor and the DSP unit.

11. 11. The system of claim 10, wherein the prediction module is configured to predict, based on the brain state signals, at least one brain state that is about to occur in the subject's brain during treatment, using a backpropagation (BP) feedback model.

12. 12. The system of claim 10 or 11, wherein at least one artifact removal module is configured to use an automated independent component analysis process to remove the artifacts from the brain state signals.

13. 13. The system of claim 10, further comprising a communication interface module embedded in the integrated circuit system and configured to communicate data over at least one of a wireless data communication channel and a wired data communication channel.

14. 14. A brain stimulation system comprising: an integrated circuit system according to any one of claims 10 to 13; and a stimulus generator electrically connected or coupled to the integrated circuit system, the stimulus generator configured to generate or adjust in real time a TMS signal to be applied to the head of the subject based on receiving the stimulation data from the at least one digital signal processing unit.

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