WO2020144259A1

WO2020144259A1 - Quantitative evaluation of electrical source imaging

Info

Publication number: WO2020144259A1
Application number: PCT/EP2020/050396
Authority: WO
Inventors: Lyubomir Georgiev Zagorchev; Shiv SABESAN
Original assignee: Koninklijke Philips N.V.
Priority date: 2019-01-09
Filing date: 2020-01-09
Publication date: 2020-07-16

Abstract

An apparatus (28) that evaluates accuracy of an electrical source imaging technique based on information associated with a stimulation electrode (15) implanted in the brain.

Description

QUANTITATIVE EVALUATION OF ELECTRICAL SOURCE IMAGING

FIELD OF THE INVENTION

[0001] The present invention is generally related to electrical source imaging.

BACKGROUND OF THE INVENTION

[0002] Electroencephalography (EEG) is an electrophysiological monitoring method to sense the electrical activity of the brain using electrodes placed on a subject’s head. High density electroencephalography (HD-EEG, e.g., having more than thirty-two channels) utilizes a high number of electrodes, and it results in superior temporal resolution and significantly higher spatial resolution as compared to traditional EEG.

[0003] Electrical source imaging (ESI), made possible by HD-EEG, is a functional imaging modality valuable for a number of clinical applications, and involves estimating the location of cortical sources in the brain that produce potentials (measured by EEG sensing electrodes) on the scalp. To make such estimates, a forward model is created that describes how electrical current generated by the cortex travels to the scalp, and an inverse problem is solved that provides an estimated mapping of the measured scalp potentials back to cortical sources. Numerical methods, including the boundary element method (BEM), the finite difference method (FDM), and the finite element method (FEM) based on individual magnetic resonance imaging (MRI) or atlases, have been used for ESI.

[0004] Clinical applications of ESI include locating irritative zones in focal epilepsy from interictal epileptiform discharges (lEDs) as well as pre-surgical evaluation and therapy planning, where accurate identification of epileptogenic zones is important to obtaining a positive surgical outcome. The accuracy of ESI can be affected by a number of factors, including the head model, EEG electrode positioning, dipole tessellation, lead field matrix, forward/inverse solution, and regularization. Accuracy of ESI is a big challenge, particularly given the fact that intra-subject EEG test-retest analysis can vary greatly even within a period of one day. [0005] An example of one method to account for this intra-subject variability is to use a head phantom that mimics consistent neural response and enables head model and algorithm specifications to be individually manipulated and verified. Various phantoms have been developed, but none have anatomically accurate electrical and mechanical properties, stable materials, and repeatable manufacturability.

SUMMARY OF THE INVENTION

[0006] One object of the present invention is to improve upon the objectivity of electrical source imaging. To better address such concerns, in a first aspect of the invention, an apparatus that evaluates accuracy of an electrical source imaging technique based on information associated with a stimulation electrode implanted in the brain.

[0007] In one embodiment, the apparatus is configured to estimate a location of an electrical source in a brain using an electrical source imaging technique; and evaluate accuracy of the electrical source imaging technique based on information associated with a stimulation electrode implanted in the brain. The stimulation electrode provides a source for which objective information may be determined.

[0008] In one embodiment, the information comprises a known location of the stimulation electrode, wherein the apparatus is configured to evaluate the accuracy by comparing the estimated location with the known location. Again, the information comprises objective information, including the spatial location of the implanted

stimulation electrode and tissue properties, which may be used to improve the accuracy of the electrical source imaging technique.

[0009] In one embodiment, the apparatus is further configured to cause

application of the stimulation current via the stimulation electrode concurrently with the sensing of the scalp potentials. Through this concurrent sensing and stimulation, multiple aspects of electrical source imaging may be evaluated independently.

[0010] In one embodiment, the apparatus is further configured to evaluate or assess an impact of the one or more stimulation parameters on the accuracy, the assessment based on an absolute distance between the estimated source and the information. The assessment enables an improvement in the electrical source imaging technique.

[0011] In one embodiment, the apparatus is configured to trigger the stimulation current via an application programming interface, the one or more stimulation

parameters updatable via the application programming interface. Since it is not known what signals are detected via electrical source imaging, it is a benefit to be able to modify (update) the stimulation parameters. The application programming interface enables the change in stimulation parameters to stimulate different conditions.

[0012] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiment(s) described hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Many aspects of the invention can be better understood with reference to the following drawings, which are diagrammatic. The components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present invention. Moreover, in the drawings, like reference numerals designate corresponding parts throughout the several views.

[0014] FIG. 1 is a schematic diagram that illustrates an example environment in which an objective-based electrical source imaging method may be implemented, in accordance with an embodiment of the invention.

[0015] FIG. 2 is a block diagram that illustrates an example apparatus for implementing an objective-based electrical source imaging method, in accordance with an embodiment of the invention.

[0016] FIG. 3 is a flow diagram that illustrates an example objective-based electrical source imaging method, in accordance with an embodiment of the invention.

DETAILED DESCRIPTION OF EMBODIMENTS

[0017] Disclosed herein are certain embodiments of an objective-based electrical source imaging method and associated apparatus and system that may provide improvements in accuracy of an electrical source imaging system. In one embodiment, the method comprises estimating a location of an electrical source in a brain using an electrical source imaging technique, and evaluating accuracy of the electrical source imaging technique based on information associated with a stimulation electrode implanted in the brain, where the information comprises a known location of the implanted stimulation electrode as determined, in one embodiment, from a computed tomography scan.

[0018] Digressing briefly, as noted above, the accuracy of an electrical source imaging technique may be based on several factors. To assess the effect of each factor on the overall accuracy of electrical source imaging, a framework for testing that relies on a realistic representation and enables accurate quantitative evaluation is needed. Prior attempts of such a framework includes the use of a head phantom, though deficiencies with such a technique are known and explained above. In contrast, certain embodiments of an objective-based electrical source imaging method acquire real data from an implantable neuro stimulation device and use this as a reference for ground truth. Implantable brain stimulation devices use intracranial electrodes implanted in specific brain areas to deliver electrical current and stimulate neuronal activity on demand. That provides an electrical source with a known spatial location and tissue properties, while also facilitating a repeatable and reproducible methodology.

[0019] Having summarized certain features of an objective-based electrical source imaging method of the present disclosure, reference will now be made in detail to the description of an objective-based electrical source imaging method as illustrated in the drawings. While an objective-based electrical source imaging method will be described in connection with these drawings, there is no intent to limit it to the

embodiment or embodiments disclosed herein. Further, although the description identifies or describes specifics of one or more embodiments, such specifics are not necessarily part of every embodiment, nor are all of any various stated advantages necessarily associated with a single embodiment. On the contrary, the intent is to cover all alternatives, modifications and equivalents included within the principles and scope of the disclosure as defined by the appended claims. For instance, two or more embodiments may be interchanged or combined in any combination. Further, it should be appreciated in the context of the present disclosure that the claims are not

necessarily limited to the particular embodiments set out in the description. [0020] FIG. 1 is a schematic diagram that illustrates an example environment 10 in which an objective-based electrical source imaging method is implemented. The environment 10 may include a clinical setting at a hospital or physician’s office or an educational or research facility. The environment 10 includes a subject 12 that is fitted with a high-density, electroencephalography (HD-EEG) net 14 comprising a plurality of electrodes (e.g., 64, 128, or 256 EEG electrodes and hence a corresponding number of EEG channels). Inserted in the brain (deep in the brain, at the cortical surface, etc.) of the subject 12 is an implantable, deep-brain stimulation (DBS) electrode 15 (hereinafter, also referred to simply as a stimulation electrode). The stimulation electrode 15 and the EEG net 14 are coupled to a processing device 16, which in turn is coupled to the computing device 18. In some embodiments, functionality of the processing device 16 and the computing device 18 may be combined into a single device, or distributed among additional devices in some embodiments. The processing device 16 comprises an amplifier that is configured to filter, measure, and sample the EEG signals acquired by the EEG net 14, and then transfer the digitized samples to the computing device 18 for use in electrical source imaging. The processing device 16 also comprises hardware/software (e.g., stimulation engine, including pattern generator, coupling capacitors, etc.) to provide a stimulation current at the stimulation electrode 15. The stimulation current may be based on one or more stimulation parameters provided by the computing device 18. In one embodiment, the stimulation parameters may be provided by the computing device 18 via an application programming interface (API) 19. Under the direction of the computing device 18, the processing device 16 provides a constant current stimulation at one or more stimulation electrodes (e.g., stimulation electrode 15), the stimulation triggered on demand through the application programming interface 19. The application programming interface 19 enables the definition and updating of the stimulation parameters within pre-configured safety limits. The stimulation may be synchronized with the HD-EEG recording. In one embodiment, the effect of different parameters on electrical source imaging may be evaluated using the absolute distance between the estimated source and the actual ground truth (implanted stimulation electrode 15). In general, electrical source imaging from the electrodes of the EEG net 14 positioned on the scalp of the subject 12 may be used to localize the seizure focus (e.g., from a source deep within the brain or at the cortical surface). In other words, signals to/from the EEG sensors of the EEG net 14 are used to record brain activity of the subject 12 from regions of the brain. The stimulation electrode 15, once implanted, is at an identifiable location. The HD EEG net 14 can record any stimulation pulse sent through the stimulation electrode 15. In other words, the stimulation electrode 15 mimics a seizure at a known spatial location.

[0021] The environment 10 further includes an MRI system (MRI) 20. The MRI system 20 acquires brain images to provide high resolution, structural images to identify and visualize the seizure focus. The MRI scan of the brain is provided to the computing device 18, where the MRI brain image is segmented to identify or delineate anatomical regions of the brain. For instance, image segmentation software in the computing device 18 may access an anatomical model of the brain from an anatomical model bank, the model comprising a surface representation of a shape-constrained

deformable brain model. Examples of suitable brain models are described in L.

Zagorchev, A. Goshtasby, K. Paulsen, T. McAllister, S. Young, and J. Weese, Manual annotation, 3-D shape reconstruction, and traumatic brain injury analysis, Int'l Workshop Multimodal Brain Image Analysis (MBIA), Toronto, Calif., September 2011 , and L.

Zagorchev, C. Meyer, T. Stehle, R. Kneser, S. Young, and J. Weese, Evaluation of Traumatic Brain Injury patients using a shape-constrained deformable model, Int'l Workshop Multimodal Brain Image Analysis (MBIA), Toronto, Calif., September 2011 , all of which are incorporated by reference in their entirety. Additional information on shape constrained deformable models may be found in“Shape-constrained deformable models and applications in medical imaging,” by Jurgen Weese, Irina Wachter-Stehle, Lyubomir Zagorchev, and Jochen Peters, pages 151 -184 of SCDM Book, Chapter 1400, and“Rapid fully automatic segmentation of subcortical brain structures by shape- constrained surface adaptation,” Fabian Wenzel, Carsten Meyer, Thomas Stehle, Jochen Peters, Susanne Siemonsen, Christian Thaler, Lyubomir Zagorchev, for the Alzheimer’s Disease Neuroimaging Initiative, Medical Image Analysis 46 (2018), pages 141-161 , each of which are also incorporated by reference. Other models are also contemplated herein. The image segmentation software is configured to segment the brain based on the anatomy represented in the model by performing an initial registration between the model and the MRI brain image, transforming the model to the brain anatomy based on a transform (e.g., the Hough transform), performing a parametric adaptation of the model, and performing a deformable adaptation of the model. Other known techniques can alternatively be used. The software delineates or identifies (e.g., using image recognition software) one or more segmented structures of the segmented MRI data.

[0022] The computing device 18 may render the images on a user interface 22 (e.g., display screen) and/or store the image data at a data repository 24. For instance, the user interface 22 enables the quantitative/visual feedback of activity with respect to functional areas of interest. The user interface 22 may be integral to the computing device 18 or be embodied as a separate device. The data repository 24 may include one or more of a picture archiving and communication system (PACS), a radiology information system (RIS), a hospital information system (HIS), an electronic medical record (EMR) database, a surgical navigation system, a server, a computer, and/or other data repository. The computing device 18 may export data corresponding to brain activity to the MRI space for import to surgical navigation systems, which may enable planning of an optimal ablation trajectory based on retrospective data (e.g. activity within ablated areas, tracts, laser trajectory, patient outcome, etc.) from the same procedure in different subjects. The user interface 22 and/or data repository 24 may be local to the MRI system 20 or remotely located (e.g., separate room, separate building, or separate region) and accessed over a network.

[0023] In one embodiment, the environment 10 further comprises a computed tomography (CT) system 26. HD-EEG scalp recording can be performed on patients with the stimulation electrode 15 as well as pre-op MRI and post-op CT. The CT may be registered with the pre-op MRI for accurate spatial localization of the stimulation electrode(s) 15, which may be localized through ESI. In other words, the CT is registered to the MRI to localize the stimulation electrode 15 (e.g., since the stimulation electrode 15 may not be MRI compatible, and hence has to be imaged with a CT). In some embodiments, an MRI compatible stimulation electrode 15 may be used, obviating the need for the registration through the MRI system 20. [0024] It is noted that the aforementioned functionality described for the environment 10 may reside in locations other than that described in association with FIG. 1. For instance, one or more of the functionality of the computing device 18 may reside in the processing device 16, or elsewhere (e.g., remote server). In some embodiments, the processing device 16 and the computing device 18 may provide for a distributed processing. In some embodiments, one or more stimulation electrodes may be used.

[0025] Flaving described an example environment 10 in which certain

embodiments of an objective-based electrical source imaging method may be implemented, attention is directed to FIG. 2, which illustrates one embodiment of an example computing device 28. The computing device 28, also referred to herein as simply an apparatus, may comprise the functionality of the computing device 18 of FIG.

1. In some embodiments, the computing device 28 may comprise one or more functionality of the processing device 16 of FIG. 1. The computing device 28 comprises co-located software and hardware components collectively embodied as a computing device (which may include a medical device). It should be appreciated that, in some embodiments, the functionality of an objective-based electrical source imaging method may be performed in one or more devices that reside local to an imaging system or that reside remote from the imaging system (e.g., in a cloud-based platform, server farm, web servers, application server, etc.). In some embodiments, plural devices remote from each other (e.g., client-server relationship) may collectively perform the

functionality of an objective-based electrical source imaging method in distributed processing fashion. One having ordinary skill in the art should appreciate in the context of the present disclosure that the example computing device 28, is merely illustrative of one embodiment, and that some embodiments of computing devices may comprise fewer or additional components, and/or some of the functionality associated with the various components depicted in FIG. 2 may be combined, or further distributed among additional modules or computing devices, in some embodiments. It should be appreciated that certain well-known components of computer systems are omitted here to avoid obfuscating relevant features of the computing device 28. [0026] In one embodiment, the computing device 28 comprises one or more processors 30 (e.g., 30A...30N), input/output interface(s) 32 , one or more user interfaces 34, which may include one or more of a keyboard, mouse, microphone, speaker, display device, etc.), and memory 36, all coupled to one or more data busses, such as data bus (DBUS) 38 . In some embodiments, the user interfaces 34 may be coupled directly to the data bus 38. The user interfaces 34 may include the user interface 22 (FIG. 1 ). The memory 36 may include any one or a combination of volatile memory elements (e.g., random-access memory RAM, such as DRAM, and SRAM, etc.) and nonvolatile memory elements (e.g., ROM, Flash, solid state, EPROM,

EEPROM, hard drive, tape, CDROM, etc.). The memory 36 may store a native operating system, one or more native applications, emulation systems, or emulated applications for any of a variety of operating systems and/or emulated hardware platforms, emulated operating systems, etc. In some embodiments, a separate storage device (STOR DEV) may be coupled to the data bus 38 or as a network-connected device (or devices) via the I/O interfaces 32 and one or more networks. In some embodiments, the storage device may serve as the data repository 24 (FIG. 1 ).

[0027] In the depicted embodiment, the computing device 28 may be coupled to an imaging system or systems (e.g., MRI system 20, CT system 26) and processing device 16 (FIG. 1 ) via the I/O interfaces 32, though it should be appreciated that the connection may be achieved via one or more networks in some embodiments or according to other known connections or interconnections. The storage device may be embodied as persistent memory (e.g., optical, magnetic, and/or semiconductor memory and associated drives). In some embodiments, the storage device and/or memory 36 may store a model bank, scans, and information (e.g., location information, such as the stimulation electrode location(s) as determined via the CT/MRI). The memory 36 and/or storage device may each be considered as a non-transitory computer-readable storage medium.

[0028] In the embodiment depicted in FIG. 2, the memory 36 comprises an operating system 40 (e.g., LINUX, macOS, Windows, etc.), and objective-based electrical source imaging software 42, which in one embodiment, includes the

application programming interface 19 (though not limited to being a module of the electrical source imaging software 42). The objective-based electrical source imaging software 42 includes one or more modules (executable code or generally, instructions) corresponding to functionality described in association with processing of inputs from the EEG net 14 and stimulation of the stimulation electrode 15. In one embodiment, the objective-based electrical source imaging software 42 estimates a location of an electrical source in a brain using an electrical source imaging technique, and evaluates accuracy of the electrical source imaging technique based on information associated with the stimulation electrode 15. The electrical source imaging technique receives as input the electric potentials from the sensing electrodes of the EEG net 14 and uses numerical methods such as the boundary element method (BEM), the finite difference method (FDM), and the finite element method (FEM) based on individual MRI (or atlases in some embodiments) to perform electrical source imaging (e.g., to estimate the location of the electrical source in the brain). The receipt of the information associated with the stimulation electrode 15 enables a reference or ground truth information as a basis for comparison to the estimated electrical source imaging locations to determine the accuracy of the estimates. In short, one goal of this process is to determine reconstructed source location using electrical source imaging. The distance between the known location of the stimulation electrode and the estimated source location from electrical source imaging shows the reconstruction accuracy. The objective-based electrical source imaging software 42 is further configured to trigger a stimulation current (via cooperation with the processing device 16) through the stimulation electrode(s) 15 based on one or more stimulation parameters, where the stimulation current is applied concurrently with the sensing by the electrodes of the EEG net 14. The trigger may be achieved on demand via the application programming interface 19, which also enables the definition and updates to the stimulation

parameters. The objective-based electrical source imaging software 42 is further configured to assess an impact of the one or more stimulation parameters on the accuracy, the assessment based on an absolute distance between the estimated source and the information.

[0029] As set forth above, the triggering of stimulation signals may be facilitated (or enabled) through the use of one or more remote procedure calls (RPCs) or generally one or more APIs (e.g., API 19) that may define one or more parameters that are passed between a calling application and other software code such as an operating system, library routine, function that provides a service, that provides data, or that performs an operation or a computation. The API may be implemented as one or more calls in program code that send or receive one or more parameters through a parameter list or other structure based on a call convention defined in an API specification document. A parameter may be a constant, a key, a data structure, an object, an object class, a variable, a data type, a pointer, an array, a list, or another call. API calls and parameters may be implemented in any programming language. The programming language may define the vocabulary and calling convention that a programmer employs to access functions supporting the API. In some implementations, an API call may report to an application the capabilities of a device running the application, including input capability, output capability, processing capability, power capability, and

communications capability.

[0030] Note that reference to software may include software, firmware, middleware, and/or microcode or microinstructions. In some embodiments, functionality of the software may be implemented via hardware (e.g., circuitry, including application- specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), vector processors, tensor processing units, etc.). The memory 36 further comprises a communications module 44. The communications module 44 comprises software that is configured to enable the communication of information (via the I/O interfaces 32) among other systems and/or devices.

[0031] Execution of the objective-based electrical source imaging software 42 may be implemented by the one or more processors 30 under the management and/or control of the operating system 40. The processor(s) 30 may be embodied as a custom-made or commercially available processor, including a single or multi-core central processing unit (CPU), tensor processing unit (TPU), graphics processing unit (GPU), vector processing unit (VPU), or an auxiliary processor among several processors, a semiconductor based microprocessor (in the form of a microchip), a macroprocessor, one or more application specific integrated circuits (ASICs), field programmable gate arrays (FPGUs), a plurality of suitably configured digital logic gates, and/or other known electrical configurations comprising discrete elements both individually and in various combinations to coordinate the overall operation of the computing device 28.

[0032] The I/O interfaces 32 comprise hardware and/or software to provide one or more interfaces to other systems or devices, including the processing device 16, the data repository 24 (FIG. 1 ), among other devices. The I/O interfaces 32 may include a cable, wireless, and/or cellular modem(s), and/or establish communications with other devices or systems via an Ethernet connection, hybrid/fiber coaxial (HFC), copper cabling (e.g., digital subscriber line (DSL), asymmetric DSL, etc.), using one or more of various communication protocols (e.g., TCP/IP, UDP, etc.). In general, the I/O interfaces 32, in cooperation with the communications module 44 comprises suitable hardware to enable communication of information via PSTN (Public Switched

Telephone Networks), POTS, Integrated Services Digital Network (ISDN), Ethernet, Fiber, DSL/ADSL, Wi-Fi, cellular (e.g., 3G, 4G, 5G, Global System for Mobile

Communications (GSM), General Packet Radio Service (GPRS), etc.), Bluetooth, near field communications (NFC), Zigbee, among others, using TCP/IP, UDP, HTTP, DSL.

[0033] The user interfaces 34 may include a keyboard, mouse, microphone, display, immersive head set, etc., which enable input and/or output by a clinician, technician, or other user. In some embodiments, the user interfaces 34 may cooperate with associated software to enable augmented reality or virtual reality, or visualization may be achieved in connection with other devices via the I/O interfaces 32. In one embodiment, the user interfaces 34 presents the accuracy of the electrical source imaging technique to a user (e.g., medical professional, technician, etc.).

[0034] When certain embodiments of the computing device 28 are implemented at least in part with software (including firmware, middleware, microcode, etc.), it should be noted that the objective-based electrical source imaging software 42 can be stored on any one of a variety of non-transitory computer-readable (storage) medium for use by, or in connection with, a variety of computer-related systems or methods. In the context of this document, a computer-readable storage medium may comprise an electronic, magnetic, optical, or other physical device or apparatus that may contain or store a computer program (e.g., executable code or instructions) for use by or in connection with a computer-related system or method. The software may be embedded in a variety of computer-readable storage mediums for use by, or in connection with, an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.

[0035] When certain embodiments of the computing device 28 are implemented at least in part with hardware, such functionality may be implemented with any or a combination of the following technologies, which are all well-known in the art: a discrete logic circuit(s) having logic gates for implementing logic functions upon data signals, an application specific integrated circuit (ASIC) having appropriate combinational logic gates, a programmable gate array(s) (PGA), a field programmable gate array (FPGA), TPUs, GPUs, and/or other accelerators/co-processors, etc.

[0036] In view of the above description, it should be appreciated that one embodiment of an objective-based electrical source imaging method, depicted in FIG. 3 and denoted as method 46, which is shown bounded by a start and end, comprises estimating a location of an electrical source in a brain using an electrical source imaging technique (48); and evaluating accuracy of the electrical source imaging technique based on information associated with a stimulation electrode implanted in the brain (50).

[0037] Note that the method 46 may be implemented by the apparatus 28, or by plural devices in some embodiments.

[0038] Certain embodiments of an objective-based electrical source imaging method enables, via the simultaneously gathering of EEG data during neurostimulation, quantification of the effects of: MRI segmentation (e.g., which defines tissue

conductivities, which are used in an electrical source imaging technique) during head model creation; electrode positioning on source localization accuracy of non-invasive ESI; improving accuracy of ESI algorithms; evaluating importance of electrode density in a FID EEG configuration; and developing new (extended) electrode montages for EEG.

[0039] Any process descriptions or blocks in flow diagrams should be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process, and alternate implementations are included within the scope of the embodiments in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present disclosure. In some embodiments, one or more steps may be omitted, or further steps may be added.

[0040] In one embodiment, an apparatus is disclosed, comprising: a memory comprising instructions; and a processor configured by the instructions to: estimate a location of an electrical source in a brain using an electrical source imaging technique; and evaluate accuracy of the electrical source imaging technique based on information associated with a stimulation electrode implanted in the brain.

[0041] In one embodiment, the preceding apparatus, wherein the information comprises a known location of the stimulation electrode, wherein the processor is configured by the instructions to evaluate the accuracy by comparing the estimated location with the known location.

[0042] In one embodiment, any one of the preceding apparatuses, wherein the processor is further configured by the instructions to determine the known location based on registration of a computed tomography scan with a magnetic resonance imaging scan.

[0043] In one embodiment, any one of the preceding apparatuses, wherein the processor is further configured by the instructions to receive scalp potentials from one or more sensing electrodes, and based on the scalp potentials, estimate the location of the electrical source using the electrical source imaging technique.

[0044] In one embodiment, any one of the preceding apparatuses, wherein the processor is further configured by the instructions to trigger a stimulation current through the stimulation electrode based on one or more stimulation parameters.

[0045] In one embodiment, any one of the preceding apparatuses, wherein the processor is further configured by the instructions to cause application of the stimulation current via the stimulation electrode concurrently with the sensing of the scalp potentials. [0046] In one embodiment, any one of the preceding apparatuses, wherein the processor is further configured by the instructions to assess an impact of the one or more stimulation parameters on the accuracy, the assessment based on an absolute distance between the estimated source and the information.

[0047] In one embodiment, any one of the preceding apparatuses, wherein the processor is further configured by the instructions to trigger the stimulation current via an application programming interface, the one or more stimulation parameters updatable via the application programming interface.

[0048] In one embodiment, a method for implementation by any one of the preceding apparatuses is disclosed.

[0049] In one embodiment, a non-transitory computer-readable storage medium that comprises instructions to implement functionality of any one of the preceding apparatuses is disclosed.

[0050] In one embodiment, a system comprising any one of the preceding apparatuses is disclosed, further comprising the one or more sensing electrodes and the stimulation electrode, the stimulation electrode implanted on a cortical surface of the brain.

[0051] In one embodiment, the preceding system, wherein the one or more sensing electrodes are part of a high-density, electroencephalography net.

[0052] In one embodiment, any one of the preceding systems, further comprising a magnetic resonance imaging system used in the electrical source imaging technique.

[0053] In one embodiment, any one of the preceding systems, further comprising a computed tomography system for obtaining the information.

[0054] In one embodiment, any one of the preceding systems, further comprising a display device, the display device providing an indication of the accuracy.

[0055] While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive; the invention is not limited to the disclosed embodiments. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims. Note that various combinations of the disclosed embodiments may be used, and hence reference to an embodiment or one embodiment is not meant to exclude features from that embodiment from use with features from other embodiments. In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article“a” or “an” does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. A computer program may be stored/distributed on a suitable medium, such as an optical medium or solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms. Any reference signs in the claims should be not construed as limiting the scope.

Claims

CLAIMS At least the following is claimed:

1. An apparatus (28), comprising:

a memory (36) comprising instructions (42); and

a processor (30) configured by the instructions to:

estimate a location of an electrical source in a brain using an electrical source imaging technique (48); and

evaluate accuracy of the electrical source imaging technique based on information associated with a stimulation electrode implanted in the brain (50).

2. The apparatus of the preceding claim, wherein the information comprises a known location of the stimulation electrode (15), wherein the processor is configured by the instructions to evaluate the accuracy by comparing the estimated location with the known location.

3. The apparatus of any one of the preceding claims, wherein the processor is further configured by the instructions to determine the known location based on registration of a computed tomography scan (26) with a magnetic resonance imaging scan (20).

4. The apparatus of any one of the preceding claims, wherein the processor is further configured by the instructions to receive scalp potentials from one or more sensing electrodes (14), and based on the scalp potentials, estimate the location of the electrical source using the electrical source imaging technique.

5. The apparatus of any one of the preceding claims, wherein the processor is further configured by the instructions to trigger a stimulation current through the stimulation electrode based on one or more stimulation parameters.

6. The apparatus of any one of the preceding claims, wherein the processor is further configured by the instructions to cause application of the stimulation current via the stimulation electrode concurrently with the sensing of the scalp potentials.

7. The apparatus of any one of the preceding claims, wherein the processor is further configured by the instructions to assess an impact of the one or more stimulation parameters on the accuracy, the assessment based on an absolute distance between the estimated source and the information.

8. The apparatus of any one of the preceding claims, wherein the processor is further configured by the instructions to trigger the stimulation current via an application programming interface (19), the one or more stimulation parameters updatable via the application programming interface.

9. A method (46) for implementation by the apparatus of any one of the preceding claims.

10. A non-transitory computer-readable storage medium (36) that comprises instructions to implement functionality of the apparatus of any one of the preceding claims.

11. A system (10) in which the apparatus of any one of the preceding claims operates, further comprising the one or more sensing electrodes and the stimulation electrode, the stimulation electrode implanted on a cortical surface of the brain.

12. The system of any one of the preceding claims, wherein the one or more sensing electrodes are part of a high-density, electroencephalography net (14).

13. The system of any one of the preceding claims, further comprising a magnetic resonance imaging system (20) used in the electrical source imaging technique.

14. The system of any one of the preceding claims, further comprising a computed tomography system (26) for obtaining the information.

15. The system of any one of the preceding claims, further comprising a display device (32), the display device providing an indication of the accuracy.