MAGNETOENCEPHALOGRAPHY APPARATUS AND METHOD

Abstract

Disclosed is a magnetoencephalography apparatus (100) and a method. The apparatus comprises a plurality of magnetic sensors, one or more processors and one or more memories. The method comprises obtaining a reference data, calculating from the reference data a reference basis, obtaining a source basis, obtain a source data, adding together the source basis and the reference basis to form a joint basis and determine an estimate for the magnetic brain activity of the source by parametrizing the source data in the joint basis.

Claims

1. A magnetoencephalography apparatus comprising: a plurality of magnetic sensors arranged for measurement of magnetic brain activity originating within a first volume, the plurality of magnetic sensors being arranged for positioning within a second volume, which is outside the first volume; one or more processors coupled to the plurality of magnetic sensors for controlling the measurement of magnetic brain activity; and one or more memories comprising computer program code, the one or more memories and the computer program code configured to cause the one or more processors to: obtain a reference data corresponding to one or more measurements of the plurality of magnetic sensors in the absence of sources of magnetic brain activity in the first volume; calculate from the reference data a reference basis, which represents magnetic activity in the absence of sources of magnetic brain activity in the first volume, in a signal space defined by the plurality of magnetic sensors; obtain a source basis, which represents magnetic brain activity of a human brain positioned in the first volume, in the signal space defined by the plurality of magnetic sensors; obtain a source data corresponding to one or more measurements of the plurality of magnetic sensors in the presence of a source of magnetic brain activity in the first volume; add together the source basis and the reference basis to form a joint basis in the signal space defined by the plurality of magnetic sensors; and determine an estimate for the magnetic brain activity of the source by parametrizing the source data in the joint basis.

2. The apparatus according to claim 1, wherein the magnetic sensors of the plurality of magnetic sensors are either all magnetometers or all gradiometers.

3. The apparatus according to claim 1, wherein the magnetic sensors of the plurality of magnetic sensors are gradiometers.

4. The apparatus according to claim 1, wherein the magnetic sensors of the plurality of magnetic sensors are planar gradiometers.

5. The apparatus according to claim 1, wherein the plurality of magnetic sensors is arranged to measure the magnetic brain activity with 48-256 signal channels.

6. The apparatus according to claim 1, wherein arranged to automatically perform the one or more measurements of the plurality of magnetic sensors to obtain the reference data.

7. The apparatus according to claim 1, wherein the source basis is obtained based on a deterministic solution for the Maxwell's equations for the magnetic brain activity of a human brain.

8. The apparatus according to claim 1, wherein the source basis is obtained based on a source model for the magnetic brain activity of a human brain comprising one or more stochastically positioned sources of magnetic brain activity.

9. A method comprising: obtaining a reference data corresponding to one or more measurements of a plurality of magnetic sensors in the absence of sources of magnetic brain activity in a first volume; wherein the plurality of magnetic sensors have been arranged for measurement of magnetic brain activity originating within the first volume and positioned within a second volume, which is outside the first volume; calculating from the reference data a reference basis, which represents magnetic activity in the absence of sources of magnetic brain activity in the first volume, in a signal space defined by the plurality of magnetic sensors; obtaining a source basis, which represents magnetic brain activity of a human brain positioned in the first volume, in the signal space defined by the plurality of magnetic sensors; obtaining a source data corresponding to one or more measurements of the plurality of magnetic sensors in the presence of a source of magnetic brain activity in the first volume; add together the source basis and the reference basis to form a joint basis in the signal space defined by the plurality of magnetic sensors; and determining an estimate for the magnetic brain activity of the source by parametrizing the source data in the joint basis.

10. The method according to claim 9, wherein the magnetic sensors of the plurality of magnetic sensors are either all magnetometers or all gradiometers, optionally planar gradiometers.

11. The method according to claim 9, wherein the plurality of magnetic sensors is arranged to measure the magnetic brain activity with 48-256 signal channels.

12. The method according to claim 9, wherein the one or more measurements of the plurality of magnetic sensors to obtain the reference data are performed automatically.

13. The method according to claim 9, wherein the source basis is obtained based on a deterministic solution for the Maxwell's equations for the magnetic brain activity of a human brain.

14. The method according to claim 9, wherein the source basis is obtained based on a source model for the magnetic brain activity of a human brain comprising one or more stochastically positioned sources of magnetic brain activity.

15. A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to perform operation comprising: obtaining a reference data corresponding to one or more measurements of a plurality of magnetic sensors in the absence of sources of magnetic brain activity in a first volume, wherein the plurality of magnetic sensors have been arranged for measurement of magnetic brain activity originating within the first volume and positioned within a second volume, which is outside the first volume; calculating from the reference data a reference basis, which represents magnetic activity in the absence of sources of magnetic brain activity in the first volume, in a signal space defined by the plurality of magnetic sensors; obtaining a source basis, which represents magnetic brain activity of a human brain positioned in the first volume, in the signal space defined by the plurality of magnetic sensors; obtaining a source data corresponding to one or more measurements of the plurality of magnetic sensors in the presence of a source of magnetic brain activity in the first volume; add together the source basis and the reference basis to form a joint basis in the signal space defined by the plurality of magnetic sensors; and determining an estimate for the magnetic brain activity of the source by parametrizing the source data in the joint basis.

16. The computer program product according to claim 15, wherein the magnetic sensors of the plurality of magnetic sensors are either all magnetometers or all gradiometers, optionally planar gradiometers.

17. The computer program product according to claim 15, wherein the plurality of magnetic sensors is arranged to measure the magnetic brain activity with 48-256 signal channels.

18. The computer program product according to claim 15, wherein the one or more measurements of the plurality of magnetic sensors to obtain the reference data are performed automatically.

19. The computer program product according to claim 15, wherein the source basis is obtained based on a deterministic solution for the Maxwell's equations for the magnetic brain activity of a human brain.

20. The computer program product according to claim 15, wherein the source basis is obtained based on a source model for the magnetic brain activity of a human brain comprising one or more stochastically positioned sources of magnetic brain activity.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings, which are included to provide a further understanding and constitute a part of this specification, illustrate examples and together with the description help to explain the principles of the disclosure. In the drawings:

[0032] FIG. 1 illustrates an apparatus according to an example in a side view,

[0033] FIG. 2 illustrates a method according to an example, and

[0034] FIG. 3 schematically illustrates an apparatus according to an example.

[0035] Like references are used to designate equivalent or at least functionally equivalent parts in the accompanying drawings.

DETAILED DESCRIPTION

[0036] The detailed description provided below in connection with the appended drawings is intended as a description of examples and is not intended to represent the only forms in which the example may be constructed or utilized. However, the same or equivalent functions and structures may be accomplished by different examples.

[0037] FIG. 1 shows an example of an apparatus 100, which can be a magnetoencephalography (MEG) apparatus. A measurement subject can be the brain of a human test subject 10. The apparatus 100 is arranged for measurement of the magnetic activity of a brain. For this purpose, the apparatus comprises a plurality of magnetic sensors 110 (“the sensors”), which are arranged for positioning proximate to the brain for measurement of brain activity. Since MEG is a noninvasive method, some or all of the sensors 110 may be positioned around the head of the test subject, for example so that the arrangement of the sensors 110 is substantially helmet-shaped. Some or all of the sensors 110 may also be positioned directly against the head of the test subject, for example when the corresponding sensors 110 are OPM sensors. This allows the positioning of the sensors 110 to adapt to the shape and/or size of the head. In an embodiment, the plurality of magnetic sensors 110 consist of or comprise OPM sensors 110. The apparatus 100 and/or the sensors 110 may be adapted for simultaneous or substantially simultaneous imaging of an entire brain.

[0038] A first volume is thereby a volume, where the brain is to be positioned and it may comprise an origin, for example substantially corresponding to the center point of the brain. The first volume is defined with respect to a second volume, where the sensors 110 are positioned during measurement. The first volume is thereby inside the second volume so that the second volume may enclose the first volume. The first volume may be substantially spherical. In this case, the origin may be located substantially at the center of the first volume. The union of the first volume and the second volume may be substantially spherical, in which case the origin may be located substantially at the center of the union. The first volume may be substantially the size of a human head. The second volume may be substantially the size of the volume required to contain the sensors 110, for example the size of a helmet or a MEG helmet positioned on a human head. The sensors 110 may be arranged to be positioned circumferentially or substantially circumferentially in the second volume. The sensors 110 may be arranged at one or more supports 112, for example a helmet-shaped support. This can be used to allow the positioning of the sensors 110 to substantially follow the curvature of a human head during measurement. The apparatus 100 may comprise a MEG helmet 114 comprising the support 112. The distances of the sensors 110 from each other and/or the origin are arranged to allow a MEG recording to be performed with the apparatus.

[0039] The sensors 110 may be magnetometers and/or gradiometers, in particular planar gradiometers. The apparatus 100 may be arranged to allow the measurement for brain activity to be performed using solely gradiometers or solely magnetometers. Each of the sensors 110 is arranged to provide one or more signal channels for measurement of magnetic brain activity and while the number of the sensors 100 may correspond to the number of signal channels, it is also possible to use multi-channel sensors providing more than one signal channel. However, the measurement of magnetic brain activity may be performed with a number of signal channels that is smaller than the previously used 306 channels. The number of signal channels may be less than 256, for a MEG recording of an entire brain. For example, the number of signal channels may be 48, 96, 148 or 220. With current levels of sensor noise, it has been found that using at least 96 signal channels provides a marked improvement in performance and using at least 148 signal channels may, in some embodiments, significantly improve the numerical stability of the signal processing. Naturally, the number of signal channels may be further increased to improve the capabilities of the apparatus 100. The number may be also larger than 306, or even larger than 700, for example for an apparatus 100 utilizing OPM sensors. The sensors 110 define a signal space as a space of magnetic signals measurable by the sensors 110. The signal space is a vector space and it can be spanned by a set of basis vectors. The number of basis vectors spanning the signal space may correspond to the number of signal channels. In practice, the effective dimension of the signal space useful for determining estimate for the magnetic brain activity may be smaller, even half of that or less.

[0040] The apparatus 100 may comprise a measurement device 300 arranged to collect measurement data from the sensors 110. While the measurement device 300 can be arranged connected to the sensors 110 with a wired and/or a wireless connection, using a wired connection allows reducing magnetic noise in the measurement environment.

[0041] FIG. 2 shows an example of a method 200 for determining magnetic brain activity, or an estimate thereof, which can be adapted as a signal processing method. The magnetic brain activity is determined for a source, such as a human brain, positioned in a first volume, as described above for the apparatus 100, which may be used for performing any or all parts of the method 200. For measurement, a plurality of magnetic sensors 110 is used and the sensors 110 can be as described above. In particular, the sensors 110 are arranged to be positioned within the second volume as described above, so that they can be used for a MEG recording of a source in the first volume. The method comprises several parts which may be performed independently from each other and/or in any order.

[0042] In the method, reference data is obtained corresponding to a reference measurement 210 with the plurality of magnetic sensors 110. This reference data can be used to determine the magnetic environment of the first volume so that it can be taken into account when determining the magnetic brain activity of the source. Typically, the magnetic environment involves an interference contribution that may be several magnitudes larger than the contribution from the magnetic brain activity of the source. In addition, the reference data allows capturing any non-idealities in the apparatus 100 and/or the sensors 110 used to perform the measurements, in particular a source measurement, where a source of magnetic brain activity is present in the first volume. A reference measurement can be performed any time, for example before and/or after the source measurement. A reference measurement may comprise, for example an MEG recording of one or more minutes in the absence of sources of magnetic activity in the first volume. When the reference measurement is performed in an MSR, the MSR may be empty of sources of magnetic brain activity.

[0043] The reference data is used to calculate a reference basis 220, which represents magnetic activity in the absence of sources of magnetic brain activity in the first volume. This allows utilizing the whole reference measurement in construction of the reference basis. This way, the reference data, or the signals measured during the reference measurement, can be divided into a set of basis vectors and, optionally, normalized. The reference basis may be orthogonal. The reference basis may be formed, for example, using principal component analysis for the reference data. A covariance matrix can be computed from the reference data and principal component analysis (PCA) can be applied to determine the spatial patterns which characterize the reference data. The number of basis vectors of the reference basis n.sub.ref may be the number of signal channels N minus the number of basis vectors of a source basis n.sub.s, i.e. n.sub.ref=N−n.sub.s, but it can also be smaller since this only means that the signals measured during the reference measurement are divided in another manner. For example, one or more of the basis vectors may correspond to clear interference shapes corresponding to a specific source of interference whereas one or more may correspond to general background interference, where reducing the size of the reference basis may increase the part of the reference data allocated for the latter basis vectors. It has been found that it can, in some instances, be enough to use a limited number of basis vectors in the reference basis. For example, the number of basis vectors in the reference basis may be at least 5-8. It has been found that in several currently relevant embodiments, it suffices to use at most 15-50 basis vectors for the reference basis. The basis vectors of the reference basis correspond to the interference contribution, which may comprise all signals arising in the absence of a source of magnetic brain activity.

[0044] The method also comprises obtaining a source basis 330, which corresponds to the magnetic brain activity of a general human brain. One or more techniques for describing the magnetic activity produced by a human brain may be used. In particular, the source basis may be determined purely deterministically or it may be determined using a stochastic soured model for a human brain. The source basis may be orthogonal. The source basis may be constructed using, for example, a minimum of 20-30 basis vectors. This may allow a MEG recoding to be provided corresponding to an entire brain. For efficiency, the source basis may be constructed using a maximum of 100-120 basis vectors, for example. The source basis may be determined with respect to the origin, for example using a series development with respect to the origin. The source basis can be determined or re-determined at any point when the method is performed. The basis vectors of the source basis may correspond to magnetic fields, which are irrotational and sourceless outside the second volume. In an embodiment, the source basis is obtained based on a deterministic solution to the Maxwell's equations for the magnetic brain activity of a human brain. One example for a possible way of determining the basis vectors is given in “The magnetostatic multipole expansion in biomagnetism: applications and implications” by Jussi Nurminen, ISBN 978-952-60-5710-1 (section 3.2, which is hereby incorporated by reference). In an embodiment, the source basis is obtamed based on a source model for the magnetic brain activity of a human brain comprising one or more stochastically positioned sources of magnetic brain activity. One example for a possible way of determining the basis vectors is given in “The magnetostatic multipole expansion in biomagnetism: applications and implications” by Jussi Nurminen, ISBN 978-952-60-5710-1 (section 5.7, which is hereby incorporated by reference). In one more example, the source basis may be determined by using a source model where a layer of magnetic dipoles is positioned between regions corresponding to the white matter and the gray matter of the human brain. For the source models mentioned above, a lead-field matrix may be calculated for which eigenvectors can be determined, for example by using a singular-value decomposition, to determine the source basis.

[0045] In the method, source data is obtained corresponding to a source measurement 240 with the plurality of magnetic sensors 110. This source data can be used to determine the magnetic activity of the source, e.g. a human brain. A source measurement can be performed any time, for example before and/or after the reference measurement.

[0046] To optimize the accuracy of the description of the magnetic environment, the positioning and/or orientation of the sensors 110 can be substantially the same during the reference measurement 210 and the source measurement 240. The reference measurement and/or the source measurement can be performed simultaneously or at least substantially simultaneously for all signal channels. Both the reference measurement and the source measurement can be performed as an attempt to determine magnetic brain activity in the first volume allowing the two measurements to correspond to a substantially similar interference contribution. The source basis and/or the reference basis may be linearly independent. The joint basis may also be linearly independent.

[0047] The source data basis and the reference basis are added together to form a joint basis 250 in the signal space defined by the sensors 110. The joint basis thereby comprises the basis vectors of both the source basis and the reference basis but since they are separate, or linearly-independent in particular, any signal can be expressed in the joint basis separately as a contribution corresponding to the reference basis and a contribution corresponding to the source basis. Since the source measurement involves an interference contribution and a contribution from the magnetic brain activity of the source, the former can now described as the contribution corresponding to the reference basis, whereas the latter can now be described as the contribution corresponding to the source basis. The interference contribution is typically much larger than the contribution from magnetic brain activity so that the more accurately it can be estimated the more accurately the brain magnetic activity of the source can be determined. An estimate is determined by parametrizing 260 the source data in the joint basis. The estimate can be determined as the part of the source data which, when expressed in the joint basis, corresponds to the basis vectors of the source basis.

[0048] Overall, a magnetic signal can be expressed as a linear combination of a set of basis vectors each weighed by an amplitude coefficient. Therefore, the contribution from the brain magnetic activity of the source can be expressed as a linear combination of the source basis weighed by the amplitude coefficients that are obtained by parameterization of the source data in the joint basis. Correspondingly, a total magnetic field can be expressed as a linear combination of the basis vectors of the joint basis each weighed by their own amplitude coefficient. With multiple signal channels, this can be expressed as a matrix equation, where the magnetic field can be obtained as a product of a matrix corresponding to the joint basis and a vector corresponding to the amplitude coefficients. Correspondingly, solving the amplitude coefficients corresponds to inverting the matrix so that, typically, the parametrization involves inverting the matrix describing the joint basis. For determining the magnetic brain activity of the source it will naturally be enough to determine corresponding amplitude coefficients. The magnetic signal can be determined or extrapolated at any location in space, for example at the origin and/or at the location of a magnetic sensor. Non-idealities of the sensor array, such as uncertainty of exact sensor position, orientation and calibration, are embedded in the measured reference data and fine-calibration adjustments are therefore not needed. In fact, a gradiometric array could not even utilize the fine-calibration procedure currently used for SSS-based systems.

[0049] While not utilizing the SSS as such, the method can be used with all the major improvements available to the SSS method. In particular, the method allows compensating for signal disturbances caused by head movements inside the second volume. Moreover, disturbance signals from nearby interference sources, such as magnetized objects in subject's mouth or on the scalp, can be identified and the information can be used to improve the estimate for the magnetic brain activity. For this, methods similar to SSS expansions and time-domain subspace methods can be used. Temporal waveforms identified as disturbances can be projected out and interference-free MEG signals can be reconstructed using the source basis. In addition, the method can be extended with spatial means by augmenting the reference basis by adding one or more vectors, such as unit vectors, for isolating individual channels or one or more vectors identified in any way including a separate measurement and representing known disturbance which can be separately explained. Another spatial extension employs cross-validation for separating the uncorrelated channel-specific noise signals. The method can also utilize covariance-based a priori information in defining the amplitude coefficients of the source basis for reducing the background noise of the signals.

[0050] FIG. 3 shows an example of an apparatus 100. The apparatus 100 comprises one or more processors 310 and one or more memories 320 comprising computer program code. These can together be configured to cause the one or more processors to perform any or all parts of the method 200. For example, this may involve controlling the sensors 110 to perform the reference measurement and/or the source measurement. Further, it may involve using the source data to determine an estimate for the magnetic brain activity. The apparatus 100 may comprise a user interface for inputting control commands to the apparatus 100 and/or communicating the source data and/or information indicative thereof to a user. The user interface 330 may be arranged to prompt a user to initiate the reference measurement and/or the source measurement. The apparatus 100 may also be arranged to automatically obtain reference data and/or calculate a reference basis, for example daily, weekly or monthly. The apparatus 100 may be arranged to automatically perform the reference measurement in accordance with a schedule, which schedule may be adjustable and/or self-adjusting. The apparatus 100 may comprise one or more detectors 340 arranged to detect whether the reference measurement and/or the source measurement can be performed, for example by detecting whether any potential sources are present in the first volume or near the apparatus 100. The one or more detectors 340 may comprise, for example, a movement detector and/or a thermal detector, which may be arranged to detect an indication of the presence of a human in the first volume or near the apparatus 100. Prior to performing the reference measurement, the apparatus 100 may thus be arranged to use the one or more detectors 340 to evaluate whether the reference measurement can be performed. The apparatus 100 may also comprise one or more detectors for detecting an indication on whether a source is present at the apparatus for the source measurement. The apparatus 100 may comprise a separate measurement device 300 arranged to be coupled to the sensors 110. The measurement device 300 may comprise any combination of a processor 310, a memory 320, a user interface 330 and a detector 340.

[0051] The apparatus 100 may also comprise one or more detectors 340 arranged to measure the interference contribution during the source measurement. This may comprise one or more magnetometers and/or gradiometers. The measurement results obtained by these detectors may be used to improve the estimate for the magnetic brain activity of the source. These detectors 340 can be arranged outside the first volume and even outside the second volume to ascertain that they predominately measure the interference contribution, e.g. the magnetic fields from external sources. They may also be oriented away from the first volume.

[0052] As an example, the method has been compared to the traditional methods utilizing the SSS method. For this purpose a shielding factor is defined as the ratio of signal channel signal-vector norms before and after signal processing. The channels are picked to Ncomponent signal vector b (components b.sub.1 . . . N) and the norm M is computed as

[00001]$M\left(t\right)=\sqrt{\underset{k=1}{\overset{N}{.Math.}}{\left[b_k\left(t\right)\right]}^2},$

where the sum runs over the length of the signal vector. Possible time dependence in any variable is indicated in parentheses with “(t)”. The norm is therefore the square root of the sum of squares of all the components of the signal vector. The shielding factor SF is the ratio of the norm from original data (M.sub.raw) and processed data (M.sub.post), SF=M.sub.raw/M.sub.post.

[0053] Shielding factor SF is estimated as a function of time. In the example, the mean values are tabulated over two-minute measurement duration. Channels with spurious artifacts have been excluded. SF has been evaluated separately for magnetometer and gradiometer channels for empty room recordings performed for nine TRIUX systems. For each system, recording has been analysed with large interference, EAS and IAS were not applied.

[0054] The shielding factors between different configurations and methods have been compared for nine systems and two recordings using two approaches for interference suppression: [0055] 1. SSS with fine-calibration. The fine-calibration adjustment for 306 channels was computed from large interference data. [0056] 2. Current method. The method as disclosed in the present application.

[0057] The shielding factors are collected in Table 1, where the type of magnetic sensors in the system is indicated on the first row and the channel geometry on the second row. For SSS, two fine-calibration models have been used: standard 1D-imbalance model and an improved 3D-imbalance model.

[0058] Both the current method and the SSS method with 3D-imbalance model have been found to outperform the standard SSS fine-calibration with 1D-imbalance model. The current method yields the best gradiometer shielding factor, where the improved result may be obtamed even with a reduced number of signal channels. Similar comparisons have been made also with the current method and the SSP method yielding similar results. With an extended set of tests, the current method has been found to provide an alternative to not only conventional SSS-based methods but conventional SSP-based methods as well. In particular, the current method may be used to improve shielding factors even without a fine calibration. It may also be used to significantly reduce the number of required channels for a MEG recording.

TABLE-US-00001 TABLE 1 Raw signal shielding factors SF for SSS with the 1D- or 3D-imbalance model and for the current method. magnetometers gradiometers 306 306 306 306 306 204 system SSS 1D SSS 3D now SSS 1D SSS 3D now 3131 168 388 304 4 8 10 3132 234 538 510 4 10 31 3133 173 434 368 5 9 13 3134 379 775 747 5 10 44 3136 108 217 251 3 7 14 3137 339 742 882 6 15 72 3138 231 500 629 6 12 36 3140 303 768 705 5 12 38 3141 485 987 981 10 17 60 mean 269 594 597 5 11 35

[0059] The apparatus as described above may be implemented in software, hardware, application logic or a combination of software, hardware and application logic. The application logic, software or instruction set may be maintained on any one of various conventional computer-readable media. A “computer-readable medium” may be any media or means that can contain, store, communicate, propagate or transport the instructions for use by or in connection with an instruction execution system, apparatus, or device, such as a computer. A computer-readable medium may comprise a computer-readable storage medium that may be any media or means that can contain or store the instructions for use by or in connection with an instruction execution system, apparatus, or device, such as a computer. The examples can store information relating to various processes described herein. This information can be stored in one or more memories, such as a hard disk, optical disk, magneto-optical disk, RAM, and the like. One or more databases can store the information used to implement the embodiments. The databases can be organized using data structures (e.g., records, tables, arrays, fields, graphs, trees, lists, and the like) included in one or more memories or storage devices listed herein. The databases may be located on one or more devices comprising local and/or remote devices such as servers. The processes described with respect to the embodiments can include appropriate data structures for storing data collected and/or generated by the processes of the devices and subsystems of the embodiments in one or more databases.

[0060] All or a portion of the embodiments can be implemented using one or more general purpose processors, microprocessors, digital signal processors, micro-controllers, and the like, programmed according to the teachings of the embodiments, as will be appreciated by those skilled in the computer and/or software art(s). Appropriate software can be readily prepared by programmers of ordinary skill based on the teachings of the embodiments, as will be appreciated by those skilled in the software art. In addition, the embodiments can be implemented by the preparation of application-specific integrated circuits or by interconnecting an appropriate network of conventional component circuits, as will be appreciated by those skilled in the electrical art(s). Thus, the embodiments are not limited to any specific combination of hardware and/or software.

[0061] The different functions discussed herein may be performed in a different order and/or concurrently with each other.

[0062] Any range or device value given herein may be extended or altered without losing the effect sought, unless indicated otherwise. Also any example may be combined with another example unless explicitly disallowed.

[0063] Although the subject matter has been described in language specific to structural features and/or acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as examples of implementing the claims and other equivalent features and acts are intended to be within the scope of the claims.

[0064] It will be understood that the benefits and advantages described above may relate to one embodiment or may relate to several embodiments. The embodiments are not limited to those that solve any or all of the stated problems or those that have any or all of the stated benefits and advantages. It will further be understood that reference to ‘an’ item may refer to one or more of those items.

[0065] The term ‘comprising’ is used herein to mean including the method, blocks or elements identified, but that such blocks or elements do not comprise an exclusive list and a method or apparatus may contain additional blocks or elements.

[0066] Although the invention has been the described in conjunction with a certain type of apparatus and/or method, it should be understood that the invention is not limited to any certain type of apparatus and/or method. While the present inventions have been described in connection with a number of examples, embodiments and implementations, the present inventions are not so limited, but rather cover various modifications, and equivalent arrangements, which fall within the purview of prospective claims. Although various examples have been described above with a certain degree of particularity, or with reference to one or more individual embodiments, those skilled in the art could make numerous alterations to the disclosed examples without departing from the scope of this specification.