System and method for monitoring brain health of a subject

Abstract

Disclosed is a system (200) and a method (1300) for monitoring brain health of a subject. The system (200) comprising a functional near infrared spectroscopy (fNIRS) sensor (102a), at least one in-ear electroencephalographic (EEG) device (104), and at least one processor (202). The at least one processor (202) is configured to receive fNIRS signal (304) and EEG signal (302) from the fNIRS sensor (102a) and the EEG device (104) respectively. The at least one processor (202) is configured to compute a brain health index (BHI) value corresponding to each of the EEG signal (302) and the fNIRS signal (304). The at least one processor (202) is configured to determine a state of the brain health based on comparing mean BHI value corresponding to each of the EEG signal (302) and the fNIRS signal (304) with a predefined threshold value.

Description

SYSTEM AND METHOD FOR MONITORING BRAIN HEALTH OF A SUBJECTFIELD OF THE INVENTION

[0001] The present disclosure generally relates to the field of medical devices and health management systems, and more particularly relates to a method and system for monitoring brain health of a subject.BACKGROUND

[0002] Brain injuries can be complex and require multidisciplinary care involving specialists such as neurologists, neurosurgeons, neuropsychologists, rehabilitation specialists, and others. However, not all healthcare facilities may have the necessary expertise or resources to provide specialized care for brain injuries, particularly in rural or underserved areas. This could result in delays in diagnosis, inadequate management, or lack of access to specialized treatments, leading to suboptimal care for patients with brain injuries.

[0003] Existing methods and technologies such as cognitive behavioural therapy (CBT), neurofeedback / assistive technologies, time management strategies, and social support systems can be used for managing brain injuries. However, these approaches may lack standardized protocols, leading to inconsistencies in care and potentially impacting patient outcomes.

[0004] Further, brain injuries can vary widely in their severity, type, and location, and accordingly appropriate diagnosis and management can differ depending on these factors. Moreover, brain injuries can also have long-lasting effects on physical, cognitive, emotional, and social functioning; ongoing rehabilitation, and follow-up care is crucial for maximizing recovery and improving quality of life. Some patients may face challenges in accessing and affording long-term rehabilitation services or may not have access to comprehensive rehabilitation programs that address their specific needs.

[0005] Therefore, in developing countries like India, there is a persistent gap in providing quality care and resources to deal with neurodegenerative patients. Additionally, the diverse range of disabilities and their impact on brain injury survivors further creates a pressing need for providing effective post operative neurorehabilitation care.

[0006] Thus, there is a need for a system and method for monitoring brain health of a patient that address the abovementioned drawbacks of the existing system.SUMMARY

[0007] This summary is provided to introduce a selection of concepts, in a simplified format, that is further described in the detailed description of the disclosure. This summary is neither intended to identify key or essential inventive concepts of the disclosure nor is it intended for determining the scope of the disclosure.

[0008] The present disclosure relates to a system for monitoring brain health of a subject. The system includes at least one functional near infrared spectroscopy (fNIRS) sensor, at least one in-ear electroencephalographic (EEG) sensor, and at least one processor. The fNIRS sensor is configured to detect an fNIRS signal from brain of the subject. The EEG sensor is configured to detect an EEG signal based on an electrical activity generated in the brain. The at least one processor is in communication with the at least one fNIRS sensor and the at least one in-ear EEG sensor. The at least one processor is configured to receive the fNIRS signal and the EEG signal from the at least one fNIRS sensor and the at least one in-ear EEG sensor respectively. The at least one processor may be configured to identify one or more characteristics corresponding to each of the fNIRS signal and the EEG signal using a feature extraction technique, the one or more characteristics are indicative of a neural activity of the brain. The at least one processor may be configured to compute a brain health index (BHI) value corresponding to each of the EEG signal and the fNIRS signal based on the identified one or more characteristics, the BHI value is indicative of a neurological condition of the subject. The at least one processor may be configured to calculate a mean BHI value based on correlating the BHI value corresponding to each of the EEG signal and the fNIRS signal. The at least one processor may be configured to determine a state of the brain health based on comparing the mean BHI value with a predefined threshold value.

[0009] The present disclosure also relates to a method for monitoring brain health of a subject. The method includes receiving a functional near infrared spectroscopy (fNIRS) signal and an electroencephalographic (EEG) signal from at least one fNIRS sensor and at least one in-ear EEG sensor respectively. The method further includes identifying one or more characteristics corresponding to each of the fNIRS signal and the EEG signal using a feature extraction technique, the one or more characteristics areindicative of a neural activity of brain of the subject. The method also includes computing a brain health index (BHI) value corresponding to each of the EEG signal and the fNIRS signal based on the identified one or more characteristics, the BHI value is indicative of a neurological condition of the subject. The method further includes calculating a mean BHI value based on correlating the BHI value corresponding to each of the EEG signal and the fNIRS signal. The method also includes determining a state of the brain health based on comparing the mean BHI value with a predefined threshold value.

[0010] To further clarify the advantages and features of the method and system, a more particular description of the method and system will be rendered by reference to specific embodiments thereof, which are illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the disclosure and are therefore not to be considered limiting of its scope. The disclosure will be described and explained with additional specificity and detail with the accompanying drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0011] These and other features, aspects, and advantages of the disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

[0012] Figure 1 illustrates a block diagram depicting an environment implementing a system for monitoring brain health of a subject, according to an embodiment of the present disclosure;

[0013] Figure 2 illustrates a schematic block diagram of the system and components of the system for monitoring the brain health of the subject, in accordance with an embodiment of the present disclosure, according to an embodiment of the present disclosure;

[0014] Figure 3 illustrates a schematic block diagram associated with a identifying module of the system, in accordance with an embodiment of the present disclosure;

[0015] Figure 4 illustrates an exemplary graphical representation of EEG signal, according to an embodiment of the present disclosure;

[0016] Figure 5 illustrates an exemplary graphical representation depicting PSD value of the EEG signal, according to an embodiment of the present disclosure;

[0017] Figure 6 illustrates an exemplary graphical representation depicting ERP value of the EEG signal, according to an embodiment of the present disclosure;

[0018] Figure 7 illustrates an exemplary graphical representation depicting coherence value of the EEG signal, according to an embodiment of the present disclosure;

[0019] Figure 8 illustrates an exemplary graphical representation of fNIRS signal, according to an embodiment of the present disclosure;

[0020] Figure 9 illustrates an exemplary graphical representation of pre-filtered fNIRS signal and post-filtered fNIRS signal depicting HbO signal and HbR signal, according to an embodiment of the present disclosure;

[0021] Figure 10 illustrates an exemplary graphical representation depicting the PSD value of the fNIRS signal, according to an embodiment of the present disclosure;

[0022] Figure 11 illustrates an exemplary graphical representation depicting the ERP value of the fNIRS signal, according to an embodiment of the present disclosure;

[0023] Figure 12 illustrates a schematic block diagram associated with a determining module of the system, in accordance with an embodiment of the present disclosure; and

[0024] Figure 13 illustrates a flow chart depicting a method for monitoring the brain health of the subject, in accordance with an embodiment of the present disclosure.

[0025] Further, skilled artisans will appreciate those elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the disclosure. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the disclosure so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.DETAILED DESCRIPTION

[0026] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the various embodiments and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as illustrated therein being contemplated as would normally occur to one skilled in the art to which the disclosure relates.

[0027] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are explanatory of the disclosure and are not intended to be restrictive thereof.

[0028] Reference throughout this specification to “an aspect”, “another aspect” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. Thus, appearances of the phrase “in an embodiment”, “in another embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0029] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by “comprises... a” does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.

[0030] Electroencephalography (EEG) is a technique for measuring the electrical changes / activity of the brain induced by extra and intra-cellular electric currents associated with neuronal activity passively using scalp electrodes. Functional nearinfrared Spectroscopy (fNIRS) is an optical brain monitoring technique that uses nearinfrared spectroscopy for the purpose of functional neuroimaging, for measuring brain activity by using near-infrared light to estimate cortical hemodynamic activity whichmay occur in response to neural activity. Accordingly, the present disclosure provides one or more techniques for multi-modal integration of EEG and fNIRS and thereby provides an approach to brain-computer interface (BCI). Multi-modal integration may be referred to as combining multiple neurophysiological signals. The Multi-modal integration may have more potential than single modality drawbacks and may yield reliable results by extracting complementary features.

[0031] The current approaches for the integration of these two modalities to integrate the two different features yield only modest improvement in BCI. Further, a mismatch of recording locations may also hinder further improvement.

[0032] Figure 1 illustrates a block diagram depicting an environment implementing a system for monitoring the brain health of a subject, according to an embodiment of the present disclosure. The environment depicts an apparatus 100 that includes a headgear 102, in-ear electroencephalographic (EEG) device 104, and an armband 106. In an example, the headgear 102 may be wearable on head of the subject, the at least one in- ear electroencephalographic (EEG) device 104 wearable on an ear of the subject, and the armband 106 wearable around an arm of the subject. Further, the headgear 102 may be adapted to mount at least one functional near infrared spectroscopy (fNIRS) sensor 102a. The fNIRS sensor 102a may be adapted to detect an fNIRS signal from brain of the subject. The at least one in-ear EEG device 104 (may alternatively be referred to as ‘the at least one EEG device’) may be adapted to detect an EEG signal based on an electrical activity generated in the brain. The armband 106 may include a microcontroller (not shown) adapted to receive the fNIRS signal and the EEG signal and transmit the signal to at least one processor.

[0033] In an exemplary embodiment, the fNIRS sensor 102a may include a compact printed circuit board (PCB) with dimensions of 11.9mm x 21.65mm x 22.8mm for integration with other components. The fNIRS sensor 102a may further include an 18mm diffuser guide which aids in evenly distributing near- infrared light across a target area on the brain thereby enhancing the accuracy of the detected signal. Operating at a high sampling rate of 100Hz, the fNIRS sensor 102a may capture high-resolution data to detect even small changes in brain activity. Further, the fNIRS sensor 102a may include a plurality of channels, specifically four channels in one location to capture detailed data of the location within the target area.

[0034] In one example, the fNIRS sensor 102a may be capable of measuring distances of 15 mm and 30 mm thereby providing data from different depths within brain tissue for a comprehensive understanding of the brain activity. The headgear 102 may support the simultaneous use of a plurality of fNIRS sensors 102a, with the ability to operate more than five sensors simultaneously which may be required for extensive spatial coverage. In one example, the plurality of fNIRS sensors may include a total number of 22 fNIRS sensor 102a, each designated as the fNIRS sensor 102a. The fNIRS sensors 102a may be equipped with dual light-emitting diodes (LEDs) at wavelengths 740nm and 860nm. The fNIRS sensor 102a may be adapted to transmit light from the LEDs. The light may reach the brain tissue to measure hemoglobin levels within the brain tissue.

[0035] Additionally, dimensions of headgear 102 may be 356.02mm x 307.27mm without the at least one fNIRS sensor 102a and 356.02mm x 363.52mm with the at least one fNIRS sensor 102a. The additional space within the headgear 102 may be required to accommodate the at least one fNIRS sensor 102a while maintaining overall stability and comfort for the subject.

[0036] In an embodiment, the at least one EEG device 104 may include a plurality of EEG electrodes, each including at least one EEG sensor 104c. The at least one EEG sensor 104c may be adapted to be positioned within an ear canal of the subject and configured to detect the EEG signal from the ear canal. The at least one EEG device 104 may also include an arch- shaped ear mould 104b coupled to the plurality of EEG electrode and adapted to conform with a contour of an outer ear of the subject. The at least one EEG device 104 may further include an ear hook 104a attached to the archshaped earmold 104b and adapted to rest on top of the outer ear.

[0037] In an embodiment, the at least one EEG device 104 may comprise the at least one EEG sensor 104c that may have the following dimensions, but not limited to, 50.72 mm X 27.28 mm. The at least one EEG device 104 may include an Analog-to-Digital Signal Processing Circuit Board (ADS PCB) having dimensions such as, but not limited to, 22.46mm X 18.63mm. The at least one EEG device 104 may further include a 10 mm speaker driver and 4-pin connectors of 20.03 mm X 9.4mm. In one example, the at least one EEG device 104 may include the plurality of electrodes made of dry silver chloride. Specifically, the at least one EEG device 104 may include four electrodes.Each of the electrode among the plurality of electrodes may include the at least one EEG sensor adapted to be positioned within an ear canal of the subject. The at least one EEG sensor may be configured to detect the EEG signal from the ear canal of the subject.

[0038] In an example, the plurality of electrodes may have a sampling rate of 1000 samples per second at 24 bit kHz. The sampling rate, also known as the sample rate, refers to number of samples taken from a continuous signal (such as the EEG signal or the fNIRS signal) per second. Additionally, the plurality of electrodes includes Photoplethysmography (PPG) capabilities with a sampling rate of 1 kHz (1,000 samples per second) to ensure that variations in blood volume pulse may be captured with great accuracy for monitoring cardiovascular health and other physiological parameters. In another embodiment, connectors used in the at least one EEG device 104 may have dimensions of 47.5 mm. The connectors may ensure robust and reliable connections between components of the at least one EEG device 104 thereby facilitating data transfer and reducing a likelihood of signal loss or degradation.

[0039] In an embodiment, the microcontroller (not shown) may use a 32-Bit High- Speed processor with BLE / Wi-Fi / LTE. The microcontroller may have the following dimensions such as, but not limited to, 24.43mm X 22.52 mm. The microcontroller may be adapted to transfer data collected from the EEG signal and fNIRS signal and transmit the data to Cloud 108. The Cloud 108 may have at least one processor adapted to generate a report 110 and share the report to a user device of the subject.

[0040] In an embodiment, an electrical field in the brain and on the surface of the scalp of the subject may be related to electrical current from cortical sources through volume conduction. The volume conductor may be described by the anatomy of the head and may be modelled as four segments of different tissue types: the brain, cerebrospinal fluid (CSF), the skull, and the scalp. The geometric description of the head anatomy may be referred to as a head model.

[0041] As different types of brain tissues may be associated with different electrical conductivity, the volume conductor may be an inhomogeneous structure and anatomically complex structure. However, the volume conductor may be a linear model. A transfer function from sources in the brain volume to electrodes on the surface of the head may be referred to as a forward model. The apparatus 100 may use one or more numerical methods to compute the transfer functions from equivalent current sources inthe brain to electrical potentials on the surface of the head. The apparatus 100 may also develop the forward model to be used in an opposite direction i.e., to calculate the location of brain sources based on a measured surface topography.

[0042] In one example, for source localization, the apparatus 100 may at first perform an independent component analysis (ICA) on multichannel EEG signal, and subsequently, may find the dipole position and orientation that best explains the topography of the components from the ICA. The above-discussed method performed by the apparatus 100 may be referred to as dipole fitting.

[0043] In an embodiment, the at least one processor implemented in cloud / remote server 108 may receive the fNIRS signal and the EEG signal from the at least one fNIRS sensorl02a and the at least one in-ear EEG device 104 respectively of the apparatus 100. The at least one processor may be adapted to determine a state of the brain health based on a brain health index (BHI) value corresponding to each of the fNIRS signal and the EEG signal. Further, the at least one processor may be adapted to generate the report 110 based on the state of the brain health and share the report with the subject. A detailed working of the at least one processor is explained in the forthcoming paragraphs.

[0044] Figure 2 illustrates a schematic block diagram of the system 200 and components of the system 200 for monitoring the brain health of the subject, in accordance with an embodiment of the present disclosure. In an embodiment, the system 200 may reside in the cloud / remote server 108.

[0045] In an embodiment, the system 200 may include, but is not limited to, the at least one processor 202 or alternatively referred to as the processor 202, a memory 204, one or more modules 206 or alternatively referred to as the modules 206. The modules 206 and the memory 204 may be coupled to the processor 202.

[0046] The processor 202 may be a single processing unit or several units, all of which could include multiple computing units. The processor 202 may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc. In one embodiment, the processor 202 may include a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), or both. The processor 202 may be one or more general processors, Digital Signal Processors (DSPs), Application-SpecificIntegrated Circuits (ASIC), Field-Programmable Gate Arrays (FPGAs), servers, networks, digital circuits, analog circuits, combinations thereof, or other now known or later developed devices for analyzing and processing data. The processor 202 may execute a software program, such as code generated manually (i.e., programmed) to perform the desired operation. In one embodiment, the processor / controller 202 may be disposed in communication with one or more Input / Output (I / O) devices via I / O interface 204. The I / O interface 204 may employ communication Code-Division Multiple Access (CDMA), High-Speed Packet Access (HSPA+), Global System for Mobile communications (GSM), Long-Term Evolution (LTE), Worldwide Interoperability for Microwave Access (WiMAX), or the like, etc.

[0047] The memory 204 may include any non-transitory computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and / or non-volatile memory, such as read-only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory 204 may alternatively be referred to as the database 204 in the present disclosure, within the scope of the invention.

[0048] In an embodiment, the processor 202 may be disposed in communication with the memory 204. The processor 202 may be configured to receive the fNIRS signal and the EEG signal from the at least one fNIRS sensor 102a and the at least one in-ear EEG device 104 respectively. The processor 202 may further be configured to identify one or more characteristics corresponding to each of the fNIRS signal and the EEG signal using a feature extraction technique, the one or more characteristics are indicative of a neural activity of the brain. The processor 202 may also be configured to compute the brain health index (BHI) value corresponding to each of the EEG signal and the fNIRS signal based on the identified one or more characteristics, the BHI value is indicative of a neurological condition of the subject. The processor 202 may also be configured to calculate a mean BHI value based on correlating the BHI value corresponding to each of the EEG signal and the fNIRS signal. The processor 202 may further be configured to determine a state of the brain health based on comparing the mean BHI value with a predefined threshold value. The functionalities of the processor 202 may further be executed by the modules 206. The detailed explanation of each of the operation perform by the processor 202 may be further explained in reference to subsequent figures 3-11.

[0049] The modules 206, amongst other things, include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement data types. The modules 206 may also be implemented as, signal processor(s), state machine(s), logic circuitries, and / or any other device or component that manipulates signals based on operational instructions.

[0050] Further, the modules 206 may be implemented in hardware, instructions executed by a processing unit, or by a combination thereof. The processor 202 may comprise a computer, a processor, a state machine, a logic array, or any other suitable devices capable of processing instructions. The processing unit may be a general- purpose processor (e.g., processor 202) which executes instructions to cause the general-purpose processor to perform the required tasks, or the processing unit can be dedicated to performing the required functions. In another embodiment of the present disclosure, the modules 206 may be machine-readable instructions (software) which, when executed by the processor 202 / processing unit, perform any of the described functionalities / methods, as discussed throughout the present disclosure. The modules 206 in communication with the processor 202 may execute one or more functions as discussed above.

[0051] In an embodiment, the modules 206 may include a receiving module 210, identifying module 212, and determining module 214. The receiving module 210 may be adapted to receive the fNIRS signal and the EEG signal from the at least one fNIRS sensor and the at least one in-ear EEG device respectively. The identifying module 212 may be adapted to identify the one or more characteristics corresponding to each of the fNIRS signal and the EEG signal using the feature extraction technique, the one or more characteristics are indicative of the neural activity of the brain. The determining module 214 may be adapted to compute the brain health index (BHI) value corresponding to each of the EEG signal and the fNIRS signal based on the identified one or more characteristics, the BHI value is indicative of a neurological condition of the subject. The determining module 214 may further be adapted to calculate the mean BHI value based on correlating the BHI value corresponding to each of the EEG signal and the fNIRS signal. The determining module 214 may be configured to determine the state of the brain health based on comparing the mean BHI value with the predefined threshold value. Further, Figures 3-11 provide a detailed description of each of the modules 206 and related sub-modules.

[0052] Figure 3 illustrates a schematic block diagram associated with the identifying module 212 of the system 200, in accordance with an embodiment of the present disclosure.

[0053] In an embodiment, the identifying module 212 may be adapted to identify one or more characteristics corresponding to each of the EEG signal 302 and the fNIRS signal 304, respectively. At block 212a, the identifying module 212 may perform feature extraction technique to derive data from the raw EEG signal 302 and the fNIRS signal 304. The one or more characteristics identified by the identifying module 212 may be indicative of the neural activity of the brain.

[0054] In an embodiment, the identifying module 212 may use feature extraction technique to analyze the fNIRS signal 304 and EEG signal 302, and identify the one or more characteristics including, but not limited to, coherence values, power spectral density (PSD) values, and event-related potential (ERP) values from each of the EEG signal 302 and the fNIRS signal 304.

[0055] At step 306, the identifying module 212 may be adapted to derive the PSD value of the EEG signal 302. The PSD value may be a measure of power distribution of the EEG signal 302 across the frequency domain to evaluate abnormalities of the brain. In an example, the feature extraction technique may extract the PSD value of the EEG signal 302 using, but not limited to, Fast Fourier Transform (FFT). Further, the EEG signal 302 may be divided into short, overlapping time segments called epochs which facilitate detailed analysis. To minimize spectral leakage, a window function, such as a Hamming or Hanning window, is applied to each segment. Further, FFT may be performed on each windowed segment to convert the time-domain signal into the frequency domain. The magnitude of the FFT result may then be squared to obtain the power spectral density for each frequency component. Finally, the PSD values may be averaged across all segments to produce a stable estimate of the PSD for each frequency band thereby depicting a representation of the power distribution of the EEG signal 302.

[0056] In an embodiment, at step 308, an Event-Related Potential (ERP) values from EEG signals 302 may be extracted. The ERP values may include marking of events across time points corresponding to specific stimuli or events while receiving the EEG signal 302. Further, epochs, or time windows, may be extracted around each of the event marker, spanning from a few hundred milliseconds before the event to a few hundredmilliseconds after the event. To remove baseline drifts and enhance accuracy, baseline correction may be performed by subtracting a mean signal value during a pre-stimulus baseline period from each epoch. The corrected epochs may then be averaged across all occurrences of the same event to improve signal-to-noise ratio, resulting in a clear ERP waveform. Finally, the ERP values may be identified and measured in terms of amplitude and latency within an averaged waveform.

[0057] In an embodiment, at step 310, the coherence value of the EEG signal 302 may be computed by receiving the EEG signal 302 from the plurality of electrodes. The EEG signal 302 may be segmented into the epoch or the time windows. Each segment may then be transformed from the time domain to the frequency domain using the Fast Fourier Transform (FFT). The Fourier-transformed signals of two channels may then be multiplied to obtain a cross-spectral density. Concurrently, the power spectral density (PSD) for each of individual EEG signal may be calculated in the same time windows using FFT. Finally, to determine the coherence value, the cross-spectral density may be normalized by the power spectral densities of the two individual EEG signals.

[0058] As mentioned above, the coherence values may measure a degree of synchronization between different brain regions, indicating functional connectivity and communication within the brain. Further, the PSD values may represent the distribution of power across different frequency bands in the EEG signal 302. The PSD values may be used to identify dominant brain rhythms and an association with different cognitive states. Furthermore, the ERP values derived from EEG signals 302 may be in response to specific stimuli or events reflecting cognitive processes such as attention and memory.

[0059] In an embodiment, the identifying module 212 may be configured to extract the PSD value, ERP value, and the coherence value from the fNIRS signal 304 using the feature extraction technique as mentioned above for the EEG signal 302. In one embodiment, at step 312, the identifying module 212 may be configured to identify HbO (oxygenated haemoglobin) and HbR (deoxygenated haemoglobin) components of the fNIRS signal 304. The identified HbO and HbR may then be segmented into shorter time windows, and a window function may be applied to each segment to minimize spectral leakage. Then, the Fourier Transform (FFT) may be performed on the windowed segments to convert the time-domain signals into the frequency domain. Thepower of each frequency component may then be calculated by squaring the magnitude of the FFT result. Finally, the PSD values may be averaged over all the segments to obtain a stable and accurate estimate of the PSD value for each frequency band within the fNIRS signal 304.

[0060] In an embodiment, at step 314, extracting event-related responses (ERP) values from the fNIRS signal 304 may include marking of events in the fNIRS signal at time points corresponding to specific stimuli or events. The epochs, or time windows, may then be extracted around each event marker for both and HbR signals. The epochs may then be adjusted by subtracting a mean value during a pre-stimulus baseline period, thereby correcting for baseline drifts. The adjusted epochs may then be averaged across all occurrences of the same event to improve the signal-to-noise ratio thereby resulting in event-related hemodynamic responses. Finally, changes in HbO and HbR concentrations in response to the events may be identified and measured associated with specific stimuli or cognitive tasks.

[0061] In an embodiment, at step 316, to extract the coherence values from the fNIRS signal 304, the fNIRS signal 304 may first be pre-processed. The fNIRS signal 304 may be filtered to remove noise and physiological artifacts such as heartbeat and respiration. Further, the cross -correlation between the HbO signal and the HbR signal from different channels may help in assessing the synchronization between the HbO signal and the HbR signal. The time-domain fNIRS signal may then be converted into the frequency domain using the FFT. Finally, coherence values may be computed by normalizing the cross-spectral density with the power spectral densities of the HbO signal and the HbR signal. The figures 4-11 illustrates an exemplary graphical representation of the EEG signal 302, the fNIRS signals 304, and the one or more characteristics related to the EEG signal 302 and the fNIRS signals 304.

[0062] Figure 4 illustrates an exemplary graphical representation 400 of the EEG signal 302, according to an embodiment of the present disclosure.

[0063] The graphical representation 400 illustrates four plots (4A-4D), each of the plot (4a-4D) representing the raw EEG signal data collected by the plurality of EEG electrodes. The horizontal axis of each plot represents sample numbers which correlate with time, while the vertical axis indicates the amplitude of the EEG signal 302.

[0064] As depicted in plot 4A-4D, the amplitude of the EEG signal 302 may indicate various levels of brain activity of the subject. The EEG signal 302 may have noise which may be due to artifacts or external interferences. The amplitude ranges approximately between 250 to 350 units with distinct peaks and troughs observed throughout recorded samples. As seen, the initial segments of the plots show significant noise, which might be due to movement artifacts, external electrical interference, or muscle activity. Proper preprocessing steps, such as filtering and artifact removal, may enhance the signal quality for further analysis. Further, the plots (4A-4D) may display periodic fluctuations and sharp peaks depicting neural events or responses. The periodic fluctuation within the plots (4A-4D) may further be examined to identify event-related potentials (ERPs) or other neural correlates. Further, for detailed analysis, the EEG signal 302 may be segmented into epoch, and the window functions may be applied to minimize spectral leakage.

[0065] Figure 5 illustrates an exemplary graphical representation 500 depicting the PSD value of the EEG signal 302, according to an embodiment of the present disclosure.

[0066] The graphical representation 500 depicts the PSD value of the EEG signal 302 under two different conditions: without music (5 A) and with music (5B). The horizontal axis represents the frequency in Hertz (Hz), while the vertical axis represents the PSD value. The graphical representation 500 showcases an impact of auditory stimuli (music) on the electrical activity of the brain as depicted in the EEG signal 302. The changes in the PSD value may indicate that listening to music influences the frequency domain characteristics of the brain. The presence and alteration of specific frequency components suggest that music may modulate brain activity thereby potentially affecting cognitive and emotional processes.

[0067] Figure 6 illustrates an exemplary graphical representation 600 depicting the ERP value of the EEG signal 302, according to an embodiment of the present disclosure.

[0068] As depicted in the graphical representation 600, the horizontal axis indicates time in seconds and the vertical axis shows events corresponding to various activities or conditions experienced by the subject during the recording of the EEG signal 302. Each dot represents the event, such as, but not limited to, eye movements, tapping, arithmetic tasks, jaw movements, and blinking. The graphical representation 600 showcases temporal dynamics of neural responses to different stimuli and activities. Byanalyzing the ERP value associated with each event marker, an influence of different events on the brain may be recorded.

[0069] Figure 7 illustrates an exemplary graphical representation 700 depicting the coherence value of the EEG signal 302, according to an embodiment of the present disclosure.

[0070] The graphical representation 700 depicts the coherence values of the EEG signal 302 across different channel pairs, according to an embodiment of the present disclosure. The horizontal axis lists the various channel pairs, such as (Channel 1, Channel 2), (Channel 1, Channel 3), and so on, and the vertical axis represents the coherence values, which range from 0 to 0.8. Each bar in the graphical representation 700corresponds to the coherence value for different channels.

[0071] Further, the graphical representation 700 depicts the degree of synchronization between the EEG signal 302 recorded from different electrode pairs. Higher coherence values indicate stronger functional connectivity between the respective brain regions represented by the electrode pairs. For instance, the coherence values between Channels 1 and 2, Channels 1 and 3, and so on, are shown, with Channel 1 consistently exhibiting higher coherence values compared to other channels. The graphical representation 700 may depict the detailed representation for an in-depth analysis of neural connectivity patterns to understand different regions of the brain interactions and synchronization of activities during various cognitive and behavioural states.

[0072] Figure 8 illustrates an exemplary graphical representation 800 of the fNIRS signal 304, according to an embodiment of the present disclosure.

[0073] The graphical representation 800 depicts multiple continuous waveforms, each representing the fNIRS signal 304 from different sensor locations. The horizontal axis represents time, and the vertical axis indicates the signal amplitude for each channel. Each waveform corresponds to the measurement of haemoglobin concentration changes (both oxygenated haemoglobin (HbO) and deoxygenated haemoglobin (HbR)) in the brain. The changes in the waveform may reflect the hemodynamic responses associated with neural activity. The changes may further indicate variations in blood oxygenation and blood volume in the brain regions being monitored. As depicted in the Figure 8, the uniformity and consistency of the waveforms across different channels suggest a stable recording environment with minimal noise and artifacts.

[0074] Figure 9 illustrates an exemplary graphical representation 900 of pre-filtered fNIRS signal and post-filtered fNIRS signal depicting the HbO signal and the HbR signal, according to an embodiment of the present disclosure.

[0075] The figure 9 may be divided into four plots: the top two plots represent the HbO signals, and the bottom two plots represent the HbR signals. Each pair of plots shows the fRINS signal 304 before and after the application of filtering techniques.

[0076] As depicted in Figure 9A, the plots depict the raw, pre-filtered HbO signal and HbR signal. The HbO signal and the HbR signals exhibit significant fluctuations and noise which may be attributed to various sources such as physiological artifacts (e.g., heartbeat, respiration) and external interferences. The presence of these artifacts may obscure the correct hemodynamic responses related to neural activity.

[0077] As depicted in Figure 9B, the plots depict the post-filtered HbO and HbR signals. After applying filtering techniques, the HbO and HbR signals may significantly be smoother with reduced noise and artifacts. The enhanced clarity allows for a more accurate interpretation of the hemodynamic responses. The distinct features in the HbO and HbR signals, such as the peaks and troughs, become more pronounced, reflecting genuine changes in blood oxygenation and volume due to neural activity.

[0078] Therefore, by comparing the pre-filtered and post-filtered signals, it is evident that filtering of fNIRS signal 304 may significantly improve the signal-to-noise ratio thereby enabling more precise analysis of the HbO and HbR signals.

[0079] Figure 10 illustrates an exemplary graphical representation 1000 depicting the PSD value of the fNIRS signal 304, according to an embodiment of the present disclosure.

[0080] As depicted in the graphical representation 1000, the horizontal axis represents the frequency in Hertz (Hz), ranging from 0.0 to 3.5 Hz, and the vertical axis shows the PSD values in decibels (dB / Hz), indicating the power of the signal at each frequency. The PSD plot displays the power of the fNIRS signal 304 distributed across different frequencies. The graphical representation 1000 shows several peaks and troughs thus indicating variations in the power of the fNIRS signal 304 at various frequency bands. Notably, there is a prominent peak around 1.0 to 1.5 Hz thereby suggesting significant power within this frequency range which may be associated with physiological rhythmssuch as heartbeat or respiratory cycles. The higher power at lower frequencies and the gradual decline as frequency increases are typical characteristics of physiological signals where most of the energy may be concentrated in the lower frequency bands.

[0081] Figure 11 illustrates an exemplary graphical representation 1100 depicting the ERP value of the fNIRS signal 304, according to an embodiment of the present disclosure.

[0082] The graphical representation 1100 shows multiple continuous waveforms, each representing the fNIRS signal 304 from different sensor locations. The horizontal axis represents time in seconds (s), ranging from 0 to 500 seconds, and the vertical axis indicates the signal amplitude for various sensor pairs and wavelengths (e.g., 760 nm and 850 nm). The multiple waveforms displayed in the figure 11 correspond to the hemodynamic responses (both HbO and HbR) at different sensor locations and wavelengths. Each row represents the signal from a specific sensor pair (e.g., S1_D1 760, S2_D4 850), and the columns are marked with different conditions or activities over time, such as "Control", "Tapping", "Pigging", "Right", and so on. The ERP value may be indicative of a response by the brain to various tasks and stimuli, captured through the changes in haemoglobin concentrations.

[0083] Further, the waveforms showcase the hemodynamic response varies over time in response to different experimental conditions. As depicted in the graphical representation 1100, some intervals show more pronounced fluctuations, likely corresponding to periods of active tasks (e.g., tapping or right movements), while other intervals remain relatively stable, indicating control or resting states. The detailed graphical representation 1100 may allow for identification of task-specific neural patterns which may be further analyzed to understand the potential abnormalities associated with the brain.

[0084] Figure 12 illustrates a schematic block diagram associated with the determining module 214 of the system 200, in accordance with an embodiment of the present disclosure.

[0085] In an embodiment, the determining module 214 may receive the one or more characteristics corresponding to each of the EEG signal 302 and the fNIRS signal 304 from the identifying module 212. The determining module 214 may include a brain health index (BHI) sub-module 214a. The BHI sub-module 214a may be configured toclassify the one or more characteristics associated with the EEG signal 302 and the fNIRS signal 304. In one example, the BHI sub-module 214a may classify the PSD value, the ERP value, the coherence value associated with the EEG signal 302 and the PSD value, the ERP value, the coherence value associated with the fNIRS signal 304. Further, the BHI sub-module 214a may be configured to determine a BHI value corresponding to each of the EEG signal 302 and the fNIRS signal 304 based on the respective one or more characteristics. The BHI value may provide a quantifiable measure of brain health thereby reflecting a functional and physiological state of the brain of the subject. In an example, the BHI value of the EEG signal 302 may be the sum of the PSD value, the ERP value, and the coherence value. Similarly, the BHI value of the fNIRS signal 304 may be the sum of the PSD value, the ERP value, and the coherence value derived from the fNIRS signal 304.

[0086] In an embodiment, the BHI sub-module 214a may be adapted to calculate a mean BHI value based on correlating the BHI value corresponding to each of the EEG signal 302 and the fNIRS signal 304. The mean BHI value may integrate different aspects of brain function measured by each modality. Further, the BHI sub-module 214a may be configured to determine a state of the brain health based on comparing the mean BHI value with a predefined threshold value. The predefined threshold value may be based on experimental data obtained from normal control subjects. If the mean BHI value exceeds the predefined threshold, it may indicate a healthy neurological state, while values below the threshold could signal potential issues requiring further investigation. Further, the BHI sub-module 214a may be adapted to generate the report 110 based on the determined state of brain health.

[0087] Figure 13 illustrates a flow chart depicting the method 1300 for monitoring the brain health of the subject, in accordance with an embodiment of the present disclosure. The method 1300 may be a computer- implemented method executed, for example, by the system 200 and related modules 206. For the sake of brevity, constructional and operational features of the system 200 that are already explained in the description of Figures 1-12 are not explained in detail in the description of Figure 13.

[0088] At step 1302, the method 1300 may include receiving the fNIRS signal 304 and the EEG signal 302 from the at least one fNIRS sensor 102a and the at least one in- ear EEG device 104 respectively.

[0089] At step 1304, the method 1300 may include identifying the one or more characteristics corresponding to each of the fNIRS signal 304 and the EEG signal 302 using the feature extraction technique, the one or more characteristics is indicative of the neural activity of the brain of the subject.

[0090] At step 1306, the method 1300 may include computing the BHI value corresponding to each of the EEG signal 302 and the fNIRS signal 304 based on the identified one or more characteristics, the BHI value is indicative of a neurological condition of the subject.

[0091] At step 1308, the method 1300 may include calculating the mean BHI value based on correlating the BHI value corresponding to each of the EEG signal 302 and the fNIRS signal 304.

[0092] At step 1310, the method 1300 may include determining the state of the brain health based on comparing the mean BHI value with the predefined threshold value. The predefined threshold value is based on experimental data obtained from normal control subjects.

[0093] The method 1300, prior to receiving the fNIRS signal 304 and the EEG signal 302, may include pre-processing the fNIRS signal 304 and the EEG signal 302 to remove noise and artifacts from each of the fNIRS signal 304 and the EEG signal 302 by the microcontroller. The method 1300 further includes generating the report based on the determined state of brain health.

[0094] The disclosed method 1300 and system 200 described herein offer a simple and friendly interface for the subjects in need of Post-operative Neurocare. Hence, cognitive abilities of the subject may be constantly monitored. The present disclosure benefits the subject by alternative monitoring and treatment methodologies, thereby reducing healthcare costs and expanding coverage of trauma health services to a larger population. Further, the present disclosure provides accurate measurements by capturing multiple measurements in a single procedure, making the system 200 faster, safer, and more cost-effective. The present disclosure aids in managing brain injuriesthat may provide access to specialized care, incorporates standardized protocols, offers long-term follow-up care to maximize patient recovery and improve persistent gap in providing quality care and resources to deal with the subjects having brain impairments, especially related to neurodegenerative brain disorders.

[0095] While specific language has been used to describe the subject matter, any limitations arising on account thereto, are not intended. As would be apparent to a person in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein. The drawings and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment.

Claims

Claims:

1. A system (200) for monitoring brain health of a subject, the system (200) comprising: at least one functional near infrared spectroscopy (fNIRS) sensor (102a) configured to detect an fNIRS signal (304) from brain of the subject; at least one in-ear electroencephalographic (EEG) device (104) configured to detect an EEG signal (302) based on an electrical activity generated in the brain; and at least one processor (202) in communication with the at least one fNIRS sensor (102a) and the at least one in-ear EEG device (104), wherein the at least one processor (202) is configured to: receive the fNIRS signal (304) and the EEG signal (302) from the at least one fNIRS sensor (102a) and the at least one in-ear EEG device (104) respectively; identify one or more characteristics corresponding to each of the fNIRS signal (304) and the EEG signal (302) using a feature extraction technique, the one or more characteristics are indicative of a neural activity of the brain; compute a brain health index (BHI) value corresponding to each of the EEG signal (302) and the fNIRS signal (304) based on the identified one or more characteristics, the BHI value is indicative of a neurological condition of the subject; calculate a mean BHI value based on correlating the BHI value corresponding to each of the EEG signal (302) and the fNIRS signal (304); and determine a state of the brain health based on comparing the mean BHI value with a predefined threshold value.

2. The system (200) as claimed in claim 1, comprising a headgear (102) wearable on the head of the subject and adapted to mount the at least one fNIRS sensor (102a).

3. The system (200) as claimed in claim 1, wherein the at least one in-ear EEG device (104) comprising: a plurality of EEG electrodes, each including at least one EEG sensor (104c), adapted to be positioned within an ear canal of the subject and configured to detect the EEG signal (302) from the ear canal; an arch-shaped ear mould (104b) coupled to the plurality of EEG electrode, and adapted to conform with a contour of an outer ear of the subject; andan ear hook (104a) attached to the arch-shaped earmold (104b) and adapted to rest on top of the outer ear.

4. The system (200) as claimed in claim 1, wherein the one or more characteristics includes coherence values, power spectral density (PSD) values, and event related potential (ERP) values.

5. The system (200) as claimed in claim 1, wherein the predefined threshold value is based on experimental data obtained from normal control subjects.

6. The system (200) as claimed in claim 1, wherein the at least one processor (202) is configured to generate a report based on the determined state of brain health.

7. A method (1300) for monitoring brain health of a subject, the method (1300) comprising: receiving a functional near infrared spectroscopy (fNIRS) signal (304) and an electroencephalographic (EEG) signal (302) from at least one fNIRS sensor (102a) and at least one in-ear EEG device (104) respectively; identifying one or more characteristics corresponding to each of the fNIRS signal (304) and the EEG signal (302) using a feature extraction technique, the one or more characteristics are indicative of a neural activity of brain of the subject; computing a brain health index (BHI) value corresponding to each of the EEG signal (302) and the fNIRS signal (304) based on the identified one or more characteristics, the BHI value is indicative of a neurological condition of the subject; calculating a mean BHI value based on correlating the BHI value corresponding to each of the EEG signal (302) and the fNIRS signal (304); and determining a state of the brain health based on comparing the mean BHI value with a predefined threshold value.

8. The method (1300) as claimed in claim 7, wherein prior to receiving the fNIRS signal (304) and the EEG signal (302), the method (1300) comprises: pre-processing the fNIRS signal (304) and the EEG signal (302) to remove noise and artifacts from each of the fNIRS signal (304) and the EEG signal (302) by a microcontroller.

9. The method (1300) as claimed in claim 7, comprises generating a report based on the determined state of brain health.

10. The method (1300) as claimed in claim 7, wherein the predefined threshold value is based on experimental data obtained from normal control subjects.

Images