The invention relates to a system, a processing unit, a computer-implemented method and a computer program product for determining a health status of a person’s nervous system, in particular, the person’s brain. It has been observed that some individuals seem to resist functional decline for longer, in the face of progressive neurodegenerative diseases, like Alzheimer's Disease (AD) and related dementias (Stern, Y. (2009). Cognitive reserve. Neuropsychologia, 47(10), 2015-2028), and that some of this resilience is associated with intelligence and life experience (especially IQ, education and general health - termed cognitive reserve). There exist factors that interact to maintain functional performance for longer, in the face of normal aging, advancing disease, or injury. Importantly, they are not measurable at the level of behavior, which is unimpaired. This can make timely identification of neuropathology very difficult for clinicians, who rely primarily on results from a battery of behavioral cognitive tests to make a determination as to patient disease status and prognosis. Mental workload measurement is one means to characterize the interplay of disease and the brain’s capacity to maintain function. In situations where functional performance of tasks is unimpaired despite brain damage, individuals may experience having to ‘work harder’ to maintain acceptable performance. Problems with the mental workload measurement are as follows: • Task interruption: The inability to do ‘live’ measurement during a challenging cognitive task. The subject must temporarily stop performing the task to briefly report their mental state or wait until the task is finished. This may introduce measurement errors as subjects are required to report their mental state retrospectively, from memory. This also changes the task itself as it requires ongoing monitoring of one’s mental state, which may itself contribute to mental workload;

• Self-report in general is highly variable and prone to systematic influence from a wide range of factors, including: social desirability bias, experimenter influence, etc.

Some physiological measures purport to measure mental workload objectively (e.g. galvanic skin response, blood pressure, heartrate variability, etc.) and do obviate many of these problems, but they actually measure the much broader constructs of arousal and autonomic function (Ranchet et al., 2017). While these measures are statistically associated with mental workload, they are also associated with a wide range of other bodily, environmental and psychological factors.

Electroencephalography, EEG is a non-invasive means to measure electrical potentials generated by the brain, at the scalp. While research has demonstrated the existence of EEG correlates of cognitive resilience at the cohort level, and separately, mental workload measurement has been used to demonstrate hidden neurocognitive deficits (not expressed in behavior) at the individual level, there has been no attempt made to amalgamate these approaches.

Assessment of brain health, cognitive capacity or the integrity of brain networks (e.g. in a CNS condition, or other condition that affects cognition) can be masked by compensation by alternative brain networks (e.g. the brain recruits a different brain area/network as a workaround to avoid using a damaged area/network that usually carries out that function), or by people simply working harder to maintain a particular level of performance. Additionally, users may have developed conscious or unconscious alternative strategies to maintain their external task performance despite underlying damage to brain networks (especially in those with higher baseline intelligence, better general health, and more education). As a result, such assessments may be insensitive to subtle change.

In other words, the dynamics of the neural networks which support task performance may not be accessible to subjective experience in full or even at all. To summarize, existing solutions lack the ability to measure the intactness of brain networks, independently of the cognitive resilience of an individual patient. Some methods are prone to bias and noise (self-reported mental workload) or measure extraneous factors (physiological measures).

It is an objective of the present invention to provide a system, a processing unit, a computer- implemented method and a computer program product which address one or more of the aforementioned problems. In particular, it is an objective of the present invention to identify the health status of a person’s nervous system, in particular, of the person’s brain.

The objective is solved by a system for determining a health status of a person’s nervous system, in particular of the person’s brain according to a first aspect, comprising an interface unit configured to output a task with at least two different levels of demand to a person to be investigated and to receive a user input for each level of demand from the person. The system further comprises a detecting unit configured to detect at least one biosignal of the person, wherein the biosignal is indicative of a reaction of a nervous system of the person to the task, in particular to each level of demand. Moreover, the system comprises a processing unit configured to extract at least one objective parameter of the at least one biosignal, compare the extracted objective parameter to a reference model and determine a health status indication based on the comparison of the extracted objective parameter and the reference model, wherein the health status indication indicates a health status of the nervous system.

The person to be investigated is provided with at least two levels of demand, wherein the levels are different, in particular in difficulty. The task may be a cognitive task paradigm presenting an interactive sensorimotor activity with graded levels of difficulty (demand). The paradigm may begin with version(s) of the task with a first, in particular a very low/trivial difficulty and progresses to at least a second, in particular more difficult and/or challenging version of the task. Each subsequent level may place progressively more demand on one or more specific cognitive functions, including any of the following: working memory, executive function and/or planning, learning and/or memorization. The primary target of the task may be the specific neural network(s) that underpin performance in the task. This/these network(s) may be engaged to a progressively greater degree by subsequent levels of the task.

The nervous system may comprise or be the person’s brain. The health status indication may indicate at least one of the mental health, physical health and cognitive health of the person, in particular of the person’s brain. The health status indication may indicate a healthy status and/or unhealthy status. In addition or as an alternative, the health status indication may indicate the presence or absence of a disease, in particular, based on the comparison with the reference model.

The task may comprise in at least part one or more stimulus presented to the person, in particular, to the nervous system and more particularly to the person’s brain. The stimulus may be at least one of an optical, acoustical, haptic, and/or other sensory stimulus.

The biosignal is detected and indicative of the reaction of the nervous system of the person, in particular the person’s brain. At the first level, e.g., at the trivial difficulty, the brain may solve the first level easily. At the second level, e.g., at the more difficult level, a healthy brain may solve the second level with the same area of the brain, whereas an unhealthy brain may solve the second level by using additionally or alternatively another brain area, since the previously used area may be damaged. At any level, an unhealthy brain may utilize the same areas/networks to solve the task as used by a healthy brain, but may show abnormally high activity levels in those areas, relative to a healthy brain.

The at least one parameter is extracted from the biosignal, which may be detected during the time of the person being challenged by the task. Moreover, the reference model is provided, wherein the reference model may be a model of a healthy brain which has been challenged with the task and the at least two levels. The at least one parameter, which may be an EEG band power measurement parameter, is extracted from the biosignal and compared to the reference model. Based on the comparison of these two, the health status indication is determined. For example, if the person to be investigated has a healthy brain, the comparison may yield that the parameters of the person and the reference model may be close to each other and/or may overlap, indicating that the person’s brain reacts similarly to a healthy brain and may thus be healthy. In contrast thereto, if the person’s brain is unhealthy, the parameter may deviate from the reference model, which may indicate that the brain is unhealthy.

Furthermore, it may be that the person to be investigated with an unhealthy brain may solve the two levels in the same time as the reference model and one might conclude from that the person has a healthy brain. Also, further measured parameters such as body temperature may not provide an indication of the unhealthy brain. However, by extracting the objective parameter and comparing said objective parameter to the reference model, it is possible to identify a health status of the brain. In addition, depending on the objective parameter and/or the analysis of the objective parameter and/or the comparison, particular damaged areas and/or diseases may be identified. The at least one biosignal, here for example a continuously measured EEG signal may be analyzed for signals associated with mental workload and neural network activation, to derive estimates of efficiency, capacity utilization and compensation. In the presence of normal behavioral performance, abnormal EEG may indicate that the neural networks supporting the subject’s performance are engaged to an abnormal degree. Moreover, due to the measurement of neural network activity underpinning task performance, during task performance, has the advantage of being ‘live’. The most valid and direct measurement of any neural phenomenon should be achieved by measuring it while it is in progress - not subjectively, and/or in retrospect. EEG, more so than other physiological measures associated with workload, may allow for direct measurement of neural activity of the nervous system, which is the primary substrate of cognition and the site at which the processes underpinning cognitive reserve operate. A focus on neural activation as the target of the task may allow to directly measure some of the capacities through which cognitive reserve is expressed.

There may be more than one reference model provided. In particular, the processing unit may be configured to compare the objective parameter to at least two, three or more reference models and may determine the health status indication based on the comparison of at least some, in particular all comparisons.

Efficiency may be defined as the degree to which a task-related brain network must become activated in order to accomplish a or the given task. Capacity may be defined as the degree to which a task-related brain network can maximally be activated to keep performing a or the task even in the face of increasing demands. Compensation may be defined as involving the utilization of alternative networks (of the nervous system, in particular the brain) not typically used by healthy individuals in order to maintain or improve cognitive performance.

In addition, ecological measurement of performance and neural activity (i.e. done at home, in one’s familiar environment) may be likely to capture a more representative snapshot of an individual than the same procedure done in a clinic or laboratory. Accordingly, the task may be provided to the person while at home. Also, the at least one objective parameter may be detected at home. In addition, day-to-day variation in condition and performance can easily be overcome by simply performing multiple recording sessions over several days. This approach allows for the creation of a user profile that is more likely to be reliable (i.e. consistent) and valid - as compared to traditional approaches. The at least one objective parameter is a parameter which can be detected by measurement. That may be, e.g., an EEG band power measurement of the brain. In contrast to the objective parameter, a subjective parameter may be, e.g., asking the person after having performed the test how she/he felt during the task. This subjective parameter is biased by the person’s feeling and subjective perception.

In addition or alternatively to the above mentioned task, the task, in particular the cognitive task may require the user to view and memorize the location of a set of abstract visuals, placed on the underside of a plurality of, such as six on-screen discs. Items may be revealed as discs flip over one-by-one. When all novel patterns have been revealed, each of the patterns seen so far may appear in the center of the screen. The person/user may have to respond by tapping on the disc they recall having that pattern on its back side. Feedback on response correctness may be given. If a response is incorrect, the correct disc may be indicated. When the recall stage is finished, another round of pattern reveals may commence, to which the user may have to pay attention and attempt to remember new patterns in addition to those previously seen. More new patterns may be introduced with each round of pattern reveals, and the number of new patterns introduced may increase on subsequent rounds/levels. In this way, the difficulty of the memorization demand may increase over time. The task may end if a predetermined number of patterns or when all patterns have been presented. A completely different set of patterns may be used on subsequent task sessions to prevent mental interference.

The task may be a variant of the well-known “Towers of London” or alternatively, “Towers of Hanoi” task, with three pegs and a variable number of plates which must be moved between pegs without placing a larger plate on top of a smaller plate. The user may be presented with an initial workspace state and a target workspace state, and given explicit time to plan their moves before initiating their responses (minimum planning time may be 5s). When the user is ready to proceed to the response phase, they may have to indicate as much by pressing a button. Plates are moved between pegs by dragging on the interface unit, which may be a tablet touch-screen. Each subsequent level may increase the degree of challenge by increasing the number of plates in play.

The at least one biosignal, in particular an EEG signal may be measured from the scalp during task completion, and user interactions with the tablet-based task may be recorded. After task completion, EEG and optional behavioral data may be uploaded from the tablet to a cloud storage server via a wireless connection. If an internet connectivity is unavailable, the data may be stored on the tablet in encrypted format. The behavioral data may be or comprise at least one of responses and timing of the responses to the task. The objective parameter extracted from the behavioral data may at least comprise an accuracy and a reaction time of the response which, in particular, may be indicative of cognitive performance.

The data of the biosignal and/or the objective parameter, in particular the objective parameter of the EEG signal may be processed offline, including the steps of extracting the objective parameter and/or correcting the data deemed to be contaminated by artifacts (i.e. due to user movement), or data from channels insufficiently well-connected to the user’s scalp. The data may be filtered to remove high and low frequency artifacts.

The data, in particular the biosignal may be further processed to extract the objective parameter, in particular of cortical network activation, including but not limited to: power spectra, inter/intra-cortical connectivity, event-related potentials (ERPs), coherence and/or network maps.

The data, in particular the objective parameter of cortical network activation may be referenced against each level of difficulty/demand presented during the task, in particular the cognitive task paradigm. Levels of difficulty associated with higher than baseline activation or higher than (age-referenced) normative activation may be identified and may form the basis of system output.

Units of the system mentioned before and afterwards may be connected to each other. The connection may be cable-bound and/or wireless.

The task has at least two levels of demand. The task may have three, four, five or more levels of demand.

The user input may comprise one user input or two, three or more user inputs from the person. For each level of demand, the user may input one, two or more user inputs, in particular depending on the task and/or the correctness of the user’s inputs.

The detecting unit is configured to detect at least one biosignal of the person. The detecting unit may be configured to detect two, three or more biosignals, wherein each biosignal is indicative of a reaction of a nervous system of the person. The detecting unit may be configured to continuously detect the at least one biosignal, in particular, during the task and/or until the task is completed, in particular successful by the person. The at least one biosignal and/or further biosignals may be detected with time stamps and/or a timeline. The at least one biosignal and/or further biosignals may be detected during the task, in particular during each level of demand.

The processing unit may be a computer, a processor, a handheld device, a mobile terminal and/or a cloud processor. The cloud processor may be a processor or processing power provided by a cloud, in particular of a network.

The processing unit is configured to extract at least one objective parameter of the at least one biosignal. Moreover, the processing unit may be configured to extract two, three or more objective parameters of the biosignal and/or a plurality of biosignals. In addition or alternatively, the processing unit may be configured to extract the same objective parameter or parameters of each detected biosignal and/or different objective parameter or parameters of each detected biosignal.

The at least one objective parameter may be a value, a metric, a vector, a waveform, image data and/or an array, matrix or tensor of values. The at least one objective parameter may characterize at least one of ERPs, event-related spectral dynamics, ERSPs and topographies of the nervous system, in particular of the user’s brain.

The objective parameter may be indicative of at least one of an activation, a degree of activation, workload, capacity, efficiency and/or atypical compensatory activity of the person’s brain.

The reference model may be indicative of a brain for the objective brain metric or objective parameter and at least the two levels of demand. Said reference model may be a reference model of a healthy brain or an unhealthy brain of a reference person, or brains of a group of healthy people who are representative of the target individual in other respects (e.g. similar genetic background, age, level of education, socio-economic level). Moreover, the reference model may be based on measurements of healthy and/or unhealthy brains and may represent an average of said measurements. The reference model may be indicative of a reference population, age and/or gender.

The interface unit may be further configured to output the task as an optical, acoustic, haptic and/or other sensory stimulus demand task, wherein the interface unit may be configured to receive the user input as any one of an optical, acoustic and/or haptic user input, a manual, speech and/or other motor-related input. The optical input may be an input of the detecting device detecting, e.g. via a camera the user’s input. The levels of demand may all be output the same, such as only optically. In contrast thereto, the levels of demand may be output differently. For example, the first level may be output optically, while the second level is output haptically. In addition or alternatively, the user input may be optically, acoustically and/or haptically. The user input may, e.g., be optically, while the task was output acoustically. In addition or as an alternative, task may comprise a combination of optically, acoustically and/or haptically output task. In addition or as an alternative, a first level of the task may be output, e.g., optically and a second level of the task may be output acoustically. Thus, the levels of the task may be output differently and/or the same.

The detecting unit may be configured to detect the at least one biosignal for at least a predetermined period of time starting with a first time point and ending with a second time point, wherein the first time point is a time point of outputting a first level of demand of the task to the person and the second time point is a time point of receiving the user input to the last level of demand of the task.

The interface unit may be configured to vary a level of demand adaptively based on the user input.

The detecting unit may be configured to detect an Electroencephalography, EEG signal of the person and the biosignal may comprise or be the EEG signal.

The objective parameter may be indicative of at least one of an activation and/or atypical compensatory activity of the person’s brain. The biosignal may be a continuous EEG signal from one or many sensors, or a short-duration (<1s) aggregate EEG signal taken from repeated occurrences of a salient event (i.e. an event-related potential or ERP). The objective parameter extracted therefrom may be Alpha power/peak frequency (dB or Hz respectively, from continuous EEG); inter-sensor frequency power correlations (r correlation statistic from continuous EEG); ERP peak amplitude (in uV); or ERP peak latency (in ms). The objective parameter may be indicative of an atypical compensatory activity of the person’s brain when the objective parameter is a high theta band power or an unusual pattern of correspondence/connective across a network of sensors detecting the at least one biosignal at a predetermined level of task difficulty.

The objective parameter may be indicative of at least one of an alpha, a theta, a spectral measurement, a time-locked signal, a topography and a spatial change of the person’s brain. The spectral measurement may be an alpha power. A resting brain produces high amplitude oscillations in a range between 7 to 14 Hz (the alpha band). The objective parameter may be a frequency value, in particular, a brain frequency value. The brain frequency value may be a frequency value measured from the brain of the user. Said brain frequency value may be at or below 40 Hz.

The processing unit may be further configured to extract the objective parameter by at least one of spectral/oscillatory coupling measurements, network connectivity measurements, event-related time and frequency domain analyses of the biosignal, wherein at least one event- related time is a time point of outputting the task, receiving the user input and/or predetermined time points, in particular during the task. Spectral coupling measurements may include cross spectral density measurements, spectral magnitude and/or phase coherence measurements, partial (direct or indirect) coherence measurements and variants thereof such as undirected and/or directed transfer functions and variants thereof. Network connectivity measurements may include graph metrics applied to spectral/oscillatory measurements obtained from multiple sensors, such as minimum spanning tree analysis, characteristic path length, or clustering coefficient.

The interface unit may be further configured to output the task with the at least two or more levels of demand, and/or wherein the interface unit may be further configured to output the task to the person until the person reaches a maximum performance level, which is a level of demand being completed successfully by the person. Accordingly, the task may continue as long as the person successfully completes the levels.

The processing unit may be configured to determine the brain health indication as normal or atypical based on the at least one objective parameter being within a predetermined health threshold of the reference model. In addition or alternatively, the processing unit may be configured to determine the brain health indication as normal or atypical by deriving a distance metric based on a distance between the at least one parameter and the reference model. In addition or as an alternative, the processing unit may be configured to determine the brain health indication by detecting a progressive decline and/or improvement with respect to the reference model. In addition or as an alternative, the objective parameter at specific time points may be compared to values at time points of the reference model corresponding thereto. In addition or as an alternative, the objective parameter at a plurality of time points may be compared to the values at a corresponding plurality of time points and thus over time. The processing unit may further be configured to determine a clinical, lifestyle, wellbeing and/or wellness decision based on the brain health indication. For example, if the comparison results in that the parameter overlaps with the reference model and the reference model is indicative of a healthy brain, the processing unit may determine that no clinical decision, such as visiting a hospital is necessary.

The reference model may be computed using data from reference data using epidemiological modelling methods such as weighted multiple regression models.

The system may further comprise a storage unit configured to store at least one of a name, an age, a gender and contact information of the person, a time and/or location of the task being output to the person, an amount of levels of demand, the levels of demand, an amount of successfully completed levels of demand, a time point of each user input, the at least one biosignal, the extracted at least one objective parameter, the reference model, the health status indication, the comparison of the at least one objective parameter with the reference model, a history of head injury of the user, a history of neurological conditions of the user, which may include epilepsy, an education history, an employment sector and/or job type, a current medication use and any ongoing medical condition. In addition or as an alternative, the storage unit may be configured to store any data regarding the user, in particular, data indicating any medical status and medication of the user. The storage unit may be a physical unit and/or may be a cloud and/or network storage.

The detecting unit comprises or is a headset wearable by the person, and/or wherein the interface unit comprises or is at least one of a display unit, in particular a touch sensitive display, a microphone, a speaker, a motion sensor such as an accelerometer, in particular for detecting motion, e.g., of the tablet and a vibration motor for haptic feedback. The headset may be a wet or dry EEG headset. In particular, the headset may be a wireless, dry-sensor (e.g. Ag-AgCl-coated polymer sensors) EEG headset with electrodes, providing coverage of relevant cortical regions. The interface unit may be a handheld tablet device. Moreover, the interface unit may be connected to the headset and configured to receive and/or collect the data from the headset. A synchronization between the headset detecting the at least one biosignal and the task, in particular each level may result in timing precision of +/- 2 ms between biosignal samples and events of the task.

The task may be initiated by the person by inputting a start command input to the interface unit. Units of the system may be connected to each other via cables or wireless, in particular via a network. In particular, the detecting unit may be given to a user such that the user can use the detecting unit at home. Other units of the system may be located in a hospital to enable clinical staff to examine the data received by outputting the task to the user.

The objective is solved by a processing unit for determining a health status of a person’s brain according to a second aspect, configured to extract at least one objective parameter of at least one biosignal of a person performing a task with at least two different levels of demand, wherein the biosignal is indicative of a reaction of a nervous system of the person to the task; compare the extracted objective parameter to a reference model; and determine a health status indication based on the comparison of the extracted objective parameter and the reference model.

The objective is solved by a computer-implemented method for determining a health status of a person’s brain according to a third aspect, comprising the steps of: extracting at least one objective parameter of at least one biosignal of a person performing a task with at least two different levels of demand, wherein the biosignal is indicative of a reaction of a nervous system of the person to the task; comparing the extracted objective parameter to a reference model; determining a health status indication based on the comparison of the extracted objective parameter and the reference model.

The method may further comprise at least one of the following steps: outputting the task to the person to be investigated; receiving a user input for each level of demand from the person; detecting the at least one biosignal of the person.

The objective is solved by a computer program product according to a fourth aspect comprising instructions which, when the program is executed by a processor, cause the processor to carry out the method according to the third aspect.

Features disclosed with reference to the system according to the first aspect may also be implemented as features of the processing unit according to the second aspect. Moreover, features disclosed with reference to the system according to the first aspect and the processing unit according to the second aspect may also be implemented as method steps of the method according to the third aspect.

Preferred embodiments are explained by way of example with reference to the enclosed figures. It shows: Fig. 1 a system for determining a health status of a person’s nervous system;

Fig. 2 a graph of objective parameters of persons and a reference model; and

Fig. 3 a computer-implemented method for determining a health status of a person’s nervous system.

In the figures, identical or essentially functionally identical or similar elements are designated with the same reference signs.

Fig. 1 shows a system 100 for determining a health status of a person’s nervous system, in particular of the person’s brain. The further description will describe the system 100 for determining the health status of one person. However, it will be appreciated by the skilled person that the system 100 may be configured to determine the health status of a plurality of nervous systems of persons, such as the nervous systems of two, three or more persons. The system 100 comprises an interface unit 110, a detecting unit 120, a processing unit 130 and an optional storage unit 140. The units of the system 100 may be connected by a wireless and/or cable bound connection. Moreover, the units of the system 100 may be configured to communicate via a network. The interface unit 110, the detecting unit 120, the processing unit 130 and/or the storage unit 140 may be comprised in a single unit or may be provided separately. Moreover, the storage unit 140 may be comprised by the interface unit 110, the detecting unit 120 and/or the processing unit 130. In addition or alternatively, the storage unit 140 may be provided as a cloud storage.

The interface unit 110 may be a handheld tablet configured to output a task with at least two different levels of demand to the person to be investigated and to receive a user input for each level of demand from the person. The task may be a cognitive task requiring the person (user) to view and memorize the location of a set of abstract visuals, placed on the underside of a plurality of, such as six on-screen discs. Items may be revealed as discs flip over one-by-one. When all novel patterns have been revealed, each of the patterns seen so far may appear in the center of the screen. The person/user may have to respond by tapping on the disc they recall having that pattern on its back side. Feedback on response correctness may be given. If a response is incorrect, the correct disc may be indicated. When the recall stage is finished, another round of pattern reveals may commence, to which the user may have to pay attention and attempt to remember new patterns in addition to those previously seen. More new patterns may be introduced with each round/level of pattern reveals, and the number of new patterns introduced may increase on subsequent rounds/levels. In this way, the difficulty of the memorization demand may increase over time. The task may end after the user makes four mistakes in their responses, or when all patterns have been presented. A completely different set of patterns may be used on subsequent task sessions to prevent mental interference.

The detecting unit 120 may be a headset wearable by the person and configured to detect at least one biosignal of the person, wherein the biosignal is indicative of a reaction of a nervous system, in particular of a brain of the person to the task. The detecting unit 120 may be configured to detect an EEG signal as the biosignal. The detecting unit 120 may detect more than one biosignal, such as two, three or more. In particular, the detecting unit 120 may continuously detect an alpha power of the person’s brain during the task. That is, the detecting unit 120 may continuously detect the EEG band power measurement of the person’s brain of each round/level of the task until the task is completed.

The processing unit 130 may be located, e.g., at a clinical department and/or may be implemented by a centralized server such as a big data centre/server farm and may receive data from the interface unit 110, the detecting unit 120 and the storage unit 140 via the wireless connection. Accordingly, the interface unit 110, the detecting unit 120 and the storage unit 140 may be located at the person’s home, while the processing unit 130 is located at the clinical department and/or implemented by the big data centre. The system 100 may further comprise a communication unit located at the clinical department configured to receive the data processed by the processing unit 130. In addition or as an alternative, the system 100 may comprise a computing device located at the clinical department to enable the staff of the clinical department to access and/or work with the data collected by the system 100. This enables the person to use the interface unit 110 and the detecting unit 120 at home, for example, at least twice a week. Moreover, the person does not have to make an appointment at the clinical department and convenience of the person is increased. At the same time, it is possible to more easily perform tasks multiple times a day or a week.

Furthermore, the processing unit 130 is configured to extract at least one objective parameter of the at least one biosignal, here the EEG signal. The at least one objective parameter may be an objective brain parameter. The at least one objective parameter may be indicative of at least one of an alpha, a theta, a time-locked signal, a spectral measurement, a topography and a spatial change of the person’s brain. The steps performed by the processing unit 130 may be performed after the person was challenged with the task and after the at least one biosignal was detected. For example, the received data may be uploaded to the storage unit 140 and then provided to the processing unit 130 and/or may be uploaded and/or provided directly to the processing unit 130, e.g., after the person has completed the task. In addition or as an alternative, the data may be provided to the storage unit 140 and/or the processing unit 130 when an internet connection is available for uploading the data.

Next, the processing unit 130 is configured to compare the extracted objective parameter to a reference model. The reference model may be a model suitable for examining the health status of the nervous system, in particular the brain. The reference model may be selected and/or adapted to the person, such as being a reference of the same sex, age group, job type, etc. In addition or as an alternative, the reference model may represent an average. The reference model can indicate a healthy nervous system, in particular a healthy brain or may indicate a damaged nervous system, in particular a damaged brain, e.g., a brain suffering from Alzheimer’s disease. Moreover, the reference model indicates values of such a nervous system, in particular such a brain and/or averaged nervous systems, in particular averaged brains for the task and the different levels of demand in order to enable a comparison between the person’s objective parameter and the “normal” reference model.

In addition, the processing unit 130 is configured to determine a health status indication based on the comparison of the extracted objective parameter and the reference model, wherein the health status indication indicates a health status of the nervous system. That is, based on the alignment and/or misalignment of the person’s objective parameter and the reference model, it may be analyzed whether the person’s nervous system, in particular the person’s brain completed the task similarly to the reference model or not. For example, the processing unit 130 may be configured to derive a distance metric for how similar the objective parameter is to a suitable norm (the reference model), and from that derive an output in the form of the health status indication. Said health status indication may that the health status is acceptable or atypical, according to a threshold relative to the reference model.

The storage unit 140 may be configured to store data from the units of the system 100 and/or to provide data to the units of the system 100. In particular, the storage unit 140 may be configured to store at least one of a name, an age, a gender and contact information of the person, a time and/or location of the task being output to the person, an amount of levels of demand, the levels of demand, an amount of successfully completed levels of demand, a time point of each user input, the at least one biosignal, the extracted at least one objective parameter, the reference model, the health status indication, the comparison of the at least one objective parameter with the reference model, a history of head injury of the user, a history of neurological conditions of the user, in particular including epilepsy, an educational history of the user, an employment sector/job type, a current medication use and any ongoing medical conditions of the user.

Fig. 2 shows a graph of objective parameters of two different persons and a reference model. The graph shown in Fig. 2 serves explanation purposes any may not be true to scale. Moreover, the individual graphs of the persons and the reference model are shown mostly linearly, while it is clear to the skilled person that other forms of graphs may be used or detected.

The graph in Fig. 2 shows on the x-axis the level of demand D from one to five. Thus, five levels of demand were output during the task to the persons to be investigated. The reference model may have data at least for the same levels of demand as have been output to the person to be investigated to enable a comparison. The y-axis shows an activation of the brain measured as an objective parameter and extracted from at least one respective biosignal.

The black line with the black dots represents the reference model, while the dashed line with white filled dots shows the objective parameter of a first person to be investigated and the dotted line with the pattern filled dots shows the objective parameter of a second person to be investigated. In this example, the reference model represents the objective parameter of a healthy brain. The dotted line may alternatively represent an unhealthy brain with Alzheimer’s disease. All three lines have measured objective parameter values for all five levels of demand.

As can be gathered from the graph of Fig. 2, the first person shows a similar brain activation on the first level of demand as the reference model. In contrast thereto, the second person shows already at the first level a higher brain activation level than the first person and the reference model. With an increase of the level of demand, the brain acitivation level of the first person increases more than the brain acitivation level of the reference model. This may indicate that the first person’s brain used a damaged area for the first level, which was sufficient to complete the first level. However, in order to complete the following levels of demand, the first person’s brain may use additional areas and thus makes use of compensation strategies to complete the following levels. While the first person may not be aware of this and may even complete the tasks in a similar time than the reference model, the brain activation level of the first person’s brain increases compared to the reference model since the process of completing the tasks is more complex. When compared to the second person with Alzheimer’s disease, it appears that the level of damage of the first person’s brain is not yet as severe as that of the second person, since the brain activation level of the second person is already at a high level at the first level. However, when challenged with the third to fifth level, the brain activation level of the second person is less than the brain activation level of the first person. Accordingly, while at the same time being able to compare the first person’s objective parameter to a healthy brain, it may also be compared to the brain activation level of a damaged brain.

Fig. 3 shows a computer-implemented method 200 for determining a health status of a person’s nervous system, in particular, of the person’s brain. The method 200 will be described in the following in connection with the system 100. However, it is not limited thereto. Moreover, steps 210 to 230 of the method 200 are described in the following in a specific order, however, the invention is not limited thereto and two or more steps may be performed at the same time or in a different order.

The method 200 comprises extracting 210, via the processing unit 130, at least one objective parameter of at least one biosignal of a person performing a task with at least two different levels of demand, wherein the biosignal is indicative of a reaction of a nervous system of the person to the task. Next, the method 200 comprises comparing 220, via the processing unit 130, the extracted objective parameter to a reference model. Then, the method 200 comprises determining 230, via the processing unit 130, a health status indication based on the comparison of the extracted objective parameter and the reference model, wherein the health status indication indicates a health status of the nervous system.

The method 200 may further comprise the steps of outputting, via the interface unit 110, the task to the person to be investigated, receiving, via the interface unit 110, a user input for each level of demand from the person, and/or detecting, via the detecting unit 120, the at least one biosignal of the person.

The storage unit 140 may be configured to store a computer program product comprising instructions which, when the program is executed by the processing unit 130, cause the processing unit 130 to carry out the method 200. The processing unit 130 may comprise or be a processor. REFERENCE SIGNS

100 System for determining a health status of a person’s nervous system

110 interface unit

120 detecting unit 130 processing unit

140 storage unit

200 method for determining a health status of a person’s nervous system

210 extracting at least one objective parameter

220 comparing the extracted objective parameter to a reference model 230 determining a health status indication