METHOD FOR OBTAINING NEAR-INFRARED SPECTROSCOPY CEREBRAL SIGNAL

Abstract

A method for obtaining a near-infrared spectroscopy (fNIRS) cerebral signal in a subject includes: placing a near-infrared emitter and respective proximal and distal near-infrared detectors on a skin of a head of a subject; during a baseline recording stage with the subject in resting-state, record near-infrared signals, the recorded signals including a baseline deep-signal and a baseline shallow-signal; calculate a scaling factor between amplitudes of the baseline deep-signal and the baseline shallow-signal at a given task-frequency; with the subject undergoing a cyclic cerebral stimulation at the task-frequency during a stimulation recording stage, record near-infrared signals, the recorded signals comprising a shallow-signal and a deep-signal; and applying the scaling factor to the shallow-signal, calculating the cerebral signal at the task-frequency as a difference between the deep-signal and the scaled shallow-signal, at the task-frequency.

Claims

1. A method to obtain a near-infrared spectroscopy (fNIRS) cerebral signal in a subject, the method comprising: placing a near-infrared emitter, a first near-infrared detector, and a second near-infrared detector on a skin of a head of a subject, the first near-infrared detector being placed closer to the near-infrared emitter than the second near-infrared detector; during a baseline recording stage with the subject in resting-state, recording, by a computer, near-infrared signals received from the near-infrared emitter in the first and second near-infrared detectors, the recorded signals comprising: a baseline deep-signal received by the second near-infrared detector, and a baseline shallow-signal received by the first near-infrared detector; calculating, by the computer, a scaling factor between amplitudes of the baseline deep-signal and the baseline shallow-signal at a given frequency; with the subject undergoing a cyclic cerebral stimulation according to an activity that occurs at the given frequency during a stimulation recording stage, recording, by the computer, near-infrared signals received from the near-infrared emitter in the first and second near-infrared detectors, the recorded signals comprising: a shallow-signal received by the first near-infrared detector, and a deep-signal received by the second near-infrared detector; obtaining, by the computer, a cerebral signal at the given frequency during stimulation by: applying the scaling factor to the shallow-signal; and calculating the cerebral signal at the given frequency as a difference between the deep-signal and the scaled shallow-signal, at the given frequency.

2. The method according to claim 1, wherein the cyclic cerebral stimulation is according to a mental or cognitive activity.

3. The method according to claim 1, wherein the cyclic cerebral stimulation is according to a visual, auditive, olfactory, gustative, somatosensorial or motor activity.

4. The method according to claim 1, wherein the obtaining the cerebral signal comprises obtaining, by the computer, a phase and an amplitude of the cerebral signal.

5. The method according to claim 4, wherein the obtaining the phase and the amplitude of the cerebral signal during stimulation comprises: determining, by the computer, a deep-signal phasor corresponding to the deep-signal during stimulation at the given frequency, having: a phase difference between the deep-signal and the shallow-signal at the given frequency, and an amplitude of the deep-signal at the given frequency; determining, by the computer, a shallow-signal phasor corresponding to the shallow-signal during stimulation at the given frequency, having: a reference phase, and an amplitude of the shallow-signal at the given frequency, multiplied by the scaling factor; determining, by the computer, a cerebral-signal phasor by subtracting the shallow-signal phasor from the deep-signal phasor; and calculating, by the computer, the phase and amplitude of the estimated cerebral signal at the given frequency.

6. The method according to claim 5, wherein the phase difference between the deep-signal and the shallow-signal at the given frequency is obtained by calculating, by the computer, an empirical transfer function in a frequency domain between the deep-signal and the shallow-signal and calculating, by the computer, an argument of the transfer function at the given frequency.

7. The method according to claim 1, wherein the calculating the scaling factor comprises calculating, by the computer, an approximation of a complex frequency dependent basal transfer function between the baseline deep-signal and the baseline shallow-signal and determining, by the computer, the scaling factor as a gain of the basal transfer function at the given frequency.

8. The method according to claim 1, wherein the given frequency is a frequency between 0.015 Hz and 0.07 Hz.

9. The method according to claim 1, wherein the given frequency is a frequency between 0.025 Hz and 0.05 Hz.

10. The method according to claim 1, wherein the given frequency is a frequency of 0.033 Hz.

11. The method according to claim 1, wherein a plurality of groups, each including a near-infrared emitter, a first near-infrared detector, and a second near-infrared detector, are provided, and the method further comprises averaging, by the computer, the shallow-signal and the deep-signal obtained for each group.

12. The method according to claim 1, wherein the recording during the stimulation recording stage comprises alternating: semi-periods of stimulating in the subject; and semi-periods of baseline resting.

13. The method according to claim 12, wherein the semi-periods of stimulating and the semi-periods of baseline resting have the same duration.

14. A system for obtaining a near-infrared spectroscopy (fNIRS) cerebral signal in a subject, the system comprising: a device comprising a near-infrared emitter, a first near-infrared detector, and a second near-infrared detector, adapted for placing the near-infrared emitter and the first and second near-infrared detectors on a skin of a head of a subject, the first near-infrared detector being placed closer to the near-infrared emitter than the second near-infrared detector; and a computer configured to perform: during a baseline recording stage with the subject in resting-state, record near-infrared signals received from the near-infrared emitter in the first and second near-infrared detectors, the recorded signals comprising: a baseline deep-signal received by the second near-infrared detector, and a baseline shallow-signal received by the first near-infrared detector; calculate a scaling factor between amplitudes of the baseline deep-signal and the baseline shallow-signal with respect to a task that occurs at a given frequency; with the subject undergoing a cyclic cerebral stimulation according to an activity that occurs at the given frequency during a stimulation recording stage, record near-infrared signals received from the near-infrared emitter in the first and second near-infrared detectors, the recorded signals comprising: a shallow-signal received by the first near-infrared detector, and a deep-signal received by the second near-infrared detector; obtaining a cerebral signal at the given frequency during stimulation by: applying the scaling factor to the shallow-signal; and calculating the cerebral signal at the given frequency as a difference between the deep-signal and the scaled shallow-signal, at the given frequency.

15. The method according to claim 1, further comprising: obtaining a relation of the cerebral signal and an extracerebral response; and using the cerebral signal and the relation for diagnosis and assessment of a clinical entity involving a brain functional change.

16. The method according to claim 15, wherein the clinical entity involving the brain functional change comprises at least one of dementia; a neurodevelopment disorder; an affective disorder and an autonomic dysfunction.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0023] As a complement to the description provided herein and for the purpose of helping to make the characteristics of the disclosure more readily understandable, this specification is accompanied by a set of drawings, which by the way of illustration and not limitation, represent the following:

[0024] FIG. 1 presents an example of a cerebral stimulation for a given task frequency;

[0025] FIG. 2 presents an example of the placement of an emitter and respective proximal and distal detectors on the skin of the head of a subject;

[0026] FIG. 3 presents a schema of the shallow and deep channels between the emitter and detectors of FIG. 2;

[0027] FIG. 4 presents a recording of filtered signals of the shallow and deep channels at the given task-frequency during the baseline and stimulation recording phase of the cerebral stimulation of FIG. 1

[0028] FIG. 5a presents the gain of a transfer function between the filtered signals of the shallow and deep channels during the baseline recording stage of FIG. 4;

[0029] FIG. 5b presents the phase of a transfer function between the filtered signals of the shallow and deep channels during the stimulation recording stage of FIG. 4;

[0030] FIG. 6a presents the averaged periods of the shallow signal and deep signal of FIG. 4 during the stimulation recording stage;

[0031] FIG. 6b presents the scaled shallow signal and the deep signal of FIG. 6a;

[0032] FIG. 6c presents the obtained cerebral signal as the difference between the scaled shallow signal and the deep signal of FIG. 6b;

[0033] FIG. 7 presents obtaining the cerebral signal as the difference between the scaled shallow signal and the deep signal using phasors;

[0034] FIG. 8 presents another example of the placement of emitters and respective proximal and distal detectors on the skin of the head of a subject;

[0035] FIG. 9 presents a schema of the shallow and deep channels between the emitters and detectors of FIG. 8.

DETAILED DESCRIPTION

[0036] FIG. 1 presents an example of a cyclic cerebral stimulation at a given task-frequency ft that may be used for obtaining the near-infrared spectroscopy fNIRS cerebral signal SCS in a subject 1 using the method of the disclosure through a system 100 comprising a device 10 and a computer 20 adapted to perform the steps of the method.

[0037] The cerebral stimulation is a mental or cognitive activity, so the corresponding obtained cerebral signal SCS will be related to this mental or cognitive activity. FIG. 1 presents a cyclic cerebral stimulation at a given task frequency ft is based on a block protocol designed as a cyclical pattern of mental effort, alternating semi-periods Tt/2 of mental math, with semi-periods Tt/2 of pause, of the same duration, i.e. regular repetitions of activation-rest, The idea behind this is to induce periodic hemodynamic changes in the form of cycles of some kind of response to the subject 1 followed by a return to basal levels. In this way, such an oscillatory pattern may be analyzed by conventional spectral methods. This cyclical cerebral estimulation may also be programmed in a computer.

[0038] As illustrated in FIG. 1, the cyclic cerebral stimulation is organized into three consecutive uninterrupted recordings: (i) 300 seconds of baseline in resting condition, as baseline recording stage t1 (ii) 300 seconds of mental arithmetic task, as stimulation recording stage t2 and (iii) 300 seconds of recovery in a relaxed state. In this case, the stimulation recording stage t2 consist of 10 consecutive 30-second trials, with a 30 second period Tt. Each trial began with a semi-period Tt/2 of 15 seconds of mental calculation, followed by a semi-period Tt/2 of 15-second pause of relaxation. To perform the mental math participants were asked to iteratively subtract a small number (between 5 and 9) from a three-digit number (between 100 and 199), as fast as possible. Both numbers, chosen randomly in each trial, were presented on a 21.5″ display monitor, 80 cm. away from the participants' eyes. Afterwards, the pause started by presenting the question “Result?” for 5 seconds, which prompted the participants to inform verbally of the final result of their mental calculations (to allow scoring the performance and ensuring that the participants were paying attention), followed by a soft image during which the participants were instructed to relax. Two seconds before presenting the subtraction operands, a fixation cross was displayed in the middle of the computer screen to announce the beginning of the mental calculation. Further, during the stimulation recording stage t2 the importance of making a mental effort was constantly emphasized, and not the amount or accuracy of the operations performed.

[0039] In this case, the 30-second period of the mental arithmetic trials corresponds to a task-frequency ft of 0.033 Hz. This frequency was chosen so that it did not overlap with well know spontaneous fluctuations such as ABP (0.08-0.12 Hz), or very slow endothelial activity (0.01-0.02 Hz). Furthermore, the 15-second duration of mental effort accommodates that of a typical hemodynamic response, while the next 15-second pause allows a return to baseline levels, being an optimal inter-event interval to minimize overlaps between consecutive hemodynamic responses. However, other task-frequency ft are also envisaged, for example frequencies between 0.015 Hz and 0.07 Hz, and frequencies between 0.025 Hz and 0.05 Hz may also be used.

[0040] In this case the cyclic cerebral stimulation is a mental arithmetic task, it is, a mental or cognitive activity, so the cerebral signal SCS to be obtained will be a response of the subject 1 to such mental or cognitive activity. However, in other embodiments, it is envisaged that the cerebral stimulation may be a visual, or auditive, or olfactory, or gustative or somatosensorial or motor activity, or even other activities depending on the cerebral signal SCS to be obtained as a response of the subject 1.

[0041] As shown in FIG. 2, for obtaining a near-infrared spectroscopy (fNIRS) cerebral signal SCS in a subject 1 a near-infrared emitter 2 and two detectors 3, respectively a proximal detector 3a and a distal detector 3b, mounted on a device 10 of the system 100, are placed on the skin of the head of the subject 1, typically in the fronto-polar region. It will allow obtaining the near-infrared spectroscopy (fNIRS) cerebral signal SCS in the subject 1 during cyclic cerebral stimulation at the given task frequency ft,

[0042] The proximal detector 3a is placed closer to the emitter 2 than the distal detector 3b, for example, the proximal detector 3a is placed 14 mm from the emitter 2 and the distal detector 3b is placed 32 mm from the emitter 2. Naturally, other arrangements of various emitters 2 and detectors 3 in the device 10 are also possible and they may be used for obtaining the near-infrared spectroscopy cerebral signal SCS in the subject 1 in a similar way as explained hereinafter.

[0043] The wavelength of the emitter 2 and detectors 3 will be adapted to detect the differences caused by the cerebral stimulation. For example, a near-infrared wavelength of 740 and 850 nm would be suitable for detecting relative changes in oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentration, consequently the cerebral signal obtained would be proportional to the adsorption of the emitted and received wavelengths.

[0044] FIG. 3, shows a representation of the shalllow channel SS established between the emitter 2 and the proximal detector 3a and the deep channel DS established between the emitter 2 and the distal detector 3b. The emitter 2 may be a light emitting diode emitting the wavelength of interest, in this case, a wavelength of 850 nm, and the detectors 3 may be fotodiodes or fototransistors, also known as optodes, adapted to receive the wavelength of interest and generating a corresponding electrical signal, that will be transmitted, for example wirelessly, and processed by a computer 20 of the system 100.

[0045] FIGS. 4 to 7 are related to a method for obtaining a cerebral signal SCS by a computer 20 based on detected changes in oxyhemoglobin (HbO), but other cerebral signals based on detected changes in other components at other wavelengths, such as deoxyhemoglobin (HbR) or even total hemoglobin (HbT) or isobestic point at 804-805 nm where the adsorption caused by HbO and HbR is the same may be used. Therefore, other wavelengths may be emitted or received as needed, depending on the variation of the component to be detected for obtaining the corresponding cerebral signal.

[0046] FIG. 4 shows an example of a recording of signals from the shalllow channel SS and deep channel DS during the baseline recording stage t.sub.1 with the subject in resting-state and the stimulation recording stage t.sub.2, which are recorded by the computer 20 As previously explained, the stimulation recording stage t.sub.2 comprises alternating semi-periods T.sub.t/2 of stimulating in the subject 1 the mental task, and semi-periods T.sub.t/2 of baseline resting, where the subject 1 recovers of the mental task, the semi-periods T.sub.t/2 of stimulating and the semi-periods T.sub.t/2 of baseline resting have the same duration. Advantageously, combining semi-periods T.sub.t/2 of stimulating and the semi-periods T.sub.t/2 of baseline resting induces a cyclic cerebral stimulation at the given task frequency ft.

[0047] The signals presented in FIG. 4 have been adapted and filtered by the computer 20 around the task-frequency f.sub.t so components of the signals at the same task-frequency f.sub.t. are obtained. In this case, the raw optical data of each detector 3 was converted to optical density, and then into oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) relative concentration changes via the modified Beer-Lambert law, in this case only oxyhemoglobin (HbO) relative concentration is presented for illustrative purposes. The data was digitally band-filtered by the computer 20 around the task-frequency f.sub.t, in this case using a zero-phase filter with a filter width of 0.015 Hz, other filtering and filter widths may be used for obtaining the signals around the task-frequency f.sub.t without introducing a delay, or compensating such delay.

[0048] As it can be seen in FIG. 4, during the baseline recording stage t.sub.1 with the subject in resting-state, near-infrared signals received from the near-infrared emitter 2 in the proximal and distal near-infrared detectors 3 at the task-frequency f.sub.t were recorded by the computer 20, the recorded signals comprising a baseline deep-signal BDS received by the distal near-infrared detector, and a baseline shallow-signal B.sub.SS received by the proximal near-infrared detector.

[0049] During the stimulation recording stage t.sub.2, with the subject undergoing a cyclic cerebral stimulation at the task-frequency f.sub.t according to the pattern of FIG. 1, near-infrared signals received from the near-infrared emitter 2 in the proximal and distal near-infrared detectors 3 at the task-frequency f.sub.t were recorded by the computer 20, the recorded signals comprising a shallow-signal SSS received by the proximal detector 3a, and a deep-signal SDS received by the distal detector 3b.

[0050] Since fNIRS signals are dominated by confounding hemodynamics not originated in the cerebral cortex by functional activity, but with origin in blood flow changes in superficial tissues and by changes in systemic physiology that are present both in the superficial layers and in the brain tissue itself, even at different time scales. To address this issue, an effective strategy is the use of multi-distance measurements. Shallow components are removed from the deep-signals by assuming that short-separation recordings are sensitive only to extra-cerebral changes while long-separation recordings are sensitive to both extra-cerebral and cerebral activity.

[0051] Therefore, at the task-frequency f.sub.t removing shallow components present in the shallow-signal SSS from the deep-signal SDS for obtaining the cerebral signal S.sub.CS may be expressed by the following linear combination:

S.sub.CS=S.sub.DS−(KS.sub.SS)

[0052] Since these signals at the task-frequency f.sub.t are considered to be sinusoids, only their amplitude and phase would differ.

[0053] The scaling factor K may be calculated by the computer 20 as the ratio of amplitudes between the baseline deep-signal BDS and the baseline shallow-signal B.sub.SS at the task-frequency f.sub.t. Multiple other methods are known for calculating by a computer this scaling factor K as the ratio of amplitudes of the baseline deep-signal BDS and the baseline shallow-signal B.sub.SS, such as detecting the average peak to peak ratio between the baseline deep-signal BDS and the baseline shallow-signal B.sub.SS, or the ratio between the root mean square measurements of the baseline deep-signal BDS and the baseline shallow-signal B.sub.SS.

[0054] Also, a transfer function strategy may be used by the computer 20 for calculating the scaling factor K, as transfer function models have become a popular approach to investigate the dynamic of cerebrovascular autoregulation (Claassen et al., 2015; Van Beek, Claassen, Rikkert, & Jansen, 2008), and they have also been used to remove systemic physiological noise from fNIRS signals (Bauernfeind, Böck, Wriessnegger, & Müller-Putz, 2013; Florian & Pfurtscheller, 1997). Assuming that the shallow-signal S.sub.SS has energy in the frequency range of interest, around the task-frequency f.sub.t, and contain quasi-periodic oscillations, the transfer function H(f) may be approximated from the experimental fNIRS data as (Zhang et al., 1998):

[00001]$H\left(f\right)=\frac{CPS{D}_{ssds}\left(f\right)}{PS{D}_{ss}\left(f\right)}$

[0055] For the time-series pair, shallow-signal S.sub.SS and deep-signal SDS are, respectively, the input and output signals used to obtain an approximation of the transfer function at the task-frequency f.sub.t. From the complex-valued result, we obtained the magnitude (gain), corresponding to the scaling factor K which represents the relative change in μM between input and output, and the phase that carries their temporal coupling (phase difference or time-lag). For reporting the gain, data were converted by the computer 20 into percentage values, which will be the scaling factor K. Then, the scaling factor K may be considered the gain value of the basal transfer function bTF at f.sub.t during “baseline”, it is, during the baseline recording stage t.sub.1, which represents the fraction of the magnitude of the shallow-signal S.sub.SS present in the deep-signal S.sub.DS when no significant cerebral signal S.sub.CS component contributes.

[0056] Therefore, during the step of calculating the scaling factor K between the amplitude of the basal deep-signal B.sub.DS and the basal shallow-signal B.sub.SS at the task-frequency f.sub.t an approximation of a complex frequency dependent basal transfer function bTF between the basal deep-signal BDS and the basal shallow-signal B.sub.SS may be computed by the computer 20 to determine the scaling factor K as the gain of the basal transfer function bTF at the task-frequency f.sub.t, as shown in FIG. 5a.

[0057] The phase difference between the deep-signal S.sub.DS and the shallow-signal S.sub.SS at the task-frequency f.sub.t may be obtained by determining by the computer 20 the delay between the deep-signal S.sub.DS and the shallow-signal S.sub.SS filtered at task-frequency f.sub.t as known by the skilled person, However, this phase difference may also be obtained by calculating an empirical transfer function TF in the frequency domain between the deep-signal S.sub.DS and the shallow-signal S.sub.SS during the stimulation recording stage t.sub.2 and calculating by the computer 20 the argument of the transfer function TF at the task-frequency f.sub.t, which is illustrated in FIG. 5b. Therefore, the scaling factor K and the phase difference that will be used to calculate the cerebral signal S.sub.CS are obtained.

[0058] FIG. 6a shows an averaged period of the periods of the deep-signal S.sub.DS and the shallow-signal S.sub.SS, while FIG. 6b shows the signals of FIG. 6a where the scaling factor K has been applied by the computer 20 to the shallow-signal S.sub.SS, thus reducing its amplitude.

[0059] Then, given the linear combination between the deep-signal S.sub.DS and the shallow-signal S.sub.SS for obtaining the cerebral signal S.sub.CS previously explained, the cerebral signal S.sub.CS at the task-frequency f.sub.t may be calculated by the computer 20 as the difference, in phase and amplitude, between the deep-signal S.sub.DS and the scaled shallow-signal KS.sub.SS, at the task-frequency f.sub.t, thus obtaining the cerebral signal S.sub.CS depicted in FIG. 6c.

[0060] Advantageously, since the deep-signal S.sub.DS and the shallow-signal S.sub.SS are sinusoidal signals with frequency the task-frequency f.sub.t, the deep-signal S.sub.DS and the shallow-signal S.sub.SS may be expressed as a respective phasor vectors corresponding to a deep-signal phasor X.sub.DS and shallow-signal phasor X.sub.SS, having the amplitudes and phases of their corresponding sinusoidal signals, so the linear combination previously indicated may also be expressed as a linear combination of phasors as indicated below:

S.sub.CS=S.sub.DS−(KS.sub.SS)

A.sub.CS cos(2πf.sub.tt+ϕ.sub.CS)=A.sub.DS cos(2πf.sub.tt+ϕ.sub.DS)−KA.sub.SS cos(2πf.sub.tt+ϕ.sub.SS)

A.sub.CSe.sup.jϕ.sup.CS=A.sub.DSe.sup.jϕ.sup.DS−KA.sub.SSe.sup.jϕ.sup.SS

X.sub.CS=X.sub.SD−KX.sub.SS

[0061] It is expected for one of the phases between the phase of the shallow-signal ϕ.sub.SS and the phase of the deep-signal ϕ.sub.DS to be a reference phase, typically a 0 degrees reference phase. In this case, the reference phase of 0 degrees is assigned to the phase of the shallow-signal ϕ.sub.SS.

[0062] This way, the difference, in phase and amplitude, between the deep-signal S.sub.DS and the scaled shallow-signal KS.sub.SS may directly be calculated by the computer 20 as a phasor subtraction of the deep-signal phasor X.sub.DS minus the shallow-signal phasor X.sub.SS as graphically represented in FIG. 7, for obtaining a cerebral signal phasor X.sub.CS with an amplitude and phase corresponding to the amplitude and phase of the cerebral signal S.sub.CS at the task-frequency f.sub.t.

[0063] Therefore, the step of obtaining the phase and amplitude of the cerebral signal S.sub.CS during stimulation comprises determining by the computer 20 a deep-signal phasor X.sub.DS corresponding to the deep-signal S.sub.DS during stimulation at the task-frequency f.sub.t, having as phase the phase difference between the deep-signal S.sub.DS and the shallow-signal S.sub.SS at the task-frequency f.sub.t, and amplitude the amplitude of the deep-signal S.sub.DS at the task-frequency f.sub.t; and determining a shallow-signal phasor X.sub.SS corresponding to the shallow-signal S.sub.SS during stimulation at the task-frequency f.sub.t, having a reference phase of 0 degrees, and as amplitude the amplitude of the shallow-signal S.sub.SS at the task-frequency f.sub.t, multiplied by the scaling factor K. Naturally, alternatively the reference phase of 0 degrees may be the phase of the deep-signal phasor X.sub.DS, or the reference phase may be any known phase, as only the phase difference is relevant.

[0064] Then, the phase and amplitude of the estimated cerebral signal S.sub.CS at the task-frequency f.sub.t may be calculated by the computer 20 by determining a cerebral-signal phasor X.sub.CS by directly subtracting the shallow-signal phasor X.sub.DS from the deep-signal phasor X.sub.DS, as graphically represented in FIG. 7.

[0065] Although previously only one emitter 2 and a proximal detector 3a and distal detector 3b group was used, also other multichannel, wireless, continuous-wave NIRS device 10 may be used, such as the Brainspy28, from Newmanbrain, S. L., which employs four emitters 2 and ten detectors 3 forming a rectangular grid of 80×20 mm. In this case, each emitter 2 housed two light-emitting-diodes (LED) at wavelengths 740 nm and 850 nm. Through a precise switching cycle, the device 10 combines pairs of optodes at different separation distances, providing 16 short-channels or shalllow channels SS and 12 long-channels or deep channels DS that corresponds to a source-detector distance of 14 and 32 mm respectively, as shown in FIG. 8 and in more detail in FIG. 9. Moreover, it is also expected that the device 10 may measure and correct the ambient light contribution, and it may also it incorporate a 3-axis accelerometer to account for head motion. Data may be wirelessly transmitted (for example via Bluetooth) at a sample rate of 10 Hz to the computer 20, that would execute the steps of processing the data to obtain the near-infrared spectroscopy fNIRS cerebral signal S.sub.CS.

[0066] As shown in FIG. 8, the NIRS probe may be applied onto the forehead, centered on AFpz according to the international 10-5 system, mainly covering the frontopolar area of the prefrontal cortex (PFC). It is expected that the optodes contact the skin through an intermediate convex lens pressing the skin when the probe is held firmly, in order to reduce cutaneous blood flow and, therefore, its hemodynamic interference.

[0067] Advantageously, by having a plurality of groups of near-infrared emitter and respective proximal and distal near-infrared detectors grouped by regions of interest, the shallow-signal and the deep-signal obtained for each region may be averaged, so the signal-noise ratio for each region of interest is improved, and a better cerebral signal S.sub.CS may be obtained per each region of interest.

[0068] While the present disclosure has been described with reference to example embodiments thereof, it will be apparent to those of ordinary skill in the art that various changes and modifications may be made thereto without departing from the spirit and scope of the present disclosure as set forth in the following claims and their equivalents.