Patent Description:
Neurofeedback is a biofeedback training which involves self-regulation of ongoing brain activity by sensory feedback. Neurofeedback may be used for various purposes, such as for instance for improving focus and attention, especially for subjects suffering attention deficit hyperactivity disorder.

Changes made in the desired direction are rewarded in a way that is understandable by a non-specialist, for instance with particular tones or pictures. On the contrary, negative feedback may be provided for undesirable or deviant brain activity.

In order to provide efficient neurofeedback training, i.e. to enable the subject to gain control of the brain activity and to train it in the desired direction, the threshold between a positive and negative reward must be finely tuned. Indeed, threshold adjustment stimulates the subjects and maintains engagement to the session.

The actual clinical practice of neurofeedback supposes trainer supervision with manual selection of the threshold during the session. The subject's resting-state brain activity is recorded for few seconds, in order to calibrate the algorithms. Then the subject performs the training session. During sessions, his brain activity is recorded, for instance by electroencephalography (EEG) on several channels (also known as electrodes), while he performs a task. A value representative of the EEG signals (in some specific frequency ranges) is then computed. It is integrated over a time window (called "integration window"), which is taken every ε seconds (potentially overlapping time windows), and averaged over all electrodes (see <FIG>). Said measure of activity is called hereafter a neuromarker and the consecutive integration over windows leads to a neuromarker time-series of frequency <NUM>/ε.

The extracted neuromarker modulates a feedback that is played (auditory feedback) or displayed (visual feedback) to the subject, and that reflects the neuromarker variation. A threshold is applied on the neuromarkers so that the subject is rewarded when his activity is below or above the threshold, depending on the protocol (e.g. downtraining protocol rewards the subject if the neuromarker is below the threshold and uptraining protocol rewards the subject if the neuromarker is above the threshold). The specialist changes the threshold manually, adjusting on the subject's performance evolution and accounting for its variability. The purpose of the manual adjustment is to maintain a reward ratio that stimulates the subjects and maintain engagement: it should be neither too easy (subject is bored) nor too difficult (subject is discouraged). It is sometimes claimed that the "art" of neurofeedback resides in the practitioner's ability to adjust these thresholds values to maintain the right levels of engagement and challenge and thereby maximize efficacy.

Consequently, neurofeedback sessions have to be performed by the subject with assistance of a trained specialist. Said supervision considerably limits availability of neurofeedback sessions. Moreover, it also limits feasibility studies and repeatability.

Unsupervised automatic adjustment of the threshold has also been implemented. Existing automatic thresholding systems are implemented based on a moving average time window. A time window, that generally lengths few seconds (also named "estimation time window"), moves forward with a regular pace of δ seconds (potentially overlapping time windows). On every window, a threshold is computed (see <FIG>) such that it provides rewards at an expected reward rate (also referred to as "expected reward ratio"). Said type of threshold has three major drawbacks: first, it is strongly influenced by artefacts in the EEG signal at all times. Second, a way-too-adaptive threshold does not allow the subject to learn and progress. With such automatic adjustments, the reward ratio is maintained at a constant rate whatever the actual neuromarker value, thereby hindering the conditioning and ultimately the self-modulation process that mediates the therapeutic or non-therapeutic effect. Finally, such thresholding does not mime specialist's habits who update the threshold only when subject's performances are too far from the expected reward ratio.

For example, Lansbergen et al. discloses reward threshold levels adjusted automatically based on the digitally filtered real-time EEG signal every <NUM> so that the subject was rewarded about <NUM>% of the time (<NPL>). However, the analysis of Lansbergen study did not reveal significant differences between EEG-neurofeedback training group and placebo feedback group, wherein the feedback is not related to the brain activity but to a simulated EEG signal.

Consequently, automatic adjusted reward threshold according to the prior art does not work as effective as manually adjusted reward threshold.

There is therefore a need for a new method and system, wherein neurofeedback sessions can be performed by the subject without the supervision of a trainer with a preserved efficacy, the method and system being such that threshold adjustment imitates trainer practice. <CIT> discloses a system for biofeedback training of a subject comprising at least one sensor, a computing unit, a memory and at least one interacting means.

In a first aspect not part of the present invention, a method for biofeedback training of a subject. Said method comprising iteratively:.

wherein at each iteration the time windows are moved forward in time.

In one embodiment not part of the present invention, the first time window used for obtaining a biomarker is referred to as the integration time window. According to one embodiment not part of the present invention, the integration time window ranges from <NUM> second to <NUM> seconds, preferably from <NUM> second to <NUM> seconds. According to one embodiment not part of the present invention, the integration time window is a moving overlapping window. According to one embodiment not part of the present invention, the integration time window is a moving non-overlapping window. According to one embodiment not part of the present invention, at each iteration the integration time window is moved forward in time with a pace ε, preferably a regular pace.

In one embodiment not part of the present invention, the second time window used for computing the intermediate threshold Thrintermediate(t) is referred to as the estimation time window. According to one embodiment not part of the present invention, the estimation time window ranges from few seconds to several tens of seconds, preferably from <NUM> seconds to <NUM> seconds. According to one embodiment not part of the present invention, the estimation time window is a moving overlapping window. According to one embodiment not part of the present invention, the estimation time window is a moving non-overlapping window. According to one embodiment not part of the present invention, at each iteration the estimation time window is moved forward in time with a pace δ, preferably a regular pace. In one embodiment not part of the present invention, the pace of the estimation time window δ differs from the pace of the integration time window ε.

In one embodiment not part of the present invention, the reward reported in real-time to the subject is based on the difference between the current biomarker at time (t) and the computed threshold Thr(t) at time (t).

According to one embodiment not part of the present invention, the expected reward ratio ranges from <NUM>% to <NUM>%.

According to one embodiment not part of the present invention, the sum of the weighting factors is equal to <NUM>.

According to one embodiment not part of the present invention, the threshold is computed as follows: <MAT> wherein α is a constant or variable coefficient and ranges between <NUM> and <NUM>. According to a preferred embodiment not part of the present invention, the coefficient α ranges strictly between <NUM> and <NUM>. According to one embodiment not part of the present invention, α is not equal to <NUM> or <NUM>.

If α equals to <NUM>, the computed threshold Thr(t) matches the moving average automated threshold computed on the estimation time window Thr(t) = Thrintermediate(t). If α equals to <NUM>, the computed threshold Thr(t) reaches a plateau and the threshold at time t equals the threshold at time (t-<NUM>): Thr(t) = Thr(t-<NUM>). The closer α is to <NUM>, the more the weight of the threshold's history Thr(t-<NUM>) increases leading to computed threshold that takes past values into account. Also, as α increases the threshold progressively converges towards a constant value.

According to one embodiment not part of the present invention, the coefficient α follows a logistic model, preferably a sigmoid model. According to one embodiment not part of the present invention, the coefficient α is variable and αt = <MAT>; wherein k is a learning coefficient and r a growth rate.

The coefficient α is used to compute a threshold that adapts from data and stabilizes as the threshold approaches its limit. As α increases all the way to <NUM>, the weight of the threshold's history Thr(t-<NUM>) increases leading to computed threshold that takes past values into account. Also, as α increases the threshold progressively converges towards a constant value.

According to one embodiment not part of the present invention, α<NUM> ranges from <NUM> to <NUM>. According to one embodiment not part of the present invention, α<NUM> ranges strictly between <NUM> and <NUM>.

According to said embodiment not part of the present invention, the coefficient α is bounded between the initial value of α (i.e. α<NUM>) and k, the learning coefficient. Furthermore, α converges towards k with a convergence speed that depends on the growth rate r.

k denotes the maximum values of the coefficient α, i.e. the model asymptote. According to one embodiment not part of the present invention, k ranges from <NUM> to <NUM>. For k equals to <NUM>, the coefficient α converges towards <NUM> where the learning stops and αt = αt-<NUM>.

r defines the slope of the coefficient α. The higher is r, the faster is the convergence towards an asymptotic value of α. According to one embodiment not part of the present invention, r ranges from <NUM> to <NUM>.

This behaviour is illustrated in <FIG>. The evolution of α, of the threshold computed on the previous iteration Thr(t-<NUM>), of the intermediate threshold Thrintermediate(t) and of the threshold Thr(t) are depicted over time. In said exemplary embodiment, α<NUM> is equal to <NUM>.

According to one embodiment not part of the present invention, the coefficient α is reset to a predefined initial value α<NUM> if the reward ratio computed during a third time window departs from more than a reward ratio tolerance around the expected reward ratio.

In one embodiment not part of the present invention, the third time window used for resetting α is referred to as the reward ratio tolerance time window. According to one embodiment not part of the present invention, the reward ratio tolerance time window ranges from <NUM> second and the session duration, preferably from <NUM> seconds to <NUM> minute.

According to one embodiment not part of the present invention, the reward ratio tolerance ranges between <NUM>% (no tolerance, the threshold resets every time the reward ratio computed on the estimation time window is not exactly equal to the expected reward ratio) to <NUM>% (large tolerance, the reward ratio can take any value without inducing a threshold reset).

According to one embodiment not part of the present invention, as depicted in <FIG>, a first reward is reported to the subject if the subject maintained the biomarker above the computed threshold Thr(t) during a time longer than a time gating parameter (for an up training protocol). For a down training protocol, a first reward is reported to the subject if the subject maintained the biomarker below the computed threshold Thr(t) during a time longer than a time gating parameter.

So when the biomarker is trained towards a defined direction, either above or below the computed threshold Thr(t), a first reward is reported to the subject if the subject maintained the biomarker respectively above or below the computed threshold Thr(t) during a time longer than a time gating parameter. Said time gating prevents from rewarding transitory artefacts. According to one embodiment not part of the present invention, the time gating is below <NUM> second, preferably ranging from <NUM> to <NUM> milliseconds.

According to one embodiment, as depicted in <FIG>, a second reward is reported to the subject if the subject maintained the biomarker above the computed threshold Thr(t) during a time longer than a time boosting parameter (for an up training protocol). For a down training protocol, a second reward is reported to the subject if the subject maintained the biomarker below the computed threshold Thr(t) during a time longer than a time boosting parameter.

Consequently, when the biomarker is trained towards a defined direction, either above or below the computed threshold Thr(t), a second reward is reported to the subject if the subject maintained the biomarker respectively above or below the computed threshold Thr(t) during a time longer than a time boosting parameter. According to one embodiment not part of the present invention, the time boosting is longer than the time gating. According to one embodiment not part of the present invention, the time boosting is ranging from <NUM> to <NUM> seconds.

According to one embodiment not part of the present invention, the initial values of the threshold are computed from a previous session or from a series of biomarkers computed from a bio-signal obtained under a given condition. Said bio-signal obtained under a given condition is for instance a resting state bio-signal.

According to one embodiment not part of the present invention, the method according to the present invention further comprises the step of removal of artefacts from the bio-signal of the subject before computing the biomarker representative of the bio-signal on the first time window. Said removal of artefacts may be performed using any real-time artefact removal algorithms known by one skilled in the art.

In a second aspect not part of the present invention, this invention also aims at proposing a method for automated initialization of the parameters required for implementing the method for biofeedback training of a subject according to the invention, wherein the method comprises the following steps:.

According to one embodiment not part of the present invention, said method for automated initialization of the parameters is implemented subjectwise, on a population, or for different trainers.

According to one embodiment not part of the present invention, the method for automated initialization of the parameters comprises:.

According to one embodiment not part of the present invention, said method is also implemented for obtaining the following parameters: the expected reward ratio, the time gating, the time boosting, the estimation time window, the reward ratio tolerance and the reward ratio tolerance time window.

According to exemplary embodiment, the error function is a method of least squares.

The present invention aims at proposing a system for implementing the method according to the invention.

In one embodiment of the present invention, said system for biofeedback training of a subject comprises:.

In one embodiment, the first time window used for obtaining a biomarker is referred to as the integration time window. According to one embodiment, the integration time window ranges from <NUM> second to <NUM> seconds, preferably from <NUM> second to <NUM> seconds. According to one embodiment, the integration time window is a moving overlapping window. According to one embodiment, the integration time window is a moving non-overlapping window. According to one embodiment, at each iteration the integration time window is moved forward in time with a pace ε, preferably a regular pace.

In one embodiment, the second time window used for computing the intermediate threshold Thrintermediate(t) is referred to as the estimation time window. According to one embodiment, the estimation time window ranges from few seconds to several tens of seconds, preferably from <NUM> seconds to <NUM> seconds. According to one embodiment, the estimation time window is a moving overlapping window. According to one embodiment, the estimation time window is a moving non-overlapping window. According to one embodiment, at each iteration the estimation time window is moved forward in time with a pace δ, preferably a regular pace. In one embodiment, the pace of the estimation time window δ differs from the pace of the integration time window ε.

According to one embodiment, the initial threshold Thr(<NUM>) is computed from a previous session or from a series of biomarkers computed from a bio-signal obtained under a given condition. Said bio-signal obtained under a given condition is for instance a resting state bio-signal.

In one embodiment, the reward reported in real-time to the subject is based on the difference between the current biomarker at time (t) and the computed threshold Thr(t) at time (t).

According to one embodiment, the expected reward ratio ranges from <NUM>% to <NUM>%. According to one embodiment, the sum of the weighting factors is equal to <NUM>.

According to one embodiment, the coefficient α follows a logistic model, preferably a sigmoid model.

According to one embodiment, the computing unit computes the coefficient α as follows: <MAT> and
wherein the memory comprises a learning coefficient k, a growth rate r and an initial value α<NUM> of the coefficient α.

According to one embodiment, α<NUM> ranges from <NUM> to <NUM>. According to one embodiment, k ranges from <NUM> to <NUM>. According to one embodiment, r ranges from <NUM> to <NUM>.

According to one embodiment, the memory further comprises a reward ratio tolerance and a third time window; and wherein the computing unit reset the coefficient α to its initial value α<NUM> if the reward ratio computed during the third time window departs from more than the reward ratio tolerance around the expected reward ratio.

In one embodiment, the third time window used for resetting α is referred to as the reward ratio tolerance time window. According to one embodiment, the reward ratio tolerance time window ranges from <NUM> second and the session duration, preferably from <NUM> seconds to <NUM> minute.

According to one embodiment, the reward ratio tolerance ranges between <NUM>% (no tolerance, the threshold resets every time the reward ratio computed on the estimation time window is not exactly equal to the expected reward ratio) and <NUM>% (large tolerance, the reward ratio can take any value without inducing a threshold reset).

According to one embodiment, the memory further comprises a time gating and a first reward is reported to the subject by the interacting means if the subject maintained the biomarker above or below the computed threshold Thr(t) during a time longer than a time gating parameter, depending if the biomarker is trained towards respectively above or below the threshold.

According to one embodiment, the time gating is below <NUM> second, preferably ranging from <NUM> to <NUM> milliseconds. According to one embodiment, the time boosting is longer than the time gating. According to one embodiment, the time boosting is ranging from <NUM> to <NUM> seconds.

According to one embodiment, the memory further comprises a time boosting and a second reward is reported to the subject by the interacting means if the subject maintained the biomarker above or below the computed threshold Thr(t) during a time longer than a time boosting parameter, depending if the biomarker is trained towards respectively above or below the threshold.

According to one embodiment, the computing unit further implements the step of removal of artefacts from the bio-signal of the subject before computing the biomarker representative of the bio-signal on a first time window.

According to a preferred embodiment, the bio-signal is obtained using electroencephalography.

According to one embodiment, the memory further comprises a series of biomarkers of a subject and a series of thresholds manually chosen by an operator during said previous session; and the computing unit identifies the optimal set of parameters that minimize an error function between the manual threshold and the threshold Thr(t) computed from the same series of biomarkers with the method according to the invention. According to one exemplary embodiment, the error function is a method of least squares.

In one embodiment, the following parameters may be automatically initialized: the expected reward ratio, the time gating, the time boosting, the second time window, the reward ratio tolerance, the third ratio tolerance time window, the growth rate, the learning coefficient and/or the initial value α<NUM> of the coefficient α.

According to the Applicant, the added value of the present invention is that it has two behaviours: (i) it follows and is adjusted to the subject's biomarker - in a way similar to that of moving average models - and, (ii) it can stabilize for given periods of time, which temporarily challenges the subject. Hence, it fully allows the optimization of both the subject's engagement and his challenge.

Moreover, the way the thresholds adapts and stabilizes depends on "parameters", which can be set to cover a broad range of behaviours. For instance, the model can learn quickly and keep adjusting to subject's evolution, or it can slowly evolve and finally converge to a value. These parameters and the broad range of behaviours they generate cover the inter-operator variation of practice and make of it a versatile tool, which can be used in several ways:.

The present invention was implemented with a set of parameters selected in order to design a threshold that complies with the following:.

The result is displayed in <FIG>. The threshold is constant at the beginning of the signal, as it has been previously initialized on the calibration. After approximately <NUM> seconds in the signal, the threshold resets, and computes its new value based on the intermediate threshold's value computed on the current moving average window and previous values of the threshold, according to parameters r and α<NUM>, in order to provide rewards <NUM>% of the time. The threshold is then stabilized to its new value equal to <NUM> for about <NUM> seconds before a new reset and a threshold value that decreases and stabilized around <NUM>.

The present invention was implemented with a different set of parameters.

Claim 1:
A system for biofeedback training of a subject, said system comprising:
- at least one sensor for obtaining a bio-signal of the subject;
- a computing unit for computing a series of biomarkers, each biomarker being representative of the bio-signal of the subject on a first time window; and
- a memory comprising the series of biomarkers and a set of parameters including an expected reward ratio and an initial threshold Thr(<NUM>);
- at least one interacting means for reporting a reward to the subject, said reward being based on the difference between the biomarker and a computed threshold Thr(t) ;
wherein the memory is connected to the computing unit for delivering the series of biomarkers and the set of parameters, and the sensor is connected to the computing unit for delivering the biomarkers;
wherein the computing unit computes an intermediate threshold Thrintermediate(t) based on the series of biomarkers on a second time window, such that said intermediate threshold on said second time window could provide the subject with an expected reward ratio;
wherein the threshold Thr(t) is a weighted sum of the intermediate threshold Thrintermediate(t) and the threshold of the previous iteration Thr(t-<NUM>) computed as Thr(t) = α * Thr(t-<NUM>) + (<NUM> - α) * Thrintermediate(t), the coefficient α being variable and ranging between <NUM> and <NUM>; and
wherein after each iteration the time windows are moved forward in time.