A system includes a communication interface for receiving information that includes data collected from an array of neural activity sensors that were placed on a patient during a session of applied stimuli. A processor is configured to analyze the received information to obtain a frequency spectrum for each sensor for a given stimulus of the applied stimuli. Neural network frequencies that correspond to an indicated impaired functionality of the nervous system of the patient are selected. For each selected frequencies, a spatial map of neural activity is generated. Each of the generated spatial maps is compared with retrieved corresponding spatial maps to identify treatment frequencies from among the selected neural network frequencies. A treatment protocol is generated for input into an electromagnetic field generator to cause the generator to apply to the patient an electromagnetic field at each identified treatment frequency.

Provided is a system for estimating a subjective evaluation by an estimation subject. This system for estimating a subjective evaluation by an estimation subject comprises: a reception means that receives feature data of a biosignal acquired from the estimation subject; a storage means that stores a plurality of feature templates extracted from a plurality of biosignals acquired from a plurality of subjects to be modeled including a first subject to be modeled and a second subject to be modeled, or that stores a plurality of models trained using said feature templates; and an estimation means that estimates a subjective evaluation by the estimation subject on the basis of the feature data and the plurality of feature templates or the plurality of models.

Provided is a system for estimating a subjective evaluation by an estimation subject. This system for estimating a subjective evaluation by an estimation subject comprises: a reception means that receives feature data of a biosignal acquired from the estimation subject; a storage means that stores a plurality of feature templates extracted from a plurality of biosignals acquired from a plurality of subjects to be modeled including a first subject to be modeled and a second subject to be modeled, or that stores a plurality of models trained using said feature templates; and an estimation means that estimates a subjective evaluation by the estimation subject on the basis of the feature data and the plurality of feature templates or the plurality of models.

Disclosed is a method for the generation of a consciousness indicator for a non-communicating subject, including the steps of generating an auditory stimulation, receiving an electrocardiographic signal of the subject obtained from a recording during the generation of the auditory stimulation, extracting at least one feature from the electrocardiographic signal and deducing a consciousness indicator from an analysis of the electrocardiographic feature.

Systems, devices, and methods described herein relate to multi-sensory presentation devices, including virtual reality (VR) devices, visual display devices, sound devices, haptic devices, and other forms of presentation devices, that are configured to present sensory elements, including visual and/or audio scenes, to a user. In some embodiments, one or more sensors including electroencephalography (EEG) sensors and a photoplethysmography (PPG) sensors, e.g., included in a brain-computer interface, can measure physiological data of a user to monitor a state of the user during the presentation of the visual and/or audio scenes. Such systems, devices, and methods can adapt one or more visual and/or audio scenes based on user physiological data, e.g., to control or manage the state of the user.

Systems, devices, and methods described herein relate to multi-sensory presentation devices, including virtual reality (VR) devices, visual display devices, sound devices, haptic devices, and other forms of presentation devices, that are configured to present sensory elements, including visual and/or audio scenes, to a user. In some embodiments, one or more sensors including electroencephalography (EEG) sensors and a photoplethysmography (PPG) sensors, e.g., included in a brain-computer interface, can measure physiological data of a user to monitor a state of the user during the presentation of the visual and/or audio scenes. Such systems, devices, and methods can adapt one or more visual and/or audio scenes based on user physiological data, e.g., to control or manage the state of the user.

A method for estimating a brain activity response following a stimulus of a person comprises the steps: providing a usage data set of the person from a personal device used by said person, wherein at least one usage attribute is associated to said usage data set, wherein attribute data is associated to each of the at least one usage attribute, providing a computational inference model, generated from a plurality of brain activity data sets and a plurality of usage data sets, wherein each brain activity data set comprises data derived from a brain activity response following a sensory stimulus, submitting the attribute data of each of the at least one usage attributes to said computational inference model, estimating a brain activity response following a sensory stimulus of said person by evaluating said computational inference model for the submitted attribute data. The method is useful to determine, for example the influence of intensive touch pad usage (of a smartphone) on somatosensory evoked potentials.

A method for estimating a brain activity response following a stimulus of a person comprises the steps: providing a usage data set of the person from a personal device used by said person, wherein at least one usage attribute is associated to said usage data set, wherein attribute data is associated to each of the at least one usage attribute, providing a computational inference model, generated from a plurality of brain activity data sets and a plurality of usage data sets, wherein each brain activity data set comprises data derived from a brain activity response following a sensory stimulus, submitting the attribute data of each of the at least one usage attributes to said computational inference model, estimating a brain activity response following a sensory stimulus of said person by evaluating said computational inference model for the submitted attribute data. The method is useful to determine, for example the influence of intensive touch pad usage (of a smartphone) on somatosensory evoked potentials.

Brain modelling includes receiving time-coded bio-signal data associated with a user; receiving time-coded stimulus event data; projecting the time-coded bio-signal data into a lower dimensioned feature space; extracting features from the lower dimensioned feature space that correspond to time codes of the time-coded stimulus event data to identify a brain response; generating a training data set for the brain response using the features; training a brain model using the training set, the brain model unique to the user; generating a brain state prediction for the user output from the trained brain model, and automatically computing similarity metrics of the brain model as compared to other user data; and inputting the brain state prediction to a feedback model to determine a feedback stimulus for the user, wherein the feedback model is associated with a target brain state.

Brain modelling includes receiving time-coded bio-signal data associated with a user; receiving time-coded stimulus event data; projecting the time-coded bio-signal data into a lower dimensioned feature space; extracting features from the lower dimensioned feature space that correspond to time codes of the time-coded stimulus event data to identify a brain response; generating a training data set for the brain response using the features; training a brain model using the training set, the brain model unique to the user; generating a brain state prediction for the user output from the trained brain model, and automatically computing similarity metrics of the brain model as compared to other user data; and inputting the brain state prediction to a feedback model to determine a feedback stimulus for the user, wherein the feedback model is associated with a target brain state.