The present invention concerns an apparatus for treating neurological disorders comprising (i) at least one electrode implantable in the brain of a patient and (ii) a processing and stimulation device connected to the at least one electrode, wherein the processing and stimulation device comprises (a) at least one stimulation module adapted to generate a stimulation signal to be sent to the at least one electrode, the stimulation signal being characterised by a plurality of parameters, (b) at least one acquisition module of a signal characteristic of cerebral activity coming from the brain of the patient adapted to determine its power in at least one frequency band, and (c) at least one control module of at least one parameter of the stimulation signal as a function of the power of the signal characteristic of cerebral activity acquired, based on a transfer function having a saturating trend, wherein the transfer function is such as to set the at least one parameter (Va, Vd, Vf) of the stimulation signal (Vstim) differently dependent on a plurality of power ranges, by keeping the at least one parameter within a predetermined stimulation range ([Vi_HighThreshold; Vi_LowThreshold]) with i=a,d,f.

Disclosed is a method for assessing a cognition and movement disease or disorder in a subject. In the method, a qualimetric activity parameter for cognition and/or fine motoric activity measurements is determined from a dataset of measurements obtained from the subject using a mobile device. The qualimetric activity parameter is compared to a reference and the disease or disorder is thereby assessed. A method identifying whether a subject will benefit from a therapy for a cognition and movement disease or disorder is also disclosed. The method can be carried out with a mobile device having a processor, a sensor and a database as well as software that carries out the method. Also disclosed is a system having a mobile device with a sensor and a remote device having a processor and a database and software that carries out the method for assessing a cognition and movement disease or disorder.

An interactive multi-purpose interactive cognitive platform is provided that uses image-based interactivities and may use enriched visual stimuli for diagnoses, treatment, and to evaluate and assess the progress and/or treatment of cognitive diseases and/or conditions with cognitive impacts. The platform may be used for performance enhancement training. The platform may include a graphical user interface. The platform is designed to cooperatively engage multiple cognitive domains using interactivities-embedded assessments. The images and image sets may involve the application of Gestalt principles for assessments and may be personalized address individual user preferences. The multi-purpose interactive cognitive platform may be used to produce diagnostics and/or treatment plans for specific cognitive conditions and diseases with a cognitive component. The multi-purpose interactive cognitive platform may be used for gaming, stress reduction, mindfulness, skills development, training, and/or performance enhancement for those without cognitive problems and/or for those interested in maintaining their cognitive health.

Presented are concepts for m classifying a disease or disability of a subject. One such concept comprises obtaining interaction data associated with a subject, the interaction data being representative of the subject's interaction with a movement-based input device. The interaction data is processed with a first machine learning process to determine a set of 5 characteristics for describing the subject. The set of characteristics is then processed with a second machine-learning process to generate a classification result for the subject. An instruction is provided to the subject for directing the subject to interact with the movement-based input device, wherein the instruction defines a challenge comprising a time-varying parameter.

A state of progression in an individual of a disease or treatment having motion symptoms is determined. A time series of accelerometer data is obtained from an accelerometer worn on an extremity of the person, over an extended period during everyday activities of the person. The accelerometer data is processed to produce a plurality of measures of kinetic state of the individual at a respective plurality of times throughout the extended period, each measure of kinetic state comprising at least one of: a measure for bradykinesia, and a measure for dyskinesia. A measure of dispersion of the measures of kinetic state is determined. An output is generated, indicating that motion symptoms are at an initial stage if the measure of dispersion is less than a threshold, or indicating that motion symptoms are at an advanced stage if the measure of dispersion is greater than the threshold.

In an example embodiment, this disclosure provides a non-transitive computer-readable medium on which are stored instructions executable by a processor, the instructions which, when executed by the processor, cause the processor to perform a method. The method includes computing, based on test performance data of a user, at least one of a performance variable characterizing cognitive functioning and a performance variable characterizing neuromotor functioning. For each of the at least one performance variable, a respective score can be computed based on the respective performance variable and based on a set of performance metrics. The method can also include outputting, via an output device, the at least one computed score.

A method includes: obtaining, by a computer, subject response data including audio data, video data, or audio-video data of a subject's response to one or more stimuli presented to the subject over a defined time period; extracting at least one subject descriptor from the subject response data as a function of time, the at least one subject descriptor including data characterizing involuntary or voluntary action of the subject in response to the one or more stimuli; deriving, from the at least one subject descriptor, a first biomarker characterizing a behavior of the subject in response to the one or more stimuli, in which a value of the first biomarker is indicative of a severity of a disease in the subject; and outputting a disease severity level for the disease as a function of the value of the first biomarker.

Systems and methods are disclosed to detect and/or classify electrophysiological signals as motor events. In some embodiments, a method may include: recording a first plurality of electrophysiological signals from a first plurality of probes inserted into the left hemisphere of the brain; recording a second plurality of electrophysiological signals from a second plurality of probes inserted into the right hemisphere of the brain; pre-processing the first plurality of electrophysiological signals and the second plurality of electrophysiological signals; bipolar re-referencing the first plurality of electrophysiological signals and the second plurality of electrophysiological signals; determining an optimal pair of electrophysiological signals from the bipolar re-referenced first plurality of electrophysiological signals and the bipolar re-referenced second plurality of electrophysiological signals; matching the optimal pair of electrophysiological signals with a template; and detecting motor events from the matching.

The abnormal data processing system is provided with: a storage unit for holding a multiple-subject DB in which data on multiple subjects are accumulated and individual-subject DB in which data on individual subjects are accumulated; an individual-subject DB divergence-degree calculation unit for calculating an individual-subject DB divergence degree which is the degree of divergence of the new data from the individual-subject DB; a multiple-subject DB divergence degree calculation unit for calculating a multiple-subject DB divergence degree which is the degree of divergence of the new data from the multiple-subject DB; and a composite divergence degree calculation unit for determining a composite divergence by compositing the individual-subject DB divergence degree and the multiple-subject DB divergence degree using the number of data instances in the individual-subject DB. The abnormal data processing system determines whether or not the new data is abnormal on the basis of the composite divergence degree.

Disclosed herein are systems and methods for treating memory impairment, and more specifically to customized visual presentation for treating memory-related disorders and diseases. The disclosed systems and methods can predict clinical status of patients based on platform user behavior, such as those of patients. This abstract is intended as a scanning tool for purposes of searching in the particular art and is not intended to be limiting of the present invention.