A biological signal analysis device includes: an acquiring unit configured to acquire biological signals of a measurement target; a trigger information acquiring unit configured to acquire, from a stimulator configured to apply stimuli to the measurement target, trigger information indicating times at which the stimuli are generated; and a signal processing unit configured to process the biological signals. The signal processing unit is configured to calculate biological information on the measurement target based on the biological signals, maintain only pieces of trigger information corresponding to times at which it is determined that biological signals of the measurement target are generated, from the calculated biological information, delete another piece of trigger information, and use an averaged waveform that is obtained by performing an averaging process on the biological signals that are generated in synchronization with the stimuli based on the pieces of remaining trigger information.

A method of predicting the long-term hedonic response to at least one predetermined sensory stimulus for an individual is disclosed. The method comprises the steps of (a) exposing the individual to the at least one sensory stimulus for a number of times over an initial time period of exposure according to an exposure pattern, (b) for each exposure, obtaining data indicative of the individual's hedonic response to the at least one sensory stimulus, (c) providing the data indicative of the individual's hedonic response and the exposure pattern to a machine learning algorithm, and (d) predicting the individual's long-term hedonic response to the sensory stimulus by the machine learning algorithm for a time point a predetermined prediction time period after the initial time period of exposure.

A method of predicting the long-term hedonic response to at least one predetermined sensory stimulus for an individual is disclosed. The method comprises the steps of (a) exposing the individual to the at least one sensory stimulus for a number of times over an initial time period of exposure according to an exposure pattern, (b) for each exposure, obtaining data indicative of the individual's hedonic response to the at least one sensory stimulus, (c) providing the data indicative of the individual's hedonic response and the exposure pattern to a machine learning algorithm, and (d) predicting the individual's long-term hedonic response to the sensory stimulus by the machine learning algorithm for a time point a predetermined prediction time period after the initial time period of exposure.

The present invention relates to a method for provision of information on a mental disorder and a device utilizing same, the method for provision of information on a mental disorder being implemented by a processor, and comprising: outputting a stimulus to a subject in order to generate brain waves; receiving brain wave data and brain activity data measured in the subject during outputting the stimulus; and determining whether a mental disorder is present in the subject by using a classification model configured to classify mental disorders on the basis of the brain wave data and the brain activity data.

The present invention relates to a method for provision of information on a mental disorder and a device utilizing same, the method for provision of information on a mental disorder being implemented by a processor, and comprising: outputting a stimulus to a subject in order to generate brain waves; receiving brain wave data and brain activity data measured in the subject during outputting the stimulus; and determining whether a mental disorder is present in the subject by using a classification model configured to classify mental disorders on the basis of the brain wave data and the brain activity data.

Systems and methods for measuring physiological responses caused by cognitive load and stress to calculate a probability of deception. The system comprises modules to determine changes in pupil dilation, flicker frequency, pupil eye trajectory, face temperature, respiratory rate, position of the user's facial points, heart rate, skin conductivity, body movements, arm temperature, temperature, and electroencephalography signals; and modules to provide stimuli to the user, record conscious responses from the user, receive physiological signals, and determine changes in the user's physiological variables associated with stress and cognitive load in response to the stimuli generated, to calculate a likelihood of deception in conscious responses.

Systems and methods for measuring physiological responses caused by cognitive load and stress to calculate a probability of deception. The system comprises modules to determine changes in pupil dilation, flicker frequency, pupil eye trajectory, face temperature, respiratory rate, position of the user's facial points, heart rate, skin conductivity, body movements, arm temperature, temperature, and electroencephalography signals; and modules to provide stimuli to the user, record conscious responses from the user, receive physiological signals, and determine changes in the user's physiological variables associated with stress and cognitive load in response to the stimuli generated, to calculate a likelihood of deception in conscious responses.

The present method and system provides a neuromodulation therapy including receiving a plurality of input data relating to a patient, the input data including brain value measurements. The method and system includes analyzing the input data in reference to reference data generated based on machine learning operations associated with existing patient data and reference database data. Based thereon, the method and system includes electronically determining, a brain malady and a severity value for the patient and electronically generating a treatment protocol for the patient, the treatment protocol includes transcranial stimulation parameters. Therein, the method and system includes applying a transcranial stimulation using the transcranial stimulation parameters based on the treatment protocol.

The present method and system provides a neuromodulation therapy including receiving a plurality of input data relating to a patient, the input data including brain value measurements. The method and system includes analyzing the input data in reference to reference data generated based on machine learning operations associated with existing patient data and reference database data. Based thereon, the method and system includes electronically determining, a brain malady and a severity value for the patient and electronically generating a treatment protocol for the patient, the treatment protocol includes transcranial stimulation parameters. Therein, the method and system includes applying a transcranial stimulation using the transcranial stimulation parameters based on the treatment protocol.

An apparatus for measuring patient data includes a frame having a plurality of electrode hubs. Each hub can include one or more electrode members. The frame can be configured to receive a head of a patient. Each of the electrode hubs can have a single electrode member or a plurality of electrode members that extend from or are connected to an outer member for contacting a scalp of the head of the patient. The outer member can have at least one circuit configured to transmit data received by at least one of the electrode members to a measurement device via a wireless communication connection (e.g. Bluetooth, near field communication, etc.) or a wired communication connection.