A method of transplanting a sleep state of a first subject (donor) to a second subject (recipient) comprising: capturing a sleep state of the first subject represented by brain activity patterns; and transplanting the sleep state of the first subject in the second subject by inducing the brain activity patterns in the second subject.

A rider state modification system for improving a state of a rider in a vehicle includes a first neural network that operates to classify a state of the vehicle through analysis of information about the vehicle captured by an Internet-of-things device during operation of the vehicle. The rider state modification system further includes a second neural network that operates to optimize at least one operating parameter of the vehicle based on the classified state of the vehicle, information about a state of a rider occupying the vehicle, and information that correlates vehicle operation with an effect on rider state.

The present disclosure pertains to a system and method for automatically detecting rapid eye movement (REM) sleep and delivering sensory stimulation to prolong REM duration, without disturbing sleep. The sensory stimulation may be auditory or other stimulation. The system and method ensure timely delivery of the stimulation and automatically adjust the amount, intensity, and/or timing of stimulation as necessary. REM sleep is detected based on brain activity, cardiac activity and/or other information. REM sleep may be detected and/or predicted by a trained neural network. The amount, timing, and/or intensity of the sensory stimulation may be determined and/or modulated to enhance REM sleep in a subject based on one or more values of one or more intermediate layers of the neural network and one or more brain activity and/or cardiac activity parameters.

The present invention discloses a method for determining a degree of response to a physical activity. Acquire a physical activity signal measured by a sensing unit in the physical activity. Determine first data of a first physical activity feature set based on the physical activity signal. Determine a recognition of the degree of response to the physical activity based on the first data of the first physical activity feature set by a mathematical model describing a relationship between the first physical activity feature set and the degree of response to a physical activity. A portion of a first mechanism of the mathematical model adopts at least one portion of a second mechanism of a first neural network model associated with the second physical activity feature set.

A sensor apparatus configured to detect at least one physiological parameter of a subject includes a sensor that detects at least one physiological parameter of the subject as sensor data. An energy harvesting circuit includes a plurality of energy harvesting devices configured to harvest ambient energy from an environment of the subject. The energy harvesting devices generate power at a plurality of voltage potential levels from ambient energy. A conditioning circuit is configured to adjust the plurality of voltage potential levels to a bus voltage supplied to a supply bus. A controller receives operating power via the supply bus and controls the activation of the sensor and the wireless communication circuit.

A sensor apparatus configured to detect at least one physiological parameter of a subject includes a sensor that detects at least one physiological parameter of the subject as sensor data. An energy harvesting circuit includes a plurality of energy harvesting devices configured to harvest ambient energy from an environment of the subject. The energy harvesting devices generate power at a plurality of voltage potential levels from ambient energy. A conditioning circuit is configured to adjust the plurality of voltage potential levels to a bus voltage supplied to a supply bus. A controller receives operating power via the supply bus and controls the activation of the sensor and the wireless communication circuit.

Embodiments may provide multimodal diagnostic systems and methods for detecting neurological disorders, such as Alzheimer's disease (AD), Parkinson's disease (PD), depression, PTSD, schizophrenia, dementia and many others. For example, a system for monitoring brain activity may comprise a plurality of sensors, each adapted to monitor a physical or physiological parameter and output a signal representing the monitored physical or physiological parameter, wherein the plurality of sensors includes at least one sensor configured to monitor a brain activity parameter, a data collection device adapted to receive the plurality of signals from the plurality of sensors and to process the signals to form digital data representing the monitored physical or physiological parameters, and a data processing device adapted to process digital data representing the monitored physical or physiological parameters to determine presence of a neurological disorder or condition.

Embodiments may provide multimodal diagnostic systems and methods for detecting neurological disorders, such as Alzheimer's disease (AD), Parkinson's disease (PD), depression, PTSD, schizophrenia, dementia and many others. For example, a system for monitoring brain activity may comprise a plurality of sensors, each adapted to monitor a physical or physiological parameter and output a signal representing the monitored physical or physiological parameter, wherein the plurality of sensors includes at least one sensor configured to monitor a brain activity parameter, a data collection device adapted to receive the plurality of signals from the plurality of sensors and to process the signals to form digital data representing the monitored physical or physiological parameters, and a data processing device adapted to process digital data representing the monitored physical or physiological parameters to determine presence of a neurological disorder or condition.

The present disclosure provides a method for determining preference implemented by a processor, comprising: providing image content to a user; receiving electroencephalogram (EEG) data and gaze data including a series of gaze position data or gaze speed data which is measured while the content is provided; determining the user's region of interest with respect to the content based on the gaze data; determining a saccade onset time based on the gaze data; extracting EEG data during a time period including the saccade onset time, based on the EEG data; and determining whether the user prefers the region of interest based on the EEG data during the time period, and a device for determining preference using the same.

The present disclosure provides a method for determining preference implemented by a processor, comprising: providing image content to a user; receiving electroencephalogram (EEG) data and gaze data including a series of gaze position data or gaze speed data which is measured while the content is provided; determining the user's region of interest with respect to the content based on the gaze data; determining a saccade onset time based on the gaze data; extracting EEG data during a time period including the saccade onset time, based on the EEG data; and determining whether the user prefers the region of interest based on the EEG data during the time period, and a device for determining preference using the same.