A method for calculating a risk of developing a medical condition includes receiving results of a biometric questionnaire of a patient, scoring said results and providing the scored results to a machine learning (ML) engine. The ML engine correlates the scored results with new non-scored results to calculate a probability of the patient developing a medical condition. Correlations are provided for various age and sex groups in order to predict longitudinal illness development.

A method for calculating a risk of developing a medical condition includes receiving results of a biometric questionnaire of a patient, scoring said results and providing the scored results to a machine learning (ML) engine. The ML engine correlates the scored results with new non-scored results to calculate a probability of the patient developing a medical condition. Correlations are provided for various age and sex groups in order to predict longitudinal illness development.

A reinforcement learning (RL) based adaptive state observation model usable for implementing a brain machine interface (BMI) is proposed for decoding a brain signal to determine a movement action and controlling a machine to perform the movement action. In the model, the brain signal is processed by a neural network (NN) for applying a nonlinear mapping defined by NN weights to the brain signal to thereby yield a transformed brain signal. The NN learns the nonlinear mapping by RL, allowing the weights to be adaptively and continuously updated to follow nonlinearity and non-stationarity of the brain signal. The transformed brain signal is processed by a Kalman filter (KF) to yield a control signal for controlling the machine to perform the movement action, thereby utilizing the KF to provide smooth generation of the control signal while blocking adverse influence of nonlinearity and non-stationarity of the brain signal to the KF.

A reinforcement learning (RL) based adaptive state observation model usable for implementing a brain machine interface (BMI) is proposed for decoding a brain signal to determine a movement action and controlling a machine to perform the movement action. In the model, the brain signal is processed by a neural network (NN) for applying a nonlinear mapping defined by NN weights to the brain signal to thereby yield a transformed brain signal. The NN learns the nonlinear mapping by RL, allowing the weights to be adaptively and continuously updated to follow nonlinearity and non-stationarity of the brain signal. The transformed brain signal is processed by a Kalman filter (KF) to yield a control signal for controlling the machine to perform the movement action, thereby utilizing the KF to provide smooth generation of the control signal while blocking adverse influence of nonlinearity and non-stationarity of the brain signal to the KF.

Systems and methods for improving behavioral therapies encompassing therapies wherein a perceptual stimulus is administered to a subject or a motor behavior is performed by the subject. Such administration of perceptual stimuli or motor performance is paired with the delivery of vagus nerve stimulation to the subject. The vagus nerve stimulation is timed with the sensory stimulus administration or motor performance in a temporal alignment that maximizes neuroplasticity and performance. Systems for performance of the method and associated software are also disclosed

Methods, systems, and apparatus for signal artifact detection and reduction are provided. The signal artifact may comprise an interference between an electroencephalography (EEG) signal and a magnetic resonance imaging (MRI) signal arising out of simultaneous EEG and MRI treatment.

Methods, systems, and apparatus for signal artifact detection and reduction are provided. The signal artifact may comprise an interference between an electroencephalography (EEG) signal and a magnetic resonance imaging (MRI) signal arising out of simultaneous EEG and MRI treatment.

The invention allows an accurate and automated method and system for determining an intrinsic frequency of an EEG band of a person. Intrinsic frequency values (specifically the intrinsic alpha frequency (IAF)) are used to diagnose mental disorders and detect brain anomalies in a person. At present, these estimates are inaccurate for the population that has EEG with low energy in the EEG band. By combining the EEG recording with a stimulus (e.g., light, sound, touch, etc., or a combination), it is possible to determine the IAF, due to the resonant properties of the brain.

A sleep assessment system includes a blood flow measurement unit and a assessment unit. The blood flow measurement unit acquires first information related to the blood flow of the user. The assessment unit determines the sleep stage of the user based on the first biological information.

Systems and methods to detect seizures using analog circuitry. One example method generally includes obtaining, at a seizure detection system, one or more electroencephalogram (EEG) signals, detecting a plurality of features associated with each of the one or more EEG signals, generating a bitstream indicating a seizure probability associated with each feature of the plurality of features to yield a plurality of bitstreams indicating a plurality of seizure probabilities, and generating a seizure detection output based on the plurality of bitstreams indicating the plurality of seizure probabilities of the plurality of features.