Described herein are systems and methods for the detection and monitoring of delirium in a subject. Other neurological conditions may also be detected and monitored. The systems may include a data module configured to obtain a plurality of electroencephalography (EEG) signals collected from a subject. The systems may also include a processing module in communication with the data module. The processing module may be configured to process the data to detect and monitor delirium and/or one or more other neurological conditions that the subject is experiencing or likely to experience. The processing module may also generate indications or assessments for delirium and/or for each neurological condition at an individual level, or optionally, between two or more related neurological conditions.

Described herein are systems and methods for the detection and monitoring of delirium in a subject. Other neurological conditions may also be detected and monitored. The systems may include a data module configured to obtain a plurality of electroencephalography (EEG) signals collected from a subject. The systems may also include a processing module in communication with the data module. The processing module may be configured to process the data to detect and monitor delirium and/or one or more other neurological conditions that the subject is experiencing or likely to experience. The processing module may also generate indications or assessments for delirium and/or for each neurological condition at an individual level, or optionally, between two or more related neurological conditions.

A wearable electronic device includes a housing, and an electrode carrier attached to the housing and having a nonplanar surface. The wearable electronic device includes a set of electrodes, including electrodes positioned at different locations on the nonplanar surface. The wearable electronic device includes a sensor circuit and a switching circuit. The switching circuit is operable to electrically connect a number of different subsets of one or more electrodes in the set of electrodes to the sensor circuit.

A wearable electronic device includes a housing, and an electrode carrier attached to the housing and having a nonplanar surface. The wearable electronic device includes a set of electrodes, including electrodes positioned at different locations on the nonplanar surface. The wearable electronic device includes a sensor circuit and a switching circuit. The switching circuit is operable to electrically connect a number of different subsets of one or more electrodes in the set of electrodes to the sensor circuit.

A local wearable brain wave cap device for detection is provided to simultaneously detect brainwave and heart rate variability data of a subject and includes a brain wave detection cap, at least one ear electrode and a transmission unit. The brain wave detection cap includes a wearable device and a plurality of electrode units. The wearable device is suitable for arranging the plurality of electrode units on brain wave positions corresponding to head of a subject. Each of the plurality of electrode units includes an accelerator, a storage unit, an input/output unit and a primary amplifier for detecting a brain wave.

A local wearable brain wave cap device for detection is provided to simultaneously detect brainwave and heart rate variability data of a subject and includes a brain wave detection cap, at least one ear electrode and a transmission unit. The brain wave detection cap includes a wearable device and a plurality of electrode units. The wearable device is suitable for arranging the plurality of electrode units on brain wave positions corresponding to head of a subject. Each of the plurality of electrode units includes an accelerator, a storage unit, an input/output unit and a primary amplifier for detecting a brain wave.

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.

Some systems, devices and methods detailed herein provide a system for use in determining metrics of a subject. The system can provide, as an output, a function-metric value determined based on a defined relationship between physiological measures and a chronological age.

The present invention relates to a syringe-injection-type brain signal measurement and stimulation structure, and a syringe injection method therefor, and provides a structure including a high-performance flexible element capable of minimizing a skull opening when inserted into the brain. Particularly, the present invention comprises: a flexible element, which includes a contact part making contact with a surface of a cortex so as to measure a signal generated in the brain or transmit an external stimulus to the brain, a transmitting/receiving part positioned between a skull and a skin, and a connection part for making a connection between the contact part and the transmitting/receiving part; and an integrated circuit connected to the transmitting/receiving part so as to transmit/receive a signal.