The present application is applicable to the field of computer application technology, and provides methods and devices for detecting driver distraction, including: acquiring the EEG data of the driver; preprocessing the EEG data, and then inputting it into a pre-trained distraction detection model to obtain the distraction detection result of the driver; obtaining the distracted detection model by training a preset convolution-recurrent neural network using EEG sample data and corresponding distracted result label; sending the distraction detection result to an in-vehicle terminal associated with the identity information of the driver, wherein the distraction detection result is used to trigger the in-vehicle terminal to generate driving reminder information according to the distraction detection result. When detecting driver distraction, the accuracy and efficiency are improved, thereby reducing the probability of traffic accidents.

The present application is applicable to the field of computer application technology, and provides methods and devices for detecting driver distraction, including: acquiring the EEG data of the driver; preprocessing the EEG data, and then inputting it into a pre-trained distraction detection model to obtain the distraction detection result of the driver; obtaining the distracted detection model by training a preset convolution-recurrent neural network using EEG sample data and corresponding distracted result label; sending the distraction detection result to an in-vehicle terminal associated with the identity information of the driver, wherein the distraction detection result is used to trigger the in-vehicle terminal to generate driving reminder information according to the distraction detection result. When detecting driver distraction, the accuracy and efficiency are improved, thereby reducing the probability of traffic accidents.

A lower limb rehabilitation system based on augmented reality and a brain computer interface includes a display, a plurality of motion sensors, a brain wave monitor, and an analysis platform. The display is configured to receive and play a virtual scene video to guide a user to perform gait rehabilitation training. The plurality of motion sensors is configured to sense gait data. The brain wave monitor is configured to record an electroencephalogram signal by detecting an electric current change in a brain wave of the user. The analysis platform is configured to compare the gait data with the virtual scene video to determine the accuracy of footsteps of the user and provide feedback. The analysis platform inputs the electroencephalogram signal to a machine learning model to quantify the electroencephalogram signal into an index value representing a lower limb motor function of the user.

Systems and methods for the evaluation of clinical treatment efficacy is disclosed. The systems and methods include protocols for selection of appropriate patients/subjects for the evaluation of a specific clinical treatment. The systems and methods are based on objective measures of brain activity. The clinical treatments include pharmacological compounds in development or existing compounds approved by the appropriate regulatory authority (e.g., U.S. Federal Drug Administration), as well as transcranial magnetic or electric stimulation, including non-invasive approaches as well as grid- or depth-based electrode arrays, as well as behavioral therapies.

Systems and methods for the evaluation of clinical treatment efficacy is disclosed. The systems and methods include protocols for selection of appropriate patients/subjects for the evaluation of a specific clinical treatment. The systems and methods are based on objective measures of brain activity. The clinical treatments include pharmacological compounds in development or existing compounds approved by the appropriate regulatory authority (e.g., U.S. Federal Drug Administration), as well as transcranial magnetic or electric stimulation, including non-invasive approaches as well as grid- or depth-based electrode arrays, as well as behavioral therapies.

The disclosed apparatus, systems and methods relate to predicting, screening, and monitoring for mortality and other negative patient outcomes. Systems and methods may include receiving one or more signals from one or more sensing devices; processing the one or more signals to extract one or more features from the one or more signals; analyzing the one or more features to determine one or more values for each of the one or more features; comparing at least one of the one or more values or a measure based on at least one of the one or more values to a threshold; determining a presence, absence, or likelihood of the subsequent mortality, falls or extended hospital stays for a patient based on the comparison; and outputting an indication of the presence, absence, or likelihood of the subsequent development of poor outcomes or death for the patient.

The present disclosure pertains to a system and method for determining sleep stages during individual sleep cycles based on algorithms and/or parameters that correspond to the individual sleep cycles. The system enables more accurate real-time sleep stage determinations compared to prior art systems. Sleep cycles are detected in real-time based on an electroencephalogram (EEG), and/or by other methods. At the end of a sleep cycle, the system is configured such that the specific algorithms and/or parameters used for the previous sleep cycle to determine sleep stages are replaced by new ones which are specifically adapted for the next sleep cycle.

Described is a system for decoding and validating memory consolidation. During operation, the system receives electroencephalographic (EEG) data while a subject is performing a specific task. Nuisance signals are then removed from the EEG data, resulting in a nuisance free signal. Skill feature vectors are generated from the nuisance free signal using time-invariant feature extraction. A skill classifier can then be trained for the specific task based on the skill feature vectors to generate a subject specific model regarding a memory replay for the specific task. Finally, electrodes in a neural cap are activated based on the memory replay.

Some embodiments relate to computer-implemented methods and systems for fidelity improvement of sleep disorder data. An example method comprises: a server system transmitting query program code defining a plurality of queries to a client device, the plurality of queries relating to sleep disorders, wherein: at least two queries of the plurality of queries relate to a common sleep metric, and at least one other of the plurality of queries relates to a different sleep metric, and the query program code is executable by the client device to permit the client device to transmit at least one response object encoding a response to each of the at least two queries; the server system receiving the at least one response object from the client device and determining the response to each of the at least two queries encoded in the response object; determining a difference between the responses to the at least two queries that relate to a common sleep metric; responsive to determining that the difference is greater than a predetermined difference threshold, the server system transmitting further query program code defining a further query that relates to the common sleep metric, the further query program code being executable by the client device to permit or cause the client device to transmit a further response object encoding a further response to the further query, wherein the further query is selected so that a difference between the further response and one of the at least two queries is less than the predetermined difference threshold.

A system includes an EEG headset, comprising EEG electrodes and InfraRed (IR) transmitters, to be worn by a subject. A recording unit is communicably coupled to the EEG headset and comprises a computing device for receiving EEG feed from the EEG headset, IR coordinates corresponding to the IR transmitters from an IR sensor, and a video recording of the subject from a video camera. The computing device is to identify a position of the subject based on the IR coordinates or the video recording or a combination thereof; cause the computing device to be reoriented based on the position of the subject; and facilitate detection and recording of seizure events based on the EEG feed or video recording or IR coordinates or a combination thereof. The computing device provides a seizure event report based on detection of the seizure events.