A method of classifying a mental state of a user using a computing device, which includes a microphone, a camera, a display, a wireless communication unit, and a processor, and a mental state classification server is provided. The method comprises: by the microphone, detecting ambient noise of the user; by the camera, generating an image by photographing a face of the user; by the processor, checking whether each of ambient noise of the user, ambient brightness of the face of the user obtained from the image, and a face position of the user obtained from the image is suitable for a heart rate variability measurement environment; by the processor, controlling to display on the display images indicating whether each of the ambient noise, the ambient brightness, and the face position is suitable for the heart rate variability.

The present disclosure relates to establishing bidirectional communication between a brain wave processing device and a vehicle to control at least one vehicle component of the vehicle. For this purpose, a brain-computer communication channel is provided between the brain wave processing device and the respective vehicle component. Subsequently, a control signal is determined as a function of a brain wave of the operator of the brain wave processing device and transmitted via the brain-computer communication channel to adapt at least one operating parameter of the respective vehicle component. This causes a change in the operating state of the respective vehicle component. Depending on this, an output signal is generated and is assigned to the change in the operating state of the vehicle component. This output signal is transmitted back to the brain wave processing device via the brain-computer communication channel and is output to the operator by means of an output unit of the brain wave processing device.

The present invention provides methods and systems determining the behavioural index of a user. The present invention involves analyzing the user's behaviour and linguistic parameters using a smart wearable device. Basis the analysis, user's behavioural index is suitably determined, modified and informed to the user.

A method and system is provided for determining a discrimination index in a subject that may be suffering from or at risk for a stress-induced psychiatric disorder. The discrimination index may be equal to a ratio of a subject's cued fear response and non-cued fear response measured during a fear-potentiated startle (FPS) paradigm. Such a value may allow a physician or researcher to quantify how well a subject discriminates between signaled (cued) fear and un-signaled (non-cued) fear, which may be a biomarker for psychiatric disorders like post-traumatic stress disorder, panic disorder, phobias, and/or generalized anxiety disorder. The determined discrimination index may provide a standardized way of diagnosing and evaluating mental illnesses, more uniform treatment of patients, and/or more precise monitoring and evaluation of treatment efficacy.

Provided is a method of evaluating emotional intelligence, the method including evaluating an accuracy of emotional recognition of subjects for the emotional image stimuli, evaluating an accuracy of interpersonal emotional recognition of subjects for a facial image stimulus with a certain emotion, a classification step of presenting a video stimulus of a certain emotion to selected subjects with high accuracy, extracting a heart rate variability (HRV) the subjects, forming a classification model by classification of the video stimulus and machine learning using the HRV through a model generating unit, and evaluating an emotional intelligence of the subject exposed to the image stimulus using the classification model.

Aspects of the subject disclosure may include, for example, receiving information about a task to be completed by a user, receiving information about the user and receiving information about a physical environment of the user. The subject disclosure may further include creating one or more immersion objects based on the information about the task, the information about the user and the information about the physical environment, creating an immersive environment including the one or more immersive objects and at least a portion of the physical environment of the user, and communicating to an extended reality (XR) device of the user information about the immersive environment to create an immersive experience for completion of the task by the user. Other embodiments are disclosed.

Systems and methods for detecting complex networks in MRI image data in accordance with embodiments of the invention are illustrated. One embodiment includes an image processing system, including a processor, a display device connected to the processor, an image capture device connected to the processor, and a memory connected to the processor, the memory containing an image processing application, wherein the image processing application directs the processor to obtain a time-series sequence of image data from the image capture device, identify complex networks within the time-series sequence of image data, and provide the identified complex networks using the display device.

Methods, systems and wearable devices for real-time mental stress assessment are provided. The methods and systems employ deep learning using a Gated Recurrent Unit (GRU) gating mechanism in a recurrent neural network with a sliding window approach applied to raw EEG data.

The present invention relates to a cognitive function test server, including a communication interface, a memory; and a processor which is operably connected to the communication interface and the memory, and the processor is configured to provide a first sequence to acquire brainwave data of a user in a resting state by means of an HMD device, acquire baseline brainwave data of the user based on the first sequence, provide at least one second sequence related to a cognitive function by means of the HMD device, acquire input data and activated brainwave data based on the second sequence from the HMD device and an input device connected thereto, and generate a cognitive evaluation result of the user based on at least one of the reference brainwave data, the activated brainwave data, and the input data of the user.

Described herein are assistant robots that anticipate needs of one or more people (or animals). The assistant robots may recognize a current activity, knowledge of the person's routines, and contextual information. As such, the assistant robots can provide or offer to provide appropriate robotic assistance. The assistant robots can learn users' habits or be provided with knowledge regarding humans in its environment. The assistant robots develop a schedule and contextual understanding of the persons' behavior and needs. The assistant robots may interact, understand, and communicate with people before, during, or after providing assistance. The robot can combine gesture, clothing, emotional aspect, time, pose recognition, action recognition, and other observational data to understand people's medical condition, current activity, and future intended activities and intents.