A cognitive signal generation unit of a cognitive ability detection device includes a brain signal acquisition unit, a database, an MRCP correction data selection unit, and a calculation unit. The brain signal acquisition unit acquires a brain signal including an event-related potential. The database stores motor readiness potential correction data corresponding to an operation of a subject. The MRCP correction data selection unit selects the motor readiness potential correction data, on the basis of prior information including the type of an action, and outputs the motor readiness potential correction data to the calculation unit. The calculation unit generates the cognitive signal by correcting the brain signal with the motor readiness potential correction data.

A cognitive signal generation unit of a cognitive ability detection device includes a brain signal acquisition unit, a database, an MRCP correction data selection unit, and a calculation unit. The brain signal acquisition unit acquires a brain signal including an event-related potential. The database stores motor readiness potential correction data corresponding to an operation of a subject. The MRCP correction data selection unit selects the motor readiness potential correction data, on the basis of prior information including the type of an action, and outputs the motor readiness potential correction data to the calculation unit. The calculation unit generates the cognitive signal by correcting the brain signal with the motor readiness potential correction data.

A method (100) is implemented by a computing device for helping a particular user use a user interface (UI). Electroencephalography (EEG) data is obtained (102) that indicates brain activity of a particular user during a period in which that user views the UI and/or interprets help information that describes how to use the UI. Based on the EEG data, the computing device selects (104), from among multiple pre-defined cognitive states, the one or more cognitive states that characterize the particular user during the period. The computing device assists (106) the particular user to use the UI by customizing the help information for the particular user based on the one or more selected cognitive states. A complementary computing device and computer program product are also disclosed.

One embodiment provides a method, including: obtaining EEG data from one or more single channel EEG sensor worn by a user; classifying, using a processor, the EEG data as one of nominal and abnormal; and providing an indication associated with a classification of the EEG data. Other embodiments are described and claimed.

This disclosure discloses a method and apparatus for processing a motor MI-EEG signal and a storage medium. The method includes: inputting a source MI-EEG signal belonging to a source domain and a target MI-EEG signal belonging to a target domain to an initial feature extraction model to obtain first source MI features and first target MI features; inputting the first source MI features to an initial classification model to obtain a first classification result outputted by the initial classification model, the first classification result representing an action predicted to be performed in the source MI-EEG signal; and adjusting a model parameter of the initial feature extraction model and/or a model parameter of the initial classification model when a certain condition (set) is met.

This disclosure discloses a method and apparatus for processing a motor MI-EEG signal and a storage medium. The method includes: inputting a source MI-EEG signal belonging to a source domain and a target MI-EEG signal belonging to a target domain to an initial feature extraction model to obtain first source MI features and first target MI features; inputting the first source MI features to an initial classification model to obtain a first classification result outputted by the initial classification model, the first classification result representing an action predicted to be performed in the source MI-EEG signal; and adjusting a model parameter of the initial feature extraction model and/or a model parameter of the initial classification model when a certain condition (set) is met.

Embodiments provide for systems and methods for modulating activity of a live neuronal circuit within the brain of a mammal. In one example, a method comprises applying a focused microwave pulse to a focal spot corresponding to the live neuronal circuit, while simultaneously monitoring a brain image produced by a brain imaging system. In this way, the focused microwave pulse may be used to transiently modulate the function of the live neuronal circuit, as guided by anatomical and functional information provided via the brain imaging system, useful for both research purposes and for treating psychiatric, neurological and neuroendocrine disorders.

Embodiments provide for systems and methods for modulating activity of a live neuronal circuit within the brain of a mammal. In one example, a method comprises applying a focused microwave pulse to a focal spot corresponding to the live neuronal circuit, while simultaneously monitoring a brain image produced by a brain imaging system. In this way, the focused microwave pulse may be used to transiently modulate the function of the live neuronal circuit, as guided by anatomical and functional information provided via the brain imaging system, useful for both research purposes and for treating psychiatric, neurological and neuroendocrine disorders.

Embodiments of portable cranial anatomy injury evaluation systems, apparatus, and methods are described generally herein where the system and apparatus may include multiple signal generation devices including photonic, acoustic, and electrical signals. Other embodiments may be described and claimed.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing EEG source localization. One of the methods includes obtaining brain data comprising: EEG data comprising respective channel data corresponding to each of a plurality of electrodes of an EEG sensor, and fMRI data comprising respective voxel data corresponding to each of a plurality of voxels; identifying, in a three-dimensional coordinate system, a respective location for each electrode; generating, using the respective identified locations of each electrode, data representing a location in the three-dimensional coordinate system of each voxel; determining, for each electrode, a region of interest in the three-dimensional coordinate system; and identifying, for each electrode, one or more corresponding parcellations in the brain of the subject, wherein each parcellation that corresponds to an electrode at least partially overlaps with the region of interest of the electrode.