The present technology relates to a control device and a control method capable of providing a more convenient electroencephalogram input user interface.

Provided is a control device including a detection unit configured to perform detection of a brain wave included in a measured biometric signal of a user and detection of a user action based on information other than the brain wave included in the biometric signal, and a processing unit configured to perform a predetermined process based on the brain wave in a case where the user action is a predetermined action. For example, the present technology can be applied to a measurement device capable of measuring a brain wave signal.

The following describes a device for generating speech from human intent. The device comprises at least one sensor for measuring brain signals, a processor, and a wearable portion. The following also describes a method of generating speech from human intent comprising two phases. The first phase is the training phase, which encompasses methods by which data is collected, source localization, and training of the deep learning modules. The second phase comprises sensing brain and/or muscle signals, processing the brain and/or muscle signals at the deep learning modules and converting said signals into an output. The output may be text or automatically generated speech.

A method for detecting the presence of slowing patterns in an EEG sample comprising a plurality of channels of EEG signals, each channel comprising one or more segments, the method comprising: obtaining a first classifier that is trained to classify EEG samples as containing abnormal slow waves or not; performing a sequence of artifact removal processes on the EEG sample to generate a preprocessed EEG sample; extracting a first feature set from the preprocessed EEG sample; and passing the first feature set to the first classifier to predict whether the EEG sample contains abnormal slow waves or not; wherein the sequence of artifact removal processes comprises removal of one or more ocular artifacts and removal of one or more electrode artifacts.

A method for detecting the presence of slowing patterns in an EEG sample comprising a plurality of channels of EEG signals, each channel comprising one or more segments, the method comprising: obtaining a first classifier that is trained to classify EEG samples as containing abnormal slow waves or not; performing a sequence of artifact removal processes on the EEG sample to generate a preprocessed EEG sample; extracting a first feature set from the preprocessed EEG sample; and passing the first feature set to the first classifier to predict whether the EEG sample contains abnormal slow waves or not; wherein the sequence of artifact removal processes comprises removal of one or more ocular artifacts and removal of one or more electrode artifacts.

The present disclosure provides a method for providing information on a major depressive disorder, which is implemented by a processor, and a device using the same, the method comprising receiving brain wave data of an individual; generating brain activity data based on the brain wave data; and determining whether the individual's major depressive disorder is present by using a classification model configured to classify the major depressive disorder based on the brain activity data.

The present disclosure provides a method for providing information on a major depressive disorder, which is implemented by a processor, and a device using the same, the method comprising receiving brain wave data of an individual; generating brain activity data based on the brain wave data; and determining whether the individual's major depressive disorder is present by using a classification model configured to classify the major depressive disorder based on the brain activity data.

A system may access electroencephalography (EEG) data of a subject, the EEG data including responses of the subject to an activation procedure. A system may analyze the EEG data including a plurality of epochs corresponding to the responses to identify first peaks, second peaks, and third peaks in one or more epochs. A system may determine values of a parameter in the plurality of epochs, the parameter being a characteristic of the first peak, the second peak, and/or the third peak. A system may generate a visual representation of the EEG data of the subject, the visual representation including an illustration of the first peak, the second peak and/or the third peak of a representative epoch or a heatmap compiled from the plurality of epochs, and the visual representation further including a graphical representation of the parameter that is presented as evidence of whether the subject is cognitively impaired.

A system may access electroencephalography (EEG) data of a subject, the EEG data including responses of the subject to an activation procedure. A system may analyze the EEG data including a plurality of epochs corresponding to the responses to identify first peaks, second peaks, and third peaks in one or more epochs. A system may determine values of a parameter in the plurality of epochs, the parameter being a characteristic of the first peak, the second peak, and/or the third peak. A system may generate a visual representation of the EEG data of the subject, the visual representation including an illustration of the first peak, the second peak and/or the third peak of a representative epoch or a heatmap compiled from the plurality of epochs, and the visual representation further including a graphical representation of the parameter that is presented as evidence of whether the subject is cognitively impaired.

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.

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.