The present invention is directed to a method for determining a paroxysmal slow waves event (PSWE) so as to determine blood-brain barrier dysfunction (BBBD) or increased risk of developing a neurological disease or disorder in a subject.

The present invention is a human centric lighting (HCL) method with adjustable lighting parameters comprises the following steps: 1) User connects with the cloud through the intelligent communication device and selects the specific spectral recipe that the user wants to achieve a specific emotion from the cloud; 2) The light emitting device is configured to emit light in a specific light field according to the lighting parameters in a specific light field to select the light in a specific light field; 3) After the user performs HCL, when the effect of specific emotion is not reached, the user adjusts the lighting parameters in the selected specific spectral recipe through the intelligent communication device; 4) When the user has achieved the effect of specific emotion after performing HCL, store the corresponding lighting parameters of the adjusted spectral recipe to the cloud.

A system (1) for estimating the brain blood volume and/or brain blood flow and/or depth of anesthesia of a patient, comprises at least one excitation electrode (110E) to be placed on the head (20) of a patient (2) for applying an excitation signal, at least one sensing electrode (110S) to be placed on the head (20) of the patient (2) for sensing a measurement signal caused by the excitation signal, and a processor device (12) for processing said measurement signal (VC) sensed by the at least one sensing electrode (110S) for determining an output indicative of the brain blood volume and/or the brain blood flow. Herein, the processor device (12) is constituted to reduce noise in the measurement signal (VC) by applying a non-linear noise-reduction algorithm. In this way a system for estimating the brain blood volume and/or the brain blood flow of a patient is provided which may lead to an increased accuracy and hence more exact estimates.

A method and an apparatus for classifying an electroencephalogram signal, a device and a computer-readable storage medium. The method includes: obtaining an electroencephalogram signal; performing feature extraction on the electroencephalogram signal to obtain a signal feature corresponding to the electroencephalogram signal; obtaining a difference distribution ratio, the difference distribution ratio being used for representing impacts of difference distributions of different types on distributions of the signal feature and a source domain feature in a feature domain, the source domain feature being a feature corresponding to a source domain electroencephalogram signal; aligning the signal feature with the source domain feature according to the difference distribution ratio to obtain an aligned signal feature; and classifying the aligned signal feature to obtain a motor imagery type corresponding to the electroencephalogram signal.

Apparatus and methods for monitoring electrical activity within the brain of a person (“brainwaves”) employing electrodes or other sensors placed proximate to portions of the body below the head to develop raw signals without physically touching the body and penetrating hair and clothing. Additionally, apparatus and methods for monitoring electrical activity within the brain of a person (“brainwaves”) employing non-contacting sensors placed proximate to portions of the head to develop raw signals. The raw signals are filtered to produce analysis signals including frequency components relevant to brain electrical activity while attenuating unrelated frequency components. The apparatus and methods can be used for biofeedback-based attention training, human performance training, gaming, biometrics, cognitive state detection, and relaxation training. Either wired or wireless signal connections are made to electronic circuitry, typically including a digital computer, for performing signal processing and analysis functions.

A method of analyzing neurophysiological data recorded from a subject is disclosed. The method comprises identifying activity-related features in the data, and parceling the data according to the activity-related features to define a plurality of capsules, each representing a spatiotemporal activity region in the brain. The method further comprises comparing at least some of the defined capsules to at least one reference capsule, and estimating a brain function of the subject based on the comparison.

A method of analyzing neurophysiological data recorded from a subject is disclosed. The method comprises identifying activity-related features in the data, and parceling the data according to the activity-related features to define a plurality of capsules, each representing a spatiotemporal activity region in the brain. The method further comprises comparing at least some of the defined capsules to at least one reference capsule, and estimating a brain function of the subject based on the comparison.

A method for selecting an AI solution for an automated robotic process including receiving at least one functional media including information indicative of brain activity by a human engaged in a task of interest, analyzing the functional media, identifying an activity level in at least one brain region, identifying a brain region parameter and an activity parameter; identifying an action parameter based in part on the brain region parameter or the activity parameter; and selecting a component of the AI solution in part on the brain region parameter, the activity parameter, or the action parameter.

The subject of the invention is a method for calibrating a direct neural interface. The calibration is performed by considering a so-called input calibration tensor, formed on the basis of measured electrophysiological signals and so-called output calibration tensor, formed on the basis of measured output signals. The method comprises the application of a least squares multivariate regression implemented by considering a covariance tensor and a cross-covariance tensor which are established on the basis of input and output calibration tensors corresponding to a current calibration period. The method takes into account covariance and cross-covariance tensors established during an earlier calibration period prior to the current calibration period, these tensors being weighted by a forget factor.

A deep intracranial electrode which comprises a flexible wire, an electrode contact, a connector and a shield sleeve, one end of the flexible wire is connected to the electrode contact, the other end connected to the connector; the shield sleeve sheathes around the flexible wire, a sum of a length of a part of the flexible wire arranged outside the shield sleeve and a length of the shield sleeve being adjustable. When the shield sleeve sheaths around the flexible wire, the length of the flexible wire inside the radio-frequency magnetic field of the magnetic resonance equipment may equal to a sum of the length of the shield sleeve and a length of the flexible wire outside the shield sleeve.