A device and method for determining the severity of sleep apnea using electroencephalography and electromyography. The device includes a headgear having a head pan sized to cover the head of a patient, at least at the locations where the measuring points C3 and C4 of the electroencephalography are situated, and a chin part, and wherein the head part has two electrodes for sensing EEG-signals of the electroencephalography at the electroencephalography points C3 and C4, and the chin part has at least one electrode for sensing the EMG-signal of the electromyography in the chin.

Disclosed is a neurofeedback device, including a body attached to a user's body and configured to provide electrical stimulation to a vagus nerve region, wherein the body includes: a frame provided in a symmetrical and elliptical shape, wherein one side and another side in a longitudinal direction are concavely recessed toward a center, and attached to a user's neck; a vagus nerve stimulator located on a back surface of the frame, provided to include a plurality of electrodes for providing electrical stimulation to the vagus nerve region, and attached to skin directly above the vagus nerve region located next to carotid artery of the user; a heart rate sensor located at a center of left and right symmetry of the frame on a back surface of the frame and configured to detect heart rates of the user; a manipulator located in front of the frame and configured to receive a user's command; and a plurality of connection ports formed on a side surface of the frame to transmit and receive signals, wherein the plural electrodes of the vagus nerve stimulator are provided one by one on left and right sides in the symmetrical structure of the frame and, when the frame is attached to the user's skin, are disposed perpendicular to a direction of the vagus nerve.

A system for providing real-time biological feedback training through remote transmission is provided and includes a local brain wave collection device, a docking device, and a dongle. The local brain wave collection device is used to detect a brain wave and a heart rate variability data of a subject. The docking device communicates with the local brain wave collection device remotely to connect a remote cloud system to compare the brain wave and the heart rate variability data with a brain wave database to generate a comparison result, and according to the comparison result, the system provides the subject a feedback training interface.

A system for providing real-time biological feedback training through remote transmission is provided and includes a local brain wave collection device, a docking device, and a dongle. The local brain wave collection device is used to detect a brain wave and a heart rate variability data of a subject. The docking device communicates with the local brain wave collection device remotely to connect a remote cloud system to compare the brain wave and the heart rate variability data with a brain wave database to generate a comparison result, and according to the comparison result, the system provides the subject a feedback training interface.

Disclosed is a natural movement electroencephalogram (EEG) recognition method based on source localization and a brain network, which includes the following steps: (1) performing multi-channel EEG measurement for natural movements; (2) preprocessing acquired EEG signals, and extracting the movement-related cortical potential (MRCP), and θ, α, β, and γ rhythms; (3) determining a lead field matrix of the signals, calculating initial solutions of sources by means of L1 regularization constraint, and then performing iteration by means of successive over-relaxation to obtain a source localization result; (4) by using the sources as nodes, calculating PLV between each pair of sources at each time point by means of short-time sliding window, and establishing brain networks; and (5) calculating a network adjacency matrix at each time point and five brain network indicators, introducing these features into a classifier for training and testing, and conducting a statistical test for the brain network indicators. The present disclosure makes improvements to the conventional source localization method by using the T-wMNE algorithm in combination with successive over-relaxation, and establishes brain networks by using the sources as nodes, thus improving the EEG decoding accuracy for natural movements and revealing the neural mechanism of the human body.

Disclosed is a natural movement electroencephalogram (EEG) recognition method based on source localization and a brain network, which includes the following steps: (1) performing multi-channel EEG measurement for natural movements; (2) preprocessing acquired EEG signals, and extracting the movement-related cortical potential (MRCP), and θ, α, β, and γ rhythms; (3) determining a lead field matrix of the signals, calculating initial solutions of sources by means of L1 regularization constraint, and then performing iteration by means of successive over-relaxation to obtain a source localization result; (4) by using the sources as nodes, calculating PLV between each pair of sources at each time point by means of short-time sliding window, and establishing brain networks; and (5) calculating a network adjacency matrix at each time point and five brain network indicators, introducing these features into a classifier for training and testing, and conducting a statistical test for the brain network indicators. The present disclosure makes improvements to the conventional source localization method by using the T-wMNE algorithm in combination with successive over-relaxation, and establishes brain networks by using the sources as nodes, thus improving the EEG decoding accuracy for natural movements and revealing the neural mechanism of the human body.

A physiological electric signal classification processing method includes: performing data alignment on an initial physiological electric signal corresponding to a target user identity based on target signal spatial information corresponding to the target user identify to obtain a target physiological electric signal; performing spatial feature extraction on the target physiological electric signal based on a target spatial filtering matrix to obtain a target spatial feature, the target spatial filtering matrix being generated based on target training physiological electric signals corresponding to a plurality of training user identities respectively and training labels corresponding to the target training physiological electric signals, the target training physiological electric signals being obtained by performing data alignment on initial training physiological electric signals based on training signal spatial information corresponding to the training user identities; and obtaining a classification result corresponding to the initial physiological electric signal based on the target spatial feature.

Devices, systems, and techniques are disclosed for managing electrical stimulation therapy and/or sensing of physiological signals such as brain signals. For example, a system may assist a clinician in identifying one or more electrode combinations for sensing a brain signal. In another example, a user interface may display brain signal information and values of a stimulation parameter at least partially defining electrical stimulation delivered to a patient when the brain signal information was sensed.

Devices, systems, and techniques are disclosed for managing electrical stimulation therapy and/or sensing of physiological signals such as brain signals. For example, a system may assist a clinician in identifying one or more electrode combinations for sensing a brain signal. In another example, a user interface may display brain signal information and values of a stimulation parameter at least partially defining electrical stimulation delivered to a patient when the brain signal information was sensed.

An electroencephalogram signal processing apparatus includes an interface and one or more processors. The interface receives, from each of a plurality of electrodes attached to a head of a subject, an electroencephalogram signal corresponding to a change over time in a brain activity potential of the subject. The processor obtains a value of an electroencephalogram parameter for each of the plurality of electrodes by processing the electroencephalogram signal, and the processor outputs data for plotting the value on a radar chart. A plurality of regions to each of which at least one electrode is attached are set in the head. Each of a plurality of coordinate axes provided in the radar chart is associated with a corresponding one of the plurality of regions.