13 results about "Eeg recording" patented technology

An auricular electroencephalogram (EEG) monitoring system may include an EEG recording module having a plurality of EEG sensor electrodes, configured to be coupled to a wearer's ear, and a processing unit configured to analyze EEG data recorded by the EEG recording module to detect presence or cessation of neuropsychiatric disorders of the wearer. An automatic detection-remedy system may include an auricular electroencephalogram (EEG) monitoring system and a transcutaneous auricular vagus nerve stimulation (taVNS) unit having a stimulating electrode in contact with vagus innervated auricular skin of the wearer's ear. When the presence of EEG signals suggestive of the neuropsychiatric disorder is detected by the processing unit, the processing unit is configured to immediately send signals to the taVNS unit to automatically start sending pre-determined electric stimuli to the vagus innervated auricular skin of the wearer's ear.

An integrated switch matrix for a medical device system used for long-term monitoring of electroencephalogram (EEG) signals and mapping of the brain through cortical stimulation is configured to switch functions of various electrodes associated with the system in response to user needs. The programmable switch matrix is integrated in an EEG recording device and allows for connecting any patient electrode(s) to a ground circuit, connecting any patient electrode to a common reference, connecting a selected common reference to any or all recording device(s) in the system and, connecting any patient electrode(s) to anode and / or cathode outputs of a neurostimulator for multi-contact cortical stimulation.

A brainstate estimate of a subject's brain is generated, including by detecting a peak shape in an EEG signal, measuring features from detected peaks, and classifying the EEG signal based at least in part on the locations of points in a multidimensional space. This includes obtaining a plurality of totally disjoint regions that the multidimensional space is divided up into and a plurality of probabilities associated with the plurality of totally disjoint regions. A combined value is determined based at least in part on the locations of the plurality of labeled points within the multidimensional space. The EEG signal is classified based at least in part on the combined value. The brainstate estimate is generated based at least in part on the classification of the EEG signal.

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.

The claimed technical solution relates generally to the field of computing, and more particularly to a device for assessing rhythmic brain activity parameters in real time on a stationary multi-channel electroencephalograph. The technical result consists in faster and more accurate real-time processing of an electroencephalogram (EEG) signal to assess rhythmic brain activity parameters. The claimed technical result is achieved by means of a device for assessing rhythmic brain activity parameters in real time on a stationary multi-channel electroencephalograph, said device comprising: a unit for initially setting and calibrating a brain parameter assessment algorithm, which is designed to be capable of calculating parameters of a rhythmic signal and noise signal model and adjusting the weighting coefficients of a spatial filter on the basis of a fragment of an EEG recording; and a digital multi-channel electroencephalograph containing: a unit for preamplifying and digitizing a calibrated EEG signal, which is designed to be capable of recording low-amplitude brain potentials and also preamplifying and digitizing same; and a signal microprocessor designed to be capable of assessing rhythmic brain activity parameters in real time.

A multi-use electroencephalogram (EEG) system and method consists of a strobe where the strobe is adjustable to produce single and multiple strobe flashes. An EEG device connected with the strobe configured for time-locking an EEG recording with the strobe where the EEG device measures patient flash visual evoked potential (FVEP) response to the strobe flashes and a controller connected with the strobe and the EEG device where the controller operates the strobe to produce one single strobe flash condition followed by a double strobe flash condition employing various interstimulus intervals in a complete counterbalanced order.

An auricular electroencephalogram (EEG) monitoring system may include an EEG recording module having a plurality of EEG sensor electrodes, configured to be coupled to a wearer's ear, and a processing unit configured to analyze EEG data recorded by the EEG recording module to detect presence or cessation of neuropsychiatric disorders of the wearer. An automatic detection-remedy system may include an auricular electroencephalogram (EEG) monitoring system and a transcutaneous auricular vagus nerve stimulation (taVNS) unit having a stimulating electrode in contact with vagus innervated auricular skin of the wearer's ear. When the presence of EEG signals suggestive of the neuropsychiatric disorder is detected by the processing unit, the processing unit is configured to immediately send signals to the taVNS unit to automatically start sending pre-determined electric stimuli to the vagus innervated auricular skin of the wearer's ear.

The invention discloses an electrode cap and a system. The electrode cap comprises a supporting body; the electrode base comprises a hollow structure defined by side walls, and the electrode base is fixed to the supporting body; the electroencephalogram recording electrode is arranged on the side wall of the electrode base, and the sensing end of the electroencephalogram recording electrode is arranged in the hollow structure; the stimulating electrode base is detachably connected with the electrode base and arranged in the hollow structure, and a stimulating electrode is integrated at the bottom of the stimulating electrode base; a containing space is arranged in the stimulating electrode base, and a conducting medium injection hole communicated with the containing space is further formed in the top of the stimulating electrode base. According to the invention, electroencephalogram acquisition and electrical stimulation can be carried out at the same point and at the same time, and closed-loop stimulation is realized.

A head stage designed for non-surgical placement of EEG electrodes in small animals is disclosed. The head stage is connected to thin barbed pin electrodes of multiple lengths, such that they can be placed into the brain at the chosen depth. A miniature device comprising a wireless transmitter or data logger can be attached using magnets or connectors to the stage. When the head stage is placed on the head of a small animal and pressure is applied on the stage, the set of pin electrodes penetrate the skull, such that the stage becomes firmly fixed to the skull. Further, the device includes electrodes on its body that eliminate the need of wires for EEG recordings. When the capsule including sharp electrodes is pressed toward the skull, the electrodes penetrate the bone and fixes the capsule onto the skull for recording biopotential signals from the brain.

A method includes accessing EEG data obtained from each of a first plurality of subjects and from each of a second plurality of subjects, and training a machine learning model to identify a brain state associated with mild traumatic brain injury (mTBI) using the EEG data for each of the first plurality of subjects. The first plurality of subjects are known to have mTBI, and the second plurality of subjects are known to be mTBI unafflicted. The EEG data for each subject includes EEG data for the subject at rest and sensory evoked response EEG data for the subject. A system for identifying a brain state of a subject includes a portable odorant generation and EEG recording system, and at least one processor configured to use a trained machine learning model to analyze EEG data obtained by the EEG recording system to identify the brain state of the subject.

Systems and methods for interpreting a user's brain wave data from EEGs using machine learning algorithms are described. In one example, a brain activity interpretation system classifies the EEG recording into five separate component signals, representing the five categories of brain waves (alpha, beta, theta, delta, gamma). In one example, the most dominant component signal is analyzed to determine whether the amplitude is higher or lower than an optimal range within the bandwidth for that brain wave. The system can then provide intelligent recommendations to the user for beneficial action(s) to help improve their everyday functioning and promote better regulation of their brain states. The system can help the user become more aware of their mental state and how to improve their mental state. EEG data is highly complex and unsuitable for immediate human comprehension, thus, the disclosed systems and methods improve the speed and accuracy of brain wave analysis.

An integrated switch matrix for a medical device system used for long-term monitoring of electroencephalogram (EEG) signals and mapping of the brain through cortical stimulation is configured to switch functions of various electrodes associated with the system in response to user needs. The programmable switch matrix is integrated in an EEG recording device and allows for connecting any patient electrode(s) to a ground circuit, connecting any patient electrode to a common reference, connecting a selected common reference to any or all recording device(s) in the system and, connecting any patient electrode(s) to anode and / or cathode outputs of a neurostimulator for multi-contact cortical stimulation.