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.

A deep intracranial electrode which comprises a conducting wires, an electrode contact, a connector and a nonelastic sleeve is provided, one end of the conducting wires connected to the electrode contact, the other end connected to the connector; the nonelastic sleeve sheathes around the conducting wires, and one end of the nonelastic sleeve is capable of being connected to the connector, the other end connected to the fixing nut which is fixed to a skull. When the deep intracranial electrode is under a pulling force, the fixing nut may avoid the nonelastic sleeve from moving, thereby avoiding the deep intracranial electrode from being pulled out.

A method for manufacturing a deep intracranial electrode, a bending-resistant deep intracranial electrode and an electroencephalograph is disclosed. The method comprises the following steps: manufacturing a support rod of the deep intracranial electrode with a shape memory alloy material, the shape memory alloy having a preset phase-transformation temperature; subjecting the support rod in a straight state to an annealing process such that the support rod memorizes a straight shape.

The purpose of the present invention is to provide a technique whereby multiple projection targets are efficiently identified from multiple neurons in multiple brain areas with the use of multis-point light stimulation. An acquisition unit 52 acquires spike signals generated from multiple neurons existing in the vicinity of two or more recording sites. A stimulation control unit 51 selects one projection target candidate from two or more candidates in accordance with a definite system on the basis of the spike signals and then determines irradiation timing of light stimulation. Upon the light stimulation, a management unit 53 acquires the spike signals in all of the recording sites within a definite period of time before or after the light stimulation, while dividing the spike signals into anti responses and collision responses. An anti response management unit 81 acquires and manages information relating to the anti responses. A collision response management unit 82 acquires and manages information relating to the collision responses. A priority control section 54 corrects and determines priority depending on the anti response information and the collision response information.

A technical solution is described for implementing a computer-executed signal processing algorithm to search for time domain segments of a recorded electroencephalogram (EEG) that are highly correlated, either positively or negatively, to one or more, individual, synthetically generated, single-period single-frequency (SPSF) sinusoids. The SPSFs are motivated by the combined concepts of individual Striatal Beat Frequencies (SBF) used to model cortical neuron activity, Frequency Domain Reflectometry used to study Voltage Standing Wave Ratios (VSWR), Geophysics Seismograms, and ghosting effects of multipath passing through periodic sinusoids. This computationally intense approach is only recently realizable through the advent of high performance computing. The SPSF approach, since it is not constrained to the error-laden one-window-fits-all approach of the Time-Frequency Spectrogram, offer's a more detailed basis to assess, and truer visualization of, the health of brain's electrical activities. This approach is a push-back against the Uncertainty Principal.

A computer-implemented method for learning a model to predict movements of a person in bed is presented. The method includes receiving first data from a plurality of first sensors installed on a bed patient support apparatus, receiving second data from a plurality of second sensors installed on the person, and learning a model to predict the second data based on the first data by assuming a sensing range of motion intensity by the plurality of first sensors is greater than a sensing range of motion intensity by the plurality of second sensors.

A machine-implemented method, computing device, and at least one non-transitory computer-readable medium are provided. A dynamical network model is parameterized by state transition matrices based on monitored interictal brain data. A node influence-to network score for each respective node is calculated indicating how influential the respective node is. An influenced-by score is calculated for the each respective node indicating an amount by which the respective node is influenced by the nodes. A score is calculated for the each respective node based on a sink index, a source influence index, and a sink connectivity index. Nodes that are in the epileptogenic zone are determined based on the calculated score for each of the nodes. An indication of the nodes in the epileptogenic zone is provided.

The methods and systems described herein provide a novel approach for detecting and/or predicting an epileptic event in a subject with or without performing an EEG on the subject. Methods of identifying and treating epilepsy in a subject are also provided herein. A broad regression analysis using a lower order statistical analysis and/or a higher order statistical analysis of one or more oculometric parameters in a time series can be used to determine that the distribution of an oculometric parameter over time and/or the related dependencies of frequencies of two or more oculometric parameters over time correlate with an epileptic event. The methods and systems described herein may also be applied to one or more facial biometrics of the subject.

A classification system of epileptic EEG signals based on non-linear dynamics features includes a preprocessing module, a feature extraction module, a feature sorting module, a feature selection module and a classification module: the preprocessing module uses discrete wavelet transformation to remove noise in the EEG data and obtain effective EEG signal data without noise; the feature extraction module uses multiple entropy algorithms to calculate the non-linear dynamics features of each EEG signal; the feature sorting module sorts features with analysis of variance; the feature selection module selects the optimal feature subset that has the most significant impact on the accuracy of the model uses a uses a forward sequential feature selection algorithm; the classification module transforms the judgment of EEG during the period of epilepsy and EEG during the interval period of epilepsy into a binary classification problem by use of a least squares support vector machine algorithm.

In some aspects, the described systems and methods provide for a method comprising transmitting to a brain of a patient, with at least one transducer, acoustic signals. The method further comprises receiving from the brain, with the at least one transducer, data acquired from the brain including information related to standing waves, distribution of acoustic modes, frequency response, and/or impulse/transient response. The method further comprises determining, from the acquired data, intracranial pressure of the person.