A method and system are described for, based upon a plurality of previously-acquired directional LFP signals measured in a plurality of different directions at a directional sensor lead located in a predetermined region of a patient's brain, determining optimised patient-specific programming parameters for programming a directional stimulation lead with parameters for stimulating the said region. The method comprises a first step of determining, over at least one predetermined frequency range, a power-frequency variation curve of each of the directional LFP signals, a second step of identifying frequency peaks in the power-frequency variation curves, a third step of detecting one of the identified frequency peaks at which a maximum difference in signal power between the directional LFP signals occurs, and a fourth step of calculating a plurality of directional stimulation weighting factors on the basis of the relative signal powers of the directional LFP signals at the detected frequency peak.

A method and system are described for, based upon a plurality of previously-acquired directional LFP signals measured in a plurality of different directions at a directional sensor lead located in a predetermined region of a patient's brain, determining optimised patient-specific programming parameters for programming a directional stimulation lead with parameters for stimulating the said region. The method comprises a first step of determining, over at least one predetermined frequency range, a power-frequency variation curve of each of the directional LFP signals, a second step of identifying frequency peaks in the power-frequency variation curves, a third step of detecting one of the identified frequency peaks at which a maximum difference in signal power between the directional LFP signals occurs, and a fourth step of calculating a plurality of directional stimulation weighting factors on the basis of the relative signal powers of the directional LFP signals at the detected frequency peak.

A neurostimulation system senses electrographic signals from the brain of a patient, extracts features from the electrographic signals, and when the extracted features satisfy certain criteria, detects a neurological event type. A mapping function relates the detected neurological event type to a stimulation parameter subspace and a default stimulation parameter set where the values of the stimulation parameters define an instance of stimulation therapy for the patient. The decision whether to implement a stimulation parameter subspace or a default stimulation parameter set may be informed by integrating other information about a state of the patient. A stimulation parameter subspace or stimulation parameter set may optimized by testing it against various thresholds until certain effectiveness criteria is satisfied. The neurological event type may be one of several electrographic seizure onset types.

A neurostimulation system senses electrographic signals from the brain of a patient, extracts features from the electrographic signals, and when the extracted features satisfy certain criteria, detects a neurological event type. A mapping function relates the detected neurological event type to a stimulation parameter subspace and a default stimulation parameter set where the values of the stimulation parameters define an instance of stimulation therapy for the patient. The decision whether to implement a stimulation parameter subspace or a default stimulation parameter set may be informed by integrating other information about a state of the patient. A stimulation parameter subspace or stimulation parameter set may optimized by testing it against various thresholds until certain effectiveness criteria is satisfied. The neurological event type may be one of several electrographic seizure onset types.

Cortical network structure that mediates response to brain stimulation, and associated systems and methods are disclosed herein. In one embodiment, a method for brain stimulation includes: delivering an input stimulus to an area of the brain, via a cortical implant; in response to delivering the input stimulus, generating neural signals in the brain; and generating a predicted outcome of the input stimulus. The predicted outcome is based on a set of data derived from a model that combines: protocol features that are brain agnostic, and network features that are based on interactions between neural nodes of the brain.

A method and a system for processing an electroencephalogram (EEG) signal are provided. The method for processing the EEG signal includes: performing a spike detection on the EEG signal to obtain a spike distribution waveform, performing an instantaneous frequency oscillation energy analysis on the EEG signal to obtain multiple energy distribution waveforms; performing a complexity analysis on the EEG signal to obtain a complexity change waveform, obtaining a determination result of a specified neural waveform based on the spike distribution waveform, the energy distribution waveforms, and the complexity change waveform, and outputting the determination result.

A method and a system for processing an electroencephalogram (EEG) signal are provided. The method for processing the EEG signal includes: performing a spike detection on the EEG signal to obtain a spike distribution waveform, performing an instantaneous frequency oscillation energy analysis on the EEG signal to obtain multiple energy distribution waveforms; performing a complexity analysis on the EEG signal to obtain a complexity change waveform, obtaining a determination result of a specified neural waveform based on the spike distribution waveform, the energy distribution waveforms, and the complexity change waveform, and outputting the determination result.

An electroencephalogram (EEG) signal classification method and apparatus, a device, a storage medium, and a program product are provided, and relate to the field of signal processing technologies. The method includes: obtaining a first EEG signal; obtaining time-frequency feature maps of at least two electrode signals in the first EEG signal; performing feature extraction based on the time-frequency feature maps of the at least two electrode signals to obtain a first extracted feature map; performing weighting processing based on an attention mechanism on the first extracted feature map to obtain an attention feature map; and obtaining a motor imagery type of the first EEG signal based on the attention feature map.

An electroencephalogram (EEG) signal classification method and apparatus, a device, a storage medium, and a program product are provided, and relate to the field of signal processing technologies. The method includes: obtaining a first EEG signal; obtaining time-frequency feature maps of at least two electrode signals in the first EEG signal; performing feature extraction based on the time-frequency feature maps of the at least two electrode signals to obtain a first extracted feature map; performing weighting processing based on an attention mechanism on the first extracted feature map to obtain an attention feature map; and obtaining a motor imagery type of the first EEG signal based on the attention feature map.

Gamma brain stimulation (around 40 Hz) is performed using light pulses. To perform theta brain stimulation (around 7 Hz) without perceptible flicker, the light source is also strobed at 47 Hz (also within the gamma range). The brain perceives the 40 Hz and a subtraction frequency of 7 Hz (in the theta range). The combined gamma and theta wave stimulation of the brain may be used for preventing or treating brain disease or sleeping disorders. The particular stimulation frequencies and their phases create neuronal gamma-theta coupling in the brain that has been shown to have positive effects on memory, Alzheimer's disease, motor skills, and other functions. Other gamma and theta frequencies, creating gamma-theta coupling in the brain, are also beneficial. The phase of the light pulses is also dynamically controlled using feedback to maximize theta-gamma coupling in the brain.