Methods and systems for improved removal of deep brain stimulation artifacts from electrical measurements of brain activity. In one embodiment, a method is provided that includes: receiving, by a computing device having a processor, waveform data caused by a deep brain stimulation of a patient-specific area of interest; determining, by the computing device, a stimulation period relative to a sampling rate; identifying, by the computing device and based on the waveform data and the stimulation period, a stimulation artifact; subtracting, by the computing device, the identified stimulation artifact from the received waveform data; and generating, in real-time, a filtered waveform data indicating an absence of the stimulation artifact.

A method for classification processing of an electrophysiological signal, including acquiring an electrophysiological signal collected by an acquisition device, and acquiring a channel association feature corresponding to the acquisition device. The channel association feature indicates spatial locations of multiple acquisition channels of the acquisition device, each of the multiple acquisition channels collecting the electrophysiological signal at a respective spatial location. The method further includes extracting a time feature corresponding to the electrophysiological signal, and generating an embedded feature based on the channel association feature and the time feature, and extracting a spatial feature corresponding to the embedded feature, and obtaining a classification result corresponding to the electrophysiological signal based on the spatial feature.

An electroencephalogram signal classification method includes: obtaining a first electroencephalogram signal; processing the first electroencephalogram signal using at least two electroencephalogram signal classification models to obtain respective motor imagery probability distributions from the at least two electroencephalogram signal classification models; and determining a motor imagery type of the first electroencephalogram signal based on the motor imagery probability distributions. A plurality of electroencephalogram signal classification models is respectively trained using an augmented data set obtained through augmentation. During prediction, by combining the plurality of electroencephalogram signal classification models, the accuracy of classifying an electroencephalogram signal to determine a motor imagery type may be improved, when using a model trained with a relatively small number of training samples.

An electroencephalogram signal classification method includes: obtaining a first electroencephalogram signal; processing the first electroencephalogram signal using at least two electroencephalogram signal classification models to obtain respective motor imagery probability distributions from the at least two electroencephalogram signal classification models; and determining a motor imagery type of the first electroencephalogram signal based on the motor imagery probability distributions. A plurality of electroencephalogram signal classification models is respectively trained using an augmented data set obtained through augmentation. During prediction, by combining the plurality of electroencephalogram signal classification models, the accuracy of classifying an electroencephalogram signal to determine a motor imagery type may be improved, when using a model trained with a relatively small number of training samples.

A computer-implemented method and system includes accessing neurophysiological and neurovascular data recorded during sleep. A function mapping is executed from said neurophysiological and neurovascular data to a target that is one of a glymphatic flow marker, a molecular analysis marker of neurodegeneration, or a neuroimaging marker of neurodegeneration. A target prediction model is output based on the function mapping. The target prediction model can receive new neurophysiological and neurovascular data and output a predicted marker of neurodegeneration.

A computer-implemented method and system includes accessing neurophysiological and neurovascular data recorded during sleep. A function mapping is executed from said neurophysiological and neurovascular data to a target that is one of a glymphatic flow marker, a molecular analysis marker of neurodegeneration, or a neuroimaging marker of neurodegeneration. A target prediction model is output based on the function mapping. The target prediction model can receive new neurophysiological and neurovascular data and output a predicted marker of neurodegeneration.

A computer-implemented method and system includes accessing neurophysiological and neurovascular data recorded during sleep. A function mapping is executed from said neurophysiological and neurovascular data to a target that is one of a glymphatic flow marker, a molecular analysis marker of neurodegeneration, or a neuroimaging marker of neurodegeneration. A target prediction model is output based on the function mapping. The target prediction model can receive new neurophysiological and neurovascular data and output a predicted marker of neurodegeneration.

A computer-implemented method and system includes accessing neurophysiological and neurovascular data recorded during sleep. A function mapping is executed from said neurophysiological and neurovascular data to a target that is one of a glymphatic flow marker, a molecular analysis marker of neurodegeneration, or a neuroimaging marker of neurodegeneration. A target prediction model is output based on the function mapping. The target prediction model can receive new neurophysiological and neurovascular data and output a predicted marker of neurodegeneration.

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 illustrative system includes a stimulation device configured to apply stimulation to a recipient, a sensing device configured to detect a physiological condition of the recipient, and a processing unit communicatively coupled to the stimulation device and the sensing device. The processing unit determines a stimulation strategy that is customized to the recipient and includes stimulation frames and stimulation gaps. The processing unit then directs the stimulation device to apply the stimulation to the recipient in accordance with the stimulation strategy by applying the stimulation only during time that corresponds to the stimulation frames. The processing unit also directs the sensing device to detect the physiological condition of the recipient in accordance with the stimulation strategy by detecting only during time that corresponds to the stimulation gaps. Based on the detected physiological condition, the processing unit performs an action. Corresponding systems, methods, and apparatuses are also disclosed.