MONITORING BASED ON CONTINUOUS INTRACRANIAL EEG ACTIVITY

1 . A method comprising:

receiving electroencephalogram (EEG) data from at least one electrode implanted in a brain of a subject, wherein the subject has a catheter implanted in the brain, wherein at least a portion of the catheter is implanted in a cerebrospinal fluid (CSF)-containing space of the brain; and

determining a brain state or predicting a future brain state of the subject based on the EEG data using an artificial intelligence (AI) model.

2 . The method of claim 1 , wherein the brain state or predicted brain state is associated with a disease of the brain of the subject.

3 . The method of claim 1 , wherein the catheter comprises an external ventricular drainage catheter or a ventriculoperitoneal shunt.

4 . The method of claim 1 , wherein the catheter comprises a therapeutic agent delivery catheter.

5 . The method of claim 1 , wherein the at least one electrode is implanted in white matter of the brain.

6 . The method of claim 1 , wherein determining the brain state or predicting the future brain state comprising determining a brain state associated with a disease of the brain or predicting a future brain state associated with a disease of the brain.

7 . The method of claim 1 , wherein determining the brain state or predicting the future brain state comprises determining a psychiatric brain state or predicting a future psychiatric brain state.

8 . The method of claim 1 , comprising administering treatment to the subject in response to the current or predicted brain state in the brain state data.

9 . The method of claim 8 , wherein treatment is administered before onset of a predicted brain state of the subject.

10 . The method of claim 8 , wherein administering treatment comprises providing an alert to the subject, a healthcare provider, or a caretaker.

11 . The method of claim 8 , wherein administering treatment comprises infusing a drug through the catheter to the CSF-containing space of the brain of the subject.

12 . The method of claim 8 , wherein the treatment comprises a treatment for one or more of the following: epilepsy, bipolar disorder, depressive disorder spectrum, anxiety disorder spectrum including post-traumatic stress disorder (PTSD), cognitive disorder spectrum, memory disorder spectrum, processing speed disorder spectrum subarachnoid hemorrhage, intracerebral hemorrhage, subdural hemorrhage, extradural hemorrhage, obstructive hydrocephalus, nervous system cancer (such as secondary malignant neoplasm of the brain, spinal cord, or other parts of the nervous system; malignant neoplasm of the cerebellum nos; malignant lymphoma of an unspecified site, extranodal site, or solid organ site; and the like), cerebral artery occlusion, unspecified cerebral infarction, closed fracture of the base of the skull (which may be associated with one or more of subarachnoid hemorrhage, subdural hemorrhage, extradural hemorrhage, and loss of consciousness), infection and inflammatory reaction (such as due to nervous system device, implant or graft), mechanical complication of a nervous system device, implant or graft), traumatic brain injury, congenital hydrocephalus, and aneurysms.

13 . The method of claim 1 , wherein determining the brain state or predicted future brain state comprises receiving EEG data from two or more electrodes.

14 . The method of claim 1 , comprising receiving other data than the EEG data and wherein the other data is used in determining the brain state or predicting the future brain state.

15 . The method of claim 14 , wherein the other data comprises one or more of the following types of data corresponding to the subject: activity, motion, heart rate, visual stimuli, audio or video recordings, and skin conductance.

16 . The method of claim 1 , comprising receiving labeling data to label a brain state relative to the EEG data and training the AI model based on the labeling data and the EEG data.

17 . The method of claim 1 , comprising training the AI model based on unlabeled EEG data.

18 . The method of claim 1 , comprising training the AI model using unsupervised or semi-supervised feature learning to identify electrical biomarkers associated with brain states from the EEG data.

19 . The method of claim 1 , comprising training the AI model using different data to refine the AI model after unsupervised or semi-supervised feature learning to train the AI model.

20 . The method of claim 19 , wherein training the AI model using different data to refine the AI model comprises using supervised feature learning using labeled data to refine the AI model.

21 . The method of claim 1 , wherein the AI model comprises a contrastive learning model.