What does ABSP mean in UNCLASSIFIED

Advanced Brain Signal Processing (ABSP) is a set of techniques used to measure and analyze electrical signals emitted from the brain. These signals can be used to study brain function, diagnose neurological disorders, and develop treatments for neurodegenerative diseases.

ABSP can be used for researching various aspects of neuroscience including functional connectivity mapping, functional imaging, electrophysiology, epilepsy diagnosis, and cognitive disease diagnosis. Additionally, it can be used in clinical practice such as monitoring patients during surgery or acute care events, or interpreting EEG data collected in research studies.

ABSP measures electroencephalography (EEG), electromyography (EMG), blood oxygenation level dependent (BOLD), event-related potentials (ERPs), local field potentials (LFPs), magnetoencephalography (MEG), genetic expression data, optogenetics data, audio-evoked responses (AER), and magnetic source imaging(MSI).

First, electrodes are placed on the scalp and attached directly to a computer system that records the electrical activity generated by neurons in the brain. This data is then analyzed using proprietary software tools developed specifically for ABSP purposes such as signal processing algorithms and mathematical models to extract important information from the raw EEG recordings. Finally this information can then be visualized or interpreted by clinicians or researchers in order to gain insights into different aspects of neuroscience or treatment development.

By using ABSP researchers gain access to more accurate real-time measurements that allow them to track changes in a patient's condition over time rather than relying on snapshots taken at separate points in time like traditional MRI scans do. Additionally it captures recordings from different parts of the brain which gives researchers a more well rounded view than what conventional EEG recording has been able to provide so far. Lastly it also helps reduce costs associated with collecting large amounts of data since it allows multiple participants' responses to be captured simultaneously with one sensor deployment.