EEG and quantitative EEG (qEEG) are increasingly used in drug development to investigate central nervous system penetration, efficacy, and safety of drug candidates.  Being largely non-invasive and replicable measurements, they provide a quantitative measure of functional brain responses to pharmacological interventions. The presence of evolutionary conserved oscillatory brain mechanisms and connectivity at circuit level underlie the value of qEEG as a preclinical-clinical translatable tool providing a high temporal resolution window into changes across time.

Despite the widespread and standardized use of EEG in the clinical space, preclinical collection methods are not streamlined across the industry and often vary within a single outsourced program. Lack of availability of database-appropriate analytical tools compounds this issue. 

qEEG is informationally rich, translatable across species, and eminently suited to machine learning or powerful statistical computational approaches. However, no streamlined approaches to qEEG exist in the industry presently. In order to maximize the power of AI/ML methods the results across species, compounds, and modalities need to be available in secure searchable databases, with fast and reliable tools for curation, analytics, and reporting.