A system and method for prediction of the likelihood of the response to placebo during clinical trials from raw scalp EEG and accompanied metadata. To accomplish this, the system includes an electroencephalography (EEG) device, at least one EEG data, at least one measured metadata, at least one machine learning unit, and at least one segmented EEG data. The method of use may include the steps of collecting behaviorally measured metadata, collecting EEG data, uploading EEG data, segmenting EEG data, performing data augmentation and channel rolling on the EEG data, performing a model inference, performing a prediction aggregation, outputting a result, and transmitting a result to an end user or client. Predicting the likelihood of a placebo response of a subject in advance assists in tailoring the subjects of the clinical trial. This will save time and money and improve the resulting data from the clinical trial.