Description

The computational electroencephalogram (EEG) is recently garnering significant attention in examining whether the EEG features can be used as new predictors for the prediction of recovery in moderate brain injury Detection. To address this issue, a computer aided approach is proposed in this article for automated DoC (Disorder of consciousness) detection through extracting knowledge from electroencephalogram (EEG) signals. It introduces a new connectivity measure: Power Spectral Density Difference (PSDD) incorporating with a recursive Cosine function (CPSDD). The subsequent processing steps. As a result, it is crucial to devise a strategy for meticulously flagging and extracting clean EEG data to retrieve high-quality discriminative features using PCA for Feature selection Then, the approach classifies brain-injured patients into DoC (positive, negative, neutral) classes through an ensemble Machine learning Approach.Our Proposed Approach to implement deep learning algorithm for high accuracy and prediction status.