Neuro quantum computing based optoelectronic artificial intelligence in electroencephalogram signal analysis

With micrometre resolution, optical coherence tomography (OCT) is a noninvasive cross-sectional imaging method. The centre wavelength and bandwidth of the light source define the theoretical axial resolution; the greater the axial resolution, the broader the bandwidth. The optical wavelength that is employed determines the properties of OCT imaging. In the field of cognitive computing for healthcare, this study suggests an architecture for evaluating artificial intelligence based on neuro-monitoring. In this work, a novel machine learning approach to Internet of Things (IoT) architecture for brain activity analysis based on electroencephalogram (EEG) signal employing semantic analysis of brain neurophysiology is proposed. Here, a neuromonitoring system uses an EEG signal to determine what input to gather. The gathered information is processed for normalisation and noise reduction. Transfer adversarial convolutional architecture is used to choose these processed input features, and reinforcement federated neural networks are used for feature selection and classification. In terms of accuracy, precision, recall, F-1 score, Normalised Square error (NSE), and Root Mean Squared Error (RMSE), experimental analysis is examined for a variety of EEG datasets. Proposed technique attained an accuracy of 95%, precision of 83%, recall of 73%, F-1 score of 63%, NSE of 63%, and RMSE of 51%.