10th International Congress on Information and Communication Technology in concurrent with ICT Excellence Awards (ICICT 2025) will be held at London, United Kingdom | February 18 - 21 2025.
Authors - Syed Muhammad Raza Abidi, Tomas Emmanuel Ward, David C. Henshall, Gabriel-Miro Muntean Abstract - Machine learning is essential to the development of personalized medicine, brain-computer interfaces (BCIs), classification and prediction, and the detection and elimination of artifacts in EEG signal data, among other applications. This work, in order to differentiate between target and non-target rapid serial visual presentation (RSVP) experimental conditions predicts the spatiotemporal patterns of entire trial types. We developed an optimized pipeline to preprocess EEG time-series data in a way that maximizes the relevance of event-related potentials (ERPs). We then utilized the machine learning techniques with the open-source EEG software, namely the MNE-Python tools (library), using the performance criteria, area under the receiver operating characteristic curve (ROC-AUC) with 5-fold cross-validation to predict the trial types.
