An Extended Computer-Aided Diagnosis System for Multidomain EEG Classification

An electroencephalogram (EEG) signal is a dominant indicator of brain activity that contains conspicuous information about the underlying mental state. The EEG signals classification is desirable in order to comprehend the objective behavior of the brain in various diseased or control activities. Even though many studies have been done to find the best analytical EEG system, they all focus on domain-specific solutions and can't be extended to more than one domain. This study introduces a multidomain adaptive broad learning EEG system (MABLES) for classifying four different EEG groups under a single sequential framework. In particular, this work expands the applicability of three previously proposed modules, namely, empirical Fourier decomposition (EFD), improved empirical Fourier decomposition (IEFD), and multidomain features selection (MDFS) approaches for the realization of MABLES. The feed-forward neural network classifier is used in extensive trials on four different datasets utilizing a 10-fold cross-validation technique. Results compared to previous research show that the mental imagery, epilepsy, slow cortical potentials, and schizophrenia EEG datasets have the highest average classification accuracy, with scores of 94.87%, 98.90%, 92.65% and 95.28%, respectively. The entire qualitative and quantitative study verifies that the suggested MABLES framework exceeds the existing domain-specific methods regarding classification accuracies and multi-role adaptability, therefore can be recommended as an automated real-time brain rehabilitation system.