1D-CNN: One Dimensional Convolution Neural Network-Based Electroencephalogram (EEG) Signals Classification with Efficient Artifact Removal for Real-Time Medical Applications

Abstract

Mental task detection and classification employing solo/restricted channel(s) electroencephalogram (EEG) signals in actual time perform a significant part in the pattern of mobile brain-computer interface (BCI) and neurofeedback (NFB) schemes. Nevertheless, the actual time registered EEG signals remain frequently adulterated with noises like ocular artifacts (OAs) and muscle artifacts (MAs) that decline the handmade features extracted out of EEG signal leading to insufficient detection and classification of mental tasks. Hence, we analyse the employment of the latest deep learning approaches that in no way need whatsoever physical feature extraction or artifact repression phase. This study proffers a one-dimensional convolutional neural network (1D-CNN) framework for mental job detection and classification. The proffered framework’s strength can be analysed employing artifact-free and artifact-adulterated EEG signals obtained out of publicly accessible datasets especially the Emotiv EPOC headset. It is observed that the proffered 1D-CNN attains 0.992 of accuracy, 0.993 of precision, 0.9905 of recall, 0.0065 of FPR, and 0.992 of F-measure. Correlative execution assessment exhibit that the proffered framework surpasses prevailing techniques not merely concerning classification precision yet as well as in strength opposing artifacts.