Deep Learning Techniques for Pattern Recognition in EEG Audio Signal-Processing-Based Eye-Closed and Eye-Open Cases

Abstract

Recently, pattern recognition in audio signal processing using electroencephalography (EEG) has attracted significant attention. Changes in eye cases (open or closed) are reflected in distinct patterns in EEG data, gathered across a range of cases and actions. Therefore, the accuracy of extracting other information from these signals depends significantly on the prediction of the eye case during the acquisition of EEG signals. In this paper, we use deep learning vector quantization (DLVQ), and feedforward artificial neural network (F-FANN) techniques to recognize the case of the eye. The DLVQ is superior to traditional VQ in classification issues due to its ability to learn a code-constrained codebook. On initialization by the k-means VQ approach, the DLVQ shows very promising performance when tested on an EEG-audio information retrieval task, while F-FANN classifies EEG-audio signals of eye state as open or closed. The DLVQ model achieves higher classification accuracy, higher F score, precision, and recall, as well as superior classification abilities as compared to the F-FANN.