A Deep Learning-Based Classification Method for Different Frequency EEG Data.

Tingxi Wen,Zhongnan Zhang,Chuanbo Huang,Yu Du,Ting Pan,Kelvin Wong

doi:10.1155/2021/1972662

Abstract

In recent years, the research on electroencephalography (EEG) has focused on the feature extraction of EEG signals. The development of convenient and simple EEG acquisition devices has produced a variety of EEG signal sources and the diversity of the EEG data. Thus, the adaptability of EEG classification methods has become significant. This study proposed a deep network model for autonomous learning and classification of EEG signals, which could self-adaptively classify EEG signals with different sampling frequencies and lengths. The artificial design feature extraction methods could not obtain stable classification results when analyzing EEG data with different sampling frequencies. However, the proposed depth network model showed considerably better universality and classification accuracy, particularly for EEG signals with short length, which was validated by two datasets.

Highlights

Epilepsy is characterized by recurrent seizures caused by the abnormal discharge of brain neurons, which often bring physical and psychological harm to patients
Several common classifiers are selected from the scikit-learn library [28], including k-nearest neighbor (k-NN), linear classifier (LDA), support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), and Gaussian naive Bayes (GNB)
This study constructed a convolutional neural network (CNN)-E classification model based on CNN

Summary

Introduction

Epilepsy is characterized by recurrent seizures caused by the abnormal discharge of brain neurons, which often bring physical and psychological harm to patients. Brain wave is a synaptic postsynaptic potential generated by numerous neurons when the brain is active. Brain wave analysis has become an effective and important method for the study of epilepsy. With the development of computer science and technology, numerous studies have focused on the classification of features extracted from EEG signals by using a computer classification model [3, 4]. Such a research often follows the following steps: EEG data acquisition and prepossessing, feature extraction, classification model training, and data prediction. Feature extraction from EEG data is one of the most important steps. Some studies have combined or redesigned these methods to obtain new features, thereby eventually achieving good classification results [8,9,10]

Methods

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Discussion

Conclusion