EEG Signals Classification in Time-Frequency Images by Fusing Rotation-Invariant Local Binary Pattern and Gray Level Co-occurrence Matrix Features

Zhongyi Hu, Zhenzhen Luo, Shan Jin, Zuoyong Li

Abstract: Automatic epilepsy diagnosis system based on EEG signals is critical in the classification of epilepsy. This disease classification through doctors’ visual observation of transient EEG signals is more art than science. This paper proposes a novel EEG signals classification method based on time-frequency images by fusing rotation-invariant local binary pattern and gray level co-occurrence matrix features. Specifically , a continuous wavelet transform is applied to perform wavelet decomposition of mutated EEG signals and obtain their time-frequency images. Then, the binary particle swarm optimization algorithm is used to eliminate redundant features in feature selection and optimize the hyperparameters of SVM. The proposed method was verified by its comparison with other available cutting-edge classification methods. The proposed EEG signals classification framework achieved a better classification effect, which will help expert clinicians and neuroscientists to make more accurate and rapid epilepsy diagnosis.