Recognition of Ocular Artifacts in EEG Signal through a Hybrid Optimized Scheme

Brain computer interface (BCI) requires an online and real-time processing of EEG signals. Hence, the accuracy of the recording system is improved by nullifying the developed artifacts. The goal of this proposal is to develop a hybrid model for recognizing and minimizing ocular artifacts through an improved deep learning scheme. The discrete wavelet transform (DWT) and Pisarenko harmonic decomposition are used for decomposing the signals. Then, the features are extracted by principal component analysis (PCA) and independent component analysis (ICA) techniques. After collecting the features, an optimized deformable convolutional network (ODCN) is used for the recognition of ocular artifacts from EEG input signals. When artifacts are sensed, the moderation method is executed by applying the empirical mean curve decomposition (EMCD) followed by ODCN for noise optimization in EEG signals. Conclusively, the spotless signal is reconstructed by an application of inverse EMCD. The proposed method has achieved a higher performance than that of conventional methods, which demonstrates a better ocular artifact reduction by the proposed method.

Automated summary

This study proposes a hybrid model to recognize and reduce ocular artifacts through an improved deep learning scheme. The signals are decomposed using discrete wavelet transform and Pisarenko harmonic decomposition, and the features are extracted using principal component analysis and independent component analysis. An optimized deformable convolutional network is used to detect ocular artifacts and empirical mean curve decomposition is applied for noise optimization. The proposed method demonstrates better performance in reducing ocular artifacts.