Multi-View Intact Space Learning for Tinnitus Classification in Resting State EEG

Tinnitus is a common but obscure auditory disease to be studied, and there are still in the lack of effective methods developed to treat tinnitus universally. Although electroencephalogram (EEG) is widely applied to the diagnosis of tinnitus, there are few machine learning methods developed to classify tinnitus patients from healthy people based on the EEG data. Moreover, there is still room for improving the classification performance due to the insufficient existing studies. Therefore, in order to improve the performance of classification based on the EEG data, we introduce a multi-view intact space learning method to characterize the EEG signals by feature extraction in a latent intact space. Considering the fact that there are only a small number of subjects available for study, we conduct the classification for valid segments of EEG data of each subject. In this way, the dataset can be enlarged and the classification performance can be improved. By combining different views of EEG data, a considerable result is achieved on classification by using Support Vector Machine classifier, with accuracy, recall, precision, F1 to be 99.23, 99.72, 98.97, 99.34% respectively. This proposed method is an effective and objective method to classify the tinnitus patients from healthy people, further researches are needed to explore the machine learning method in classification and prediction of the effectiveness of tinnitus interventions based on the EEG response of tinnitus individuals.