Tinnitus is a kind of auditory disease characterized by an ongoing conscious perception of a sound in the absence of any external sound source. It is a common symptom for which no effective treatment exists. Though many non-invasive functional imaging modalities have been rapidly developed and applied to this field, yet, whether the EEG signal can be utilized to distinguish tinnitus patients from normal populations has not been investigated. In the present study, we perform a binary classification based on EEG signal to distinguish tinnitus patients from normal populations. In this study, 22 subjects are involved in the experiment with 15 of them being tinnitus patients and the others being normal controls. The collected EEG signals are preprocessed in frequency domain and well represented as features that depict each subject. Then the linear support vector machine is applied to classify the subjects. Satisfactory results have been achieved, where the accuracy of the classification could reach 90.91% in spite of the undeniable fact that the collected EEG signals contain noises. Accordingly, the present study reveals that the EEG signals can be utilized to distinguish tinnitus patients from normal populations, which could be regarded as an auxiliary therapy in tinnitus.
Li, P. Z., Li, J. H., & Wang, C. D. (2016). A SVM-based EEG signal analysis: An auxiliary therapy for tinnitus. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10023 LNAI, pp. 207–219). Springer Verlag. https://doi.org/10.1007/978-3-319-49685-6_19