Classification of electroencephalograms before or after applying transcutaneous electrical nerve stimulation therapy using fractional empirical mode decomposition

Abstract

It is worth noting that applying the transcutaneous electrical nerve stimulation (TENS) therapy at the superficial nerve locations can modulate the brain activities. This paper aims to further investigate whether applying the TENS therapy at the superficial nerve locations can improve the attention of the subjects or not when the subjects are playing the mathematical game or reading a technical paper. First, the electroencephalograms (EEGs) are acquired before and after the TENS therapy is applied at the superficial nerve locations. Then, both the EEGs acquired before and after applying the TENS therapy are mixed together. Next, the preprocessing is applied to these acquired EEGs. Second, the fractional empirical mode decomposition (FEMD) is employed for extracting the features. Subsequently, the genetic algorithm (GA) is employed for performing the feature selection to obtain the optimal features. Finally, the support vector machine (SVM) and the random forest (RF) are used to classify whether the EEGs are acquired before or after the TENS therapy is applied. Since the higher classification accuracy refers to the larger difference of the EEGs acquired before and after the TENS therapy is applied, the classification accuracy reflects the effectiveness of applying the TENS therapy for improving the attention of the subjects. It is found that the percentages of the classification accuracies based on the EEGs acquired via the one channel device during playing the online mathematical game via the SVM and the RF by our proposed method are between 78.90% and 98.31% as well as between 78.44% and 100%, respectively. The percentages of the classification accuracies based on the EEGs acquired via the eight channel device during playing the online mathematical game via the SVM and the RF by our proposed method are between 80.84% and 93.63% as well as between 86.83% and 99.09%, respectively. the percentages of the classification accuracies based on the EEGs acquired via the one channel device during reading a technical paper via the SVM and the RF by our proposed method are between 77.67% and 83.67% as well as between 79.61% and 84.69%, respectively. the percentages of the classification accuracies based on the EEGs acquired via the sixteen channel device during reading a technical paper via the SVM and the RF by our proposed method are between 82.30% and 90.02% as well as between 91.72% and 95.91%, respectively. As our proposed method yields a higher classification accuracy than the states of the arts methods, this demonstrates the potential of using our proposed method as a tool for the medical officers to perform the precise clinical diagnoses and make the therapeutic decisions based on TENS.