Evaluating cognitive enhancement through TENS: An EEG classification framework using TQWT, PSR, and ETNet

Transcutaneous Electrical Nerve Stimulation (TENS) is a widely used therapy for cognitive enhancement. Traditionally, its efficacy has been assessed through subjective feedback, which is prone to errors and insufficient for accurately evaluating therapeutic outcomes. To address this limitation, we propose a novel framework for assessing the cognitive enhancement effects of TENS therapy using single channel electroencephalogram (EEG) signals. Specifically, we constructed a new EEG dataset collected during TENS therapy, with TENS serving as the sole experimental variable. EEGs recorded during cognitive tests were classified to assess the therapy’s impact. The classification accuracy reflects the extent to which TENS impact EEGs, providing an objective measure of its effects on cognitive enhancement. The EEGs are first decomposed into multiple components using the Tunable-Q factor Wavelet Transform (TQWT). Subsequently, these components are embedded into a high-dimensional space using Phase Space Reconstruction (PSR). We then design a EEG classification network, ETNet, which efficiently learns both the time–frequency features and nonlinear dynamics extracted in the earlier steps. Numerical simulation results demonstrate the effectiveness of our approach, achieving an average accuracy (ACC) of 91.31%, a standard deviation (STD) of 2.52%, a kappa score of 0.8247, an F1 score of 0.9125, and an AUC of 0.9390. These results surpass those of various existing methods, underscoring the practical utility of our proposed evaluation framework.