EEG-Based Validation of Neural Response Similarity Between Vibration and Electrical Stimulation for Virtual Tactile Feedback

Electrical stimulation virtual vibration is widely used in fields like virtual reality and medical rehabilitation. However, its parameter optimization still relies on subjective psychological evaluation. This approach lacks objective quantitative criteria. The purpose of this paper is to investigate the neural response relationship between low-frequency vibration stimulation and electrical stimulation using EEG technology, providing quantitative theoretical support for optimizing electrical stimulation parameters. This paper employs a custom-built electrical stimulation system (incorporating a flexible electrode array) to conduct tactile EEG experiments with 42 participants under both electrical stimulation and vibration stimulation conditions. We extract beta-band power spectral density (PSD) and regional permutation entropy (PE) features for the two types of stimulation. Results demonstrate that under vibration stimulation, PSD at C6 and T8 channels exhibit positive correlation with frequency, while PE in the right central region and right temporal-frontal-parietal region shows positive correlation with amplitude. During electrical stimulation, corresponding neural features follow analogous patterns. For both modalities, PSD-frequency correlations (Pearson's r > 0.84) and PE-amplitude correlations (r > 0.68) achieve statistically significant levels. Finally, we conducted classification experiments using a k-nearest neighbor (kNN) classifier, with EEG features from vibratory stimulation as the training set and EEG features from electrical stimulation as the test set. The results show that the accuracy reached 66.7% for the frequency discrimination task, while the average accuracy for the amplitude discrimination task was 67.9%. These findings demonstrate significant similarity in neural signatures elicited by low-frequency vibration stimulation (1-15 Hz) and electrical stimulation. Our study provides new insights for quantifying the refinement of electrical stimulation parameters.