Tactile feedback is crucial for enhancing the virtual-reality (VR) interaction experience. However, current electrotactile devices suffer from issues such as current diffusion and electrode crosstalk, limiting spatial accuracy. To address this challenge, we designed a fabric-based ultrathin flexible microelectrode array with novel stimulation–inhibition electrode units that reduces current diffusion and improves focusing, improving tactile feedback accuracy and clarity. Additionally, we developed an electrical tactile interaction evaluation system to quantitatively assess the tactile recognition accuracy and reaction time of 30 participants. Experimental results demonstrate that the proposed electrode structure and evaluation system substantially enhance tactile perception in VR environments. This system has been demonstrated through immersive scenarios such as touching running water, stroking a bird’s forehead, and feeling a cactus, highlighting its potential for providing precise tactile feedback and enhancing personalized human–computer interaction in VR.

Common image sentiment transformation methods are based on the assumption that transferring image color can transfer image sentiment. However, due to the influence of image content, transferring image color cannot completely transfer image sentiment, and it is necessary to obtain a suitable reference image before transferring image color. However, in practical application, there will be difficulties in obtaining reference images that are similar to the target image in sentiments and similar to the source image in content, and the semantic consistency of local objects need to be considered when transferring image color. Therefore, this paper proposed an image sentiment transformation method based on adaptive brightness adjustment. According to the significant correlation between image brightness and image sentiment (also known as Valence value, abbreviated as V value) in psychology, the method adaptively adjusts brightness through deep neural network ISTNet to convert the image to target image sentiment. First, an image and its corresponding true V value were obtained from the existing image emotion dataset. By changing the image brightness, a series of images with different brightness can be obtained. Then, the pseudo V values corresponding to the images with the same content but different brightness were predicted by the pre-trained image V value regression. Finally, ISTNet was trained with these images and pseudo V values to learn the internal relationship between image brightness adjustment and sentimental change. In practical application, without any reference image, directly input the image and the target V value into the neural network ISTNet to obtain the output image of the corresponding sentimental tag. The experimental results show that the performance of this method is better than the existing color based image sentiment transformation methods.