Electroencephalography (EEG) was widely investigated in brain status detection and disease diagnosis, in which the fractal analysis played an important role. In this paper, the roughness scaling extraction (RSE) algorithm proposed in our previous study on surface morphologies was applied to calculate the fractal dimensions (FDs) of artificial profiles and EEG signals. Fractal profiles with ideal FDs ranging from 1.01 to 1.99 were generated through the Weierstrass-Mandelbrot function. The RSE algorithm and the traditional algorithms, including the Higuchi algorithm, the Katz algorithm, and the box counting algorithm, were compared by analyzing the artificial profiles. Based on the mean relative errors and mean square errors, it was found that the RSE algorithm was more accurate than the traditional algorithms. To investigate the influence of noise on FD calculation, noise with different levels was added to the fractal profiles. The RSE and Higuchi algorithms were found reliable at signal-to-noise ratios of 50 and 40 dB, while the accuracy of RSE was also superior to that of the Higuchi. The RSE, Higuchi, and Katz algorithms were utilized to analyze the EEG signals of epilepsy events. The significant FD increasing, which corresponded to the seizure onset, could be detected, and the overlapping between the seizure and non-seizure statuses was small by using the RSE algorithm, indicating its feasibility for the EEG fractal analysis.

Profile pictures are perceived as a form of social identity and individuals often engage in similar categorization processes. Profile pictures in social media represent virtual identities in online social networks and embody personal impression. The influence of profile picture styles on empathy plays an important role in the design and user experience of social media. However, the potential impact of profile pictures with different image styles (e.g., cartoon faces, real faces, or non-face images) on the perception of users in social media is still unclear. To investigate this impact, a controlled laboratory experiment (48 participants) and an ecological online experiment (184 participants) were conducted. Results show that participants’ empathic experience for users choosing cartoon faces or real faces as profile pictures are greater than that for users choosing non-face images. Besides, participants performed better in identity recognition for users choosing real face or non-face images than those choosing cartoon faces. Increasing empathic experience was associated with the degree to which users choosing profile pictures are categorized as ingroup members. Users of social media often make social categorizations (i.e., ingroup/outgroup categorization) based on the social identities expressed by their profile pictures. Our results also showed that increasing empathic experience was associated with the degree to which users choosing profile pictures are categorized as ingroup members.