Investigating the Neural Correlates of Stiffness Perception using Force and Pseudo-Haptic Feedback

Abstract

Understanding how humans perceive and process sensations, such as stiffness, is crucial for designing effective human-computer interfaces, robotic systems, and rehabilitation protocols. Despite extensive research in the field, the neural mechanisms underlying the perception of stiffness remain largely unknown. This study aims to bridge this gap by investigating the electrical neural responses associated with stiffness stimuli, utilizing electroencephalography (EEG) as a non-invasive and reliable measurement tool. In this study, we investigate the variations in neural perception of stiffness across three scenarios: real feedback, continuous force feedback, and pseudo-haptic feedback, with two possible stiffness levels. The brain waves of participants are recorded during the task using a commercially available singlechannel EEG device. The EEG data recorded for the three scenarios is analyzed to assess the neural correlates associated with each situation. Our findings from the experiments reveal that real and haptic feedback generate similar neural responses (𝐹(1, 58) = 0.8044, 𝑝 = 0.373) at low stiffness level(𝑘 = 169𝑁/𝑚). Pseudo-haptic feedback consistently results in lower neural activity, indicating less effective engagement. Delta band analysis indicates the greatest cognitive effort in the pseudo-haptic high stiffness condition (𝑘 = 500𝑁/𝑚) due to visualtactile cue conflict, with an average Power Spectral Density (PSD) difference of 2,142.6 𝜇V 2 /Hz across participants. The results highlight the need to improve haptic feedback technology, especially at higher stiffness levels, to reduce cognitive load and enhance user experience by ensuring congruence between visual and haptic feedback. However, the use of a single-channel EEG device limits observation to the prefrontal cortex, potentially overlooking activity in other relevant brain areas. Future research should incorporate multi-channel EEG to provide a more comprehensive understanding of neural responses across different brain regions.