Touch-mediated affect has largely been studied using natural textures and brushing techniques, which present challenges in control and deployment across haptic applications. A common alternative is vibrotactile stimulation (VBT) since it is easily deployable and accessible across devices. However, sparse literature exists on the VBT-induced affect modulation and its cortical correlates. Addressing this gap, we developed a novel paradigm that examined the behavioral and electrophysiological correlates of affect induced by VBT. We used electroencephalography (EEG) to record the cortical responses to VBT across six different locations and measured the concurrent affect ratings. Mixed effects modelling, an unsupervised modelling technique, was used to decipher the relationships between stimulation conditions, affect ratings and cortical modulations. Our study revealed that altering the duration of the vibrotactile stimuli can elicit distinct affect responses. The location of the VBT stimuli did not play a part in the perceptual aspects of affect but was involved in the cortical encoding of affect. Furthermore, early cortical processing in the somatosensory cortex (SCx) primarily encoded Arousal and not Valence. Our study lays out phenomenological aspects of cortical VBT processing and lays the groundwork for future research.

Source localization in EEG necessitates aligning EEG sensor coordinates with the subject's MRI, typically done through electromagnetic tracking or 3D scanning. Both methods have drawbacks-electromagnetic tracking is slow and immobile, while 3D scanners are expensive. Photogrammetry offers a cost-effective alternative, but requires multiple photos to sample the head, with good spatial sampling. Post-reconstruction, existing tools for electrode position labelling on the 3D head-surface have limited visual feedback and an inability to accommodate customized montages, typical in multi-modal measurements. Mark3D is an open-source, integrated tool for 3D head reconstruction from phone camera videos. Its eliminates the need for precise spatial sampling during image capture. It includes blur detection algorithms, a user-friendly interface for electrode and tracking, and integrates with popular toolboxes like FieldTrip and MNE Python. The accuracy of the proposed method was benchmarked with the head-surface derived from a handheld 3D scanner ("ground truth"). The derived head shapes were used to reconstruct source information and the source estimates from the ground truth and video-based models were compared. The error in source reconstruction using the photogrammetry model was found to be 0.033 ± 0.016 mm and 0.037 ± 0.017 mm in the left and right cortical hemispheres respectively.

The surgical procedure or Deep Brain Stimulation (DBS) is an established technique aimed at neuromodulation and is most often used for treating neurological and neuropsychiatric disorders. In clinical studies, Magnetoencephalography (MEG) is a widely used technique for characterizing the effects of Deep Brain Stimulation (DBS). A significant limitation of DBS stimulation is the inability to distinguish stimulation artifacts from actual neuronal activity, especially in the lower frequencies, where it may obscure the biological response and be a confounding factor. This paper aims to understand how to circumvent the DBS artifactual component in the summary metrics of DBS-corrupted MEG data. To do the same, we used a watermelon as a phantom model of the head to deploy DBS electrodes. The spectral signature of the artifactual component of DBS and it's power in different clinically significant bands of interest were analyzed in the frequency domain. We present the preliminary results for the same in this paper.