Functional connectivity characteristics of the brain network involved in prickle perception of single fiber stimulation.

BACKGROUND: The complex network connections, information transmission and organization play key roles in brain cognition on sensory stimulation. Previous studies showed that several brain regions of somatosensory, motor, emotional, cognitive, etc. are linked to fabric-evoked prickle. But the functional connectivity characteristics of the brain network involved in prickle perception is still unclear. MATERIALS AND METHODS: In the present study, resting state fMRI (functional magnetic resonance imaging) with functional connectivity analysis was adopted to build the initial brain functional network, and task fMRI with psychophysiological interaction analysis was employed to investigate modulation features of prickling task to functional connections in the brain network. RESULTS: The results showed that, in resting state, six groups or sub-networks can be identified in the prickle network, and when the subjects performed the prickling task, functional connectivity strength between some seed regions (e.g., somatosensory regions and precuneus, emotional regions and the prefrontal cortex, etc.) in the network increased. CONCLUSION: Combining resting-state fMRI with task fMRI is a feasible and promising method to study functional connectivity characteristics of the brain network involved in prickle perception. It is inferred that the "itch" ingredient of prickle sensation was transmitted from somatosensory cortices to precuneus, and emotional attribute (e.g., pain) from somatosensory cortices to the prefrontal cortex and at last to emotional regions.

Multifunctional Silver Nanowire Fabric Reinforced by Hot Pressing for Electromagnetic Interference Shielding, Electric Heating, and Sensing.

Durability and multifunctionality are crucial considerations in the realm of electronic textiles. Herein, a hot-pressing process has been developed to enhance the fixation of silver nanowires (AgNWs) on polyethylene terephthalate (PET) fabric. The conductivity, electromagnetic shielding, and electric heating properties of the hot-pressed fabric were measured to demonstrate the effectiveness of the hot-pressing process. The conductivity of the hot-pressed fabric (180 °C for 90 s) was found to be 464.2 S/m, while that of the fabric without hot pressing was 94.9 S/m. The washed hot-pressed fabric was able to provide a maximum electromagnetic shielding of 17 dB, a negative strain sensing performance (the ΔR/R0 of the hot-pressed fabric was maintained at -15%), and an outstanding electric heating property (the temperature reached 110 °C at a current of 0.08 A). This AgNW fabric holds great potential for use in multi-functional wearable devices, and the hot-pressing process improved its stability and durability, making it suitable for industrial production.

Comparison of brain functional response to mechanical prickling stimuli to the glabrous and hairy skin.

BACKGROUND: A kind of prickle sensation, which is a composite feeling of pain and itch, can be evoked by mechanical stimulation of fiber ends from fabric surface against to human hairy skin, rather than glabrous skin. Now, a functional magnetic resonance imaging (fMRI) study was conducted to investigate the cognitive differences in the brain for mechanical prickling stimuli to the two types of skin. MATERIALS AND METHODS: A nylon filament with the diameter of 205 µm and the length of 8 mm was used to deliver mechanical prickling stimuli respectively to two skin sites, fingertip (glabrous skin) and volar forearm (hairy skin), of eight healthy male subjects. Simultaneously, the technology of fMRI was adopted to acquire BOLD (Blood Oxygen Level-Dependent) signals of brain functional response of the subjects. RESULTS: Somatosensory areas, emotional areas, and the posterior parietal cortex (especially the precuneus) are important brain regions that distinguish between the two conditions. The representation of mechanical prickling stimulation to glabrous skin in the brain favors much more the tactile information of the stimulation and contains no itch, while the key brain area, precuneus, involved in itch was activated by the same mechanical prickling stimulation to hairy skin, and brain response for the condition of hairy skin contains more emotional information, which plays an important role in pain processing. CONCLUSION: Therefore, it can be inferred that a kind of stronger prickle sensation, which contains both pain and itch, was evoked by mechanical stimulation to hairy skin than glabrous skin.

Electroencephalogram signals emotion recognition based on convolutional neural network-recurrent neural network framework with channel-temporal attention mechanism for older adults.

Reminiscence and conversation between older adults and younger volunteers using past photographs are very effective in improving the emotional state of older adults and alleviating depression. However, we need to evaluate the emotional state of the older adult while conversing on the past photographs. While electroencephalogram (EEG) has a significantly stronger association with emotion than other physiological signals, the challenge is to eliminate muscle artifacts in the EEG during speech as well as to reduce the number of dry electrodes to improve user comfort while maintaining high emotion recognition accuracy. Therefore, we proposed the CTA-CNN-Bi-LSTM emotion recognition framework. EEG signals of eight channels (P3, P4, F3, F4, F7, F8, T7, and T8) were first implemented in the MEMD-CCA method on three brain regions separately (Frontal, Temporal, Parietal) to remove the muscle artifacts then were fed into the Channel-Temporal attention module to get the weights of channels and temporal points most relevant to the positive, negative and neutral emotions to recode the EEG data. A Convolutional Neural Networks (CNNs) module then extracted the spatial information in the new EEG data to obtain the spatial feature maps which were then sequentially inputted into a Bi-LSTM module to learn the bi-directional temporal information for emotion recognition. Finally, we designed four group experiments to demonstrate that the proposed CTA-CNN-Bi-LSTM framework outperforms the previous works. And the highest average recognition accuracy of the positive, negative, and neutral emotions achieved 98.75%.

An Optical Algorithm for Relative Thickness of Each Monochrome Component in Multilayer Transparent Mixed Films.

A modification of the two-flux Kubelka-Munk (K-M) model was proposed to describe the energy conservation of scattered light in colored mixed material with a defined scattered photometric, which is applied for the relative quantity distribution of each colored monochrome component in mixed material. A series of systematical experiments demonstrated a higher consistency with the reference quantity distribution than the common Lambert-Beer (L-B) law. Its application in the fibrogram of each component for measuring the cotton fiber's length was demonstrated to be good, extending its applicability to white and dark colored blended fibers, the length of which is harder to measure using L-B law.