EEG frequency tagging to explore the cortical activity related to the tactile exploration of natural textures

EEG correlates to perceived urgency elicited by vibration stimulation of the upper body

Conveying information effectively while minimizing user distraction is critical to human-computer interaction. As the proliferation of audio-visual communication pushes human information processing capabilities to the limit, researchers are turning their attention to haptic interfaces. Haptic feedback has the potential to create a desirable sense of urgency that allows users to selectively focus on events/tasks or process presented information with minimal distraction or annoyance. There is a growing interest in understanding the neural mechanisms associated with haptic stimulation. In this study, we aim to investigate the EEG correlates associated with the perceived urgency elicited by vibration stimuli on the upper body using a haptic vest. A total of 31 participants enrolled in this experiment and were exposed to three conditions: no vibration pattern (NVP), urgent vibration pattern (UVP), and very urgent vibration pattern (VUVP). Through self-reporting, participants confirmed that the vibration patterns elicited significantly different levels of perceived urgency (Friedman test, Holm-Bonferroni correction, p < 0.01). Furthermore, neural analysis revealed that the power spectral density of the delta, theta, and alpha frequency bands in the middle central area (C1, Cz, and C2) significantly increased for the UVP and VUVP conditions as compared to the NVP condition (One-way ANOVA test, Holm-Bonferroni correction, p < 0.01). While the perceptual experience of haptic-induced urgency is well studied with self-reporting and behavioral evidence, this is the first effort to evaluate the neural correlates to haptic-induced urgency using EEG. Further research is warranted to identify unique correlates to the cognitive processes associated with urgency from sensory feedback correlates.

The neural correlates of texture perception: A systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies

INTRODUCTION

Humans use discriminative touch to perceive texture through dynamic interactions with surfaces, activating low-threshold mechanoreceptors in the skin. It was largely assumed that texture was processed in primary somatosensory regions in the brain; however, imaging studies indicate heterogeneous patterns of brain activity associated with texture processing.

METHODS

To address this, we conducted a coordinate-based activation likelihood estimation meta-analysis of 13 functional magnetic resonance imaging studies (comprising 15 experiments contributing 228 participants and 275 foci) selected by a systematic review.

RESULTS

Concordant activations for texture perception occurred in the left primary somatosensory and motor regions, with bilateral activations in the secondary somatosensory, posterior insula, and premotor and supplementary motor cortices. We also evaluated differences between studies that compared touch processing to non-haptic control (e.g., rest or visual control) or those that used haptic control (e.g., shape or orientation perception) to specifically investigate texture encoding. Studies employing a haptic control revealed concordance for texture processing only in the left secondary somatosensory cortex. Contrast analyses demonstrated greater concordance of activations in the left primary somatosensory regions and inferior parietal cortex for studies with a non-haptic control, compared to experiments accounting for other haptic aspects.

CONCLUSION

These findings suggest that texture processing may recruit higher order integrative structures, and the secondary somatosensory cortex may play a key role in encoding textural properties. The present study provides unique insight into the neural correlates of texture-related processing by assessing the influence of non-textural haptic elements and identifies opportunities for a future research design to understand the neural processing of texture.

Tactile estimation of hedonic and sensory properties during active touch: An electroencephalography study

Perceptual judgements about our physical environment are informed by somatosensory information. In real-world exploration, this often involves dynamic hand movements to contact surfaces, termed active touch. The current study investigated cortical oscillatory changes during active exploration to inform the estimation of surface properties and hedonic preferences of two textured stimuli: smooth silk and rough hessian. A purpose-built touch sensor quantified active touch, and oscillatory brain activity was recorded from 129-channel electroencephalography. By fusing these data streams at a single trial level, oscillatory changes within the brain were examined while controlling for objective touch parameters (i.e., friction). Time-frequency analysis was used to quantify changes in cortical oscillatory activity in alpha (8-12 Hz) and beta (16-24 Hz) frequency bands. Results reproduce findings from our lab, whereby active exploration of rough textures increased alpha-band event-related desynchronisation in contralateral sensorimotor areas. Hedonic processing of less preferred textures resulted in an increase in temporoparietal beta-band and frontal alpha-band event-related desynchronisation relative to most preferred textures, suggesting that higher order brain regions are involved in the hedonic processing of texture. Overall, the current study provides novel insight into the neural mechanisms underlying texture perception during active touch and how this process is influenced by cognitive tasks.

Neural correlates of perceptual texture change during active touch

Introduction

Texture changes occur frequently during real-world haptic explorations, but the neural processes that encode perceptual texture change remain relatively unknown. The present study examines cortical oscillatory changes during transitions between different surface textures during active touch.

Methods

Participants explored two differing textures whilst oscillatory brain activity and finger position data were recorded using 129-channel electroencephalography and a purpose-built touch sensor. These data streams were fused to calculate epochs relative to the time when the moving finger crossed the textural boundary on a 3D-printed sample. Changes in oscillatory band power in alpha (8-12 Hz), beta (16-24 Hz) and theta (4-7 Hz) frequency bands were investigated.

Results

Alpha-band power reduced over bilateral sensorimotor areas during the transition period relative to ongoing texture processing, indicating that alpha-band activity is modulated by perceptual texture change during complex ongoing tactile exploration. Further, reduced beta-band power was observed in central sensorimotor areas when participants transitioned from rough to smooth relative to transitioning from smooth to rough textures, supporting previous research that beta-band activity is mediated by high-frequency vibrotactile cues.

Discussion

The present findings suggest that perceptual texture change is encoded in the brain in alpha-band oscillatory activity whilst completing continuous naturalistic movements across textures.

Neurophysiological correlates of tactile width discrimination in humans

Introduction

Tactile information processing requires the integration of sensory, motor, and cognitive information. Width discrimination has been extensively studied in rodents, but not in humans.

Methods

Here, we describe Electroencephalography (EEG) signals in humans performing a tactile width discrimination task. The first goal of this study was to describe changes in neural activity occurring during the discrimination and the response periods. The second goal was to relate specific changes in neural activity to the performance in the task.

Results

Comparison of changes in power between two different periods of the task, corresponding to the discrimination of the tactile stimulus and the motor response, revealed the engagement of an asymmetrical network associated with fronto-temporo-parieto-occipital electrodes and across multiple frequency bands. Analysis of ratios of higher [Ratio 1: (0.5-20 Hz)/(0.5-45 Hz)] or lower frequencies [Ratio 2: (0.5-4.5 Hz)/(0.5-9 Hz)], during the discrimination period revealed that activity recorded from frontal-parietal electrodes was correlated to tactile width discrimination performance between-subjects, independently of task difficulty. Meanwhile, the dynamics in parieto-occipital electrodes were correlated to the changes in performance within-subjects (i.e., between the first and the second blocks) independently of task difficulty. In addition, analysis of information transfer, using Granger causality, further demonstrated that improvements in performance between blocks were characterized by an overall reduction in information transfer to the ipsilateral parietal electrode (P4) and an increase in information transfer to the contralateral parietal electrode (P3).

Discussion

The main finding of this study is that fronto-parietal electrodes encoded between-subjects' performances while parieto-occipital electrodes encoded within-subjects' performances, supporting the notion that tactile width discrimination processing is associated with a complex asymmetrical network involving fronto-parieto-occipital electrodes.

Random Tactile Noise Stimulation Reveals Beta-Rhythmic Impulse Response Function of the Somatosensory System

Both passive tactile stimulation and motor actions result in dynamic changes in beta band (15-30 Hz Hz) oscillations over somatosensory cortex. Similar to alpha band (8-12 Hz) power decrease in the visual system, beta band power also decreases following stimulation of the somatosensory system. This relative suppression of α and β oscillations is generally interpreted as an increase in cortical excitability. Here, next to traditional single-pulse stimuli, we used a random intensity continuous right index finger tactile stimulation (white noise), which enabled us to uncover an impulse response function of the somatosensory system. Contrary to previous findings, we demonstrate a burst-like initial increase rather than decrease of beta activity following white noise stimulation (human participants, N = 18, 8 female). These β bursts, on average, lasted for 3 cycles, and their frequency was correlated with resonant frequency of somatosensory cortex, as measured by a multifrequency steady-state somatosensory evoked potential paradigm. Furthermore, beta band bursts shared spectro-temporal characteristics with evoked and resting-state β oscillations. Together, our findings not only reveal a novel oscillatory signature of somatosensory processing that mimics the previously reported visual impulse response functions, but also point to a common oscillatory generator underlying spontaneous β bursts in the absence of tactile stimulation and phase-locked β bursts following stimulation, the frequency of which is determined by the resonance properties of the somatosensory system.SIGNIFICANCE STATEMENT The investigation of the transient nature of oscillations has gained great popularity in recent years. The findings of bursting activity, rather than sustained oscillations in the beta band, have provided important insights into its role in movement planning, working memory, inhibition, and reactivation of neural ensembles. In this study, we show that also in response to tactile stimulation the somatosensory system responds with ∼3 cycle oscillatory beta band bursts, whose spectro-temporal characteristics are shared with evoked and resting-state beta band oscillatory signatures of the somatosensory system. As similar bursts have been observed in the visual domain, these oscillatory signatures might reflect an important supramodal mechanism in sensory processing.

EEG guided electrical stimulation parameters generation from texture force profiles

Objective.Our aim is to enhance sensory perception and spatial presence in artificial interfaces guided by EEG. This is done by developing a closed-loop electro-tactile system guided by EEG that adaptively update the electrical stimulation parameters to achieve EEG responses similar to the EEG responses generated from touching textured surface.Approach.In this work, we introduce a model that defines the relationship between the contact force profiles and the electrical stimulation parameters. This is done by using the EEG and force data collected from two experiments. The first was conducted by moving a set of textured surfaces against the subjects' fingertip, while collecting both EEG and force data. Whereas the second was carried out by applying a set of different pulse and amplitude modulated electrical stimuli to the subjects' index finger while recording EEG.Main results.We were able to develop a model which could generate electrical stimulation parameters corresponding to different textured surfaces. We showed by offline testing and validation analysis that the average error between the EEG generated from the estimated electrical stimulation parameters and the actual EEG generated from touching textured surfaces is around 7%.Significance.Haptic feedback plays a vital role in our daily life, as it allows us to become aware of our environment. Even though a number of methods have been developed to measure perception of spatial presence and provide sensory feedback in virtual reality environments, there is currently no closed-loop control of sensory stimulation. The proposed model provides an initial step towards developing a closed loop electro-tactile haptic feedback model that delivers more realistic touch sensation through electrical stimulation.

Neural Coding of Vibration Intensity

Vibrotactile feedback technology has become widely used in human-computer interaction due to its low cost, wearability, and expressiveness. Although neuroimaging studies have investigated neural processes associated with different types of vibrotactile feedback, encoding vibration intensity in the brain remains largely unknown. The aim of this study is to investigate neural processes associated with vibration intensity using electroencephalography. Twenty-nine healthy participants (aged 18-40 years, nine females) experienced vibrotactile feedback at the distal phalanx of the left index finger with three vibration intensity conditions: no vibration, low-intensity vibration (1.56 g), and high-intensity vibration (2.26 g). The alpha and beta band event-related desynchronization (ERD) as well as P2 and P3 event-related potential components for each of the three vibration intensity conditions are obtained. Results demonstrate that the ERD in the alpha band in the contralateral somatosensory and motor cortex areas is significantly associated with the vibration intensity. The average power spectral density (PSD) of the peak period of the ERD (400-600 ms) is significantly stronger for the high- and low-vibration intensity conditions compared to the no vibration condition. Furthermore, the average PSD of the ERD rebound (700-2,000 ms) is significantly maintained for the high-vibration intensity compared to low-intensity and no vibration conditions. Beta ERD signals the presence of vibration. These findings inform the development of quantitative measurements for vibration intensities based on neural signals.

Investigation of Tactile Perception Evoked by Ridged Texture Using ERP and Non-linear Methods

The triangular ridged surface can improve the grip reliability of products, but the sharp edge of triangular ridge induces sharp and uncomfortable feeling. To study the effect of edge shape (sharp, round, and flat shape) of triangular ridges on brain activity during touching, electroencephalograph (EEG) signals during tactile perception were evaluated using event-related potentials (ERP) and non-linear analysis methods. The results showed that the early component of P100 and P200, and the late component of P300 were successfully induced during perceiving the ridged texture. The edge shape features affect the electrical activity of brain during the tactile perceptions. The sharp shape feature evoked fast P100 latency and high P100 amplitude. The flat texture with complex (sharp and flat) shape feature evoked fast P200 latency, high P200 amplitude and RQA parameters. Both of the sharp shape and complex shape feature tended to evoke high peak amplitude of P300. The large-scale structures of recurrence plots (RPs) and recurrence quantification analysis (RQA) parameters can visually and quantitatively characterize the evolution regulation of the dynamic behavior of EEG system along with the tactile process. This study proved that RPs and RQA were protential methods for the feature extraction and state recognition of EEG during tactile perception of textured surface. This research contributes to optimize surface tactile characteristics on products, especially effective surface textures design for good grip.

Human low-threshold mechanoafferent responses to pure changes in friction controlled using an ultrasonic haptic device

The forces that are developed when manipulating objects generate sensory cues that inform the central nervous system about the qualities of the object’s surface and the status of the hand/object interaction. Afferent responses to frictional transients or slips have been studied in the context of lifting/holding tasks. Here, we used microneurography and an innovative tactile stimulator, the Stimtac, to modulate both the friction level of a surface, without changing the surface or adding a lubricant, and, to generate the frictional transients in a pure and net fashion. In three protocols, we manipulated: the frictional transients, the friction levels, the rise times, the alternation of phases of decrease or increase in friction to emulate grating-like stimuli. Afferent responses were recorded in 2 FAIs, 1 FAII, 2 SAIs and 3 SAIIs from the median nerve of human participants. Independently of the unit type, we observed that: single spikes were generated time-locked to the frictional transients, and that reducing the friction level reduced the number of spikes during the stable phase of the stimulation. Our results suggest that those frictional cues are encoded in all the unit types and emphasize the possibility to use the Stimtac device to control mechanoreceptor firing with high temporal precision.

EEG-based texture roughness classification in active tactile exploration with invariant representation learning networks

During daily activities, humans use their hands to grasp surrounding objects and perceive sensory information which are also employed for perceptual and motor goals. Multiple cortical brain regions are known to be responsible for sensory recognition, perception and motor execution during sensorimotor processing. While various research studies particularly focus on the domain of human sensorimotor control, the relation and processing between motor execution and sensory processing is not yet fully understood. Main goal of our work is to discriminate textured surfaces varying in their roughness levels during active tactile exploration using simultaneously recorded electroencephalogram (EEG) data, while minimizing the variance of distinct motor exploration movement patterns. We perform an experimental study with eight healthy participants who were instructed to use the tip of their dominant hand index finger while rubbing or tapping three different textured surfaces with varying levels of roughness. We use an adversarial invariant representation learning neural network architecture that performs EEG-based classification of different textured surfaces, while simultaneously minimizing the discriminability of motor movement conditions (i.e., rub or tap). Results show that the proposed approach can discriminate between three different textured surfaces with accuracies up to 70%, while suppressing movement related variability from learned representations.

EEG-based trial-by-trial texture classification during active touch

Trial-by-trial texture classification analysis and identifying salient texture related EEG features during active touch that are minimally influenced by movement type and frequency conditions are the main contributions of this work. A total of twelve healthy subjects were recruited. Each subject was instructed to use the fingertip of their dominant hand's index finger to rub or tap three textured surfaces (smooth flat, medium rough, and rough) with three levels of movement frequency (approximately 2, 1 and 0.5 Hz). EEG and force data were collected synchronously during each touch condition. A systematic feature selection process was performed to select temporal and spectral EEG features that contribute to texture classification but have low contribution towards movement type and frequency classification. A tenfold cross validation was used to train two 3-class (each for texture and movement frequency classification) and a 2-class (movement type) Support Vector Machine classifiers. Our results showed that the total power in the mu (8-15 Hz) and beta (16-30 Hz) frequency bands showed high accuracy in discriminating among textures with different levels of roughness (average accuracy > 84%) but lower contribution towards movement type (average accuracy < 65%) and frequency (average accuracy < 58%) classification.

Differential width discrimination task for active and passive tactile discrimination in humans

The neurophysiological basis of width discrimination has been extensively studied in rodents and has shown that active and passive tactile discrimination engage fundamentally different neural networks. Although previous studies have analyzed active and passive tactile processing in humans, little is known about the neurophysiological basis of width discrimination in humans. Here we present a width discrimination task for humans that reproduces the main features of the width discrimination task previously developed for rodents. The task required subjects to actively or passively sample two movable bars forming a “narrow” or “wide” aperture. Subjects were then required to press one of two buttons to indicate if the bar width was “narrow” or “wide”. Behavioral testing showed that subjects were capable of discriminating between wide or narrow apertures up to distances of 0.1 cm. Electroencephalography (EEG) recordings further suggested distinct topographic maps for active and passive versions of the task during the period associated with the aperture discrimination. These results indicate that the Human Differential Width Discrimination Task is a valuable tool to describe the behavioral characteristics and neurophysiological basis of tactile processing.•

Active and passive width discrimination has been extensively studied in rodents but not in humans.

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Human subjects were capable of discriminating aperture widths of 0.1 cm.

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Electroencephalography recordings showed that active and passive versions of the task were associated with different topographic maps.

From finger friction to brain activation: Tactile perception of the roughness of gratings

The formation of tactile perception is related to skin receptors and the cerebral cortex. In order to systematically study the tactile perception from finger friction to the brain response, a 32-channel Brain Products system and two tri-axial force sensors were used to obtain electroencephalograph (EEG) and friction signals during fingers exploring grating surfaces. A finite element finger model was established to analyze the stress changes of the skin receptors during tactile perception. Samples with different grating widths and spaces were chosen. The results indicated that different gratings induced different stress concentrations within skin that stimulated Meissner and Merkel receptors. Skin friction was affected by gratings during the tactile perception. It was also found that P300 evoked by gratings was related with the skin deformation, contact area, friction force, and stress around cutaneous mechanoreceptors. The wider grating width generated larger skin deformation, friction force, and stress, which induced stronger tactile stimulation. The smaller grating spacing generated higher vibration frequency, inducing stronger tactile stimulation. The latency of the P300 peak was related to the difference between the textured target stimulus and the smooth non-target stimulus. This study proofed that there was a relationship between the activation in brain regions, surface friction, and contact conditions of skin during the tactile perception. It contributes to understanding the formation process and cognitive mechanism of tactile perception of different surface textures.

Recent Advances in Tactile Sensing Technology

Research on tactile sensing technology has been actively conducted in recent years to pave the way for the next generation of highly intelligent devices. Sophisticated tactile sensing technology has a broad range of potential applications in various fields including: (1) robotic systems with tactile sensors that are capable of situation recognition for high-risk tasks in hazardous environments; (2) tactile quality evaluation of consumer products in the cosmetic, automobile, and fabric industries that are used in everyday life; (3) robot-assisted surgery (RAS) to facilitate tactile interaction with the surgeon; and (4) artificial skin that features a sense of touch to help people with disabilities who suffer from loss of tactile sense. This review provides an overview of recent advances in tactile sensing technology, which is divided into three aspects: basic physiology associated with human tactile sensing, the requirements for the realization of viable tactile sensors, and new materials for tactile devices. In addition, the potential, hurdles, and major challenges of tactile sensing technology applications including artificial skin, medical devices, and analysis tools for human tactile perception are presented in detail. Finally, the review highlights possible routes, rapid trends, and new opportunities related to tactile devices in the foreseeable future.

Tactile Perception of Roughness and Hardness to Discriminate Materials by Friction-Induced Vibration

The human fingertip is an exquisitely powerful bio-tactile sensor in perceiving different materials based on various highly-sensitive mechanoreceptors distributed all over the skin. The tactile perception of surface roughness and material hardness can be estimated by skin vibrations generated during a fingertip stroking of a surface instead of being maintained in a static position. Moreover, reciprocating sliding with increasing velocities and pressures are two common behaviors in humans to discriminate different materials, but the question remains as to what the correlation of the sliding velocity and normal load on the tactile perceptions of surface roughness and hardness is for material discrimination. In order to investigate this correlation, a finger-inspired crossed-I beam structure tactile tester has been designed to mimic the anthropic tactile discrimination behaviors. A novel method of characterizing the fast Fourier transform integral (FFT) slope of the vibration acceleration signal generated from fingertip rubbing on surfaces at increasing sliding velocity and normal load, respectively, are defined as k_v and k_w, and is proposed to discriminate the surface roughness and hardness of different materials. Over eight types of materials were tested, and they proved the capability and advantages of this high tactile-discriminating method. Our study may find applications in investigating humanoid robot perceptual abilities.

EEG Frequency-Tagging and Input-Output Comparison in Rhythm Perception

The combination of frequency-tagging with electroencephalography (EEG) has recently proved fruitful for understanding the perception of beat and meter in musical rhythm, a common behavior shared by humans of all cultures. EEG frequency-tagging allows the objective measurement of input-output transforms to investigate beat perception, its modulation by exogenous and endogenous factors, development, and neural basis. Recent doubt has been raised about the validity of comparing frequency-domain representations of auditory rhythmic stimuli and corresponding EEG responses, assuming that it implies a one-to-one mapping between the envelope of the rhythmic input and the neural output, and that it neglects the sensitivity of frequency-domain representations to acoustic features making up the rhythms. Here we argue that these elements actually reinforce the strengths of the approach. The obvious fact that acoustic features influence the frequency spectrum of the sound envelope precisely justifies taking into consideration the sounds used to generate a beat percept for interpreting neural responses to auditory rhythms. Most importantly, the many-to-one relationship between rhythmic input and perceived beat actually validates an approach that objectively measures the input-output transforms underlying the perceptual categorization of rhythmic inputs. Hence, provided that a number of potential pitfalls and fallacies are avoided, EEG frequency-tagging to study input-output relationships appears valuable for understanding rhythm perception.

Spatiotemporal Dynamics of the Cortical Responses Induced by a Prolonged Tactile Stimulation of the Human Fingertips

The sense of touch is fundamental for daily behavior. The aim of this work is to understand the neural network responsible for touch processing during a prolonged tactile stimulation, delivered by means of a mechatronic platform by passively sliding a ridged surface under the subject's fingertip while recording the electroencephalogram (EEG). We then analyzed: (i) the temporal features of the Somatosensory Evoked Potentials and their topographical distribution bilaterally across the cortex; (ii) the associated temporal modulation of the EEG frequency bands. Long-latency SEP were identified with the following physiological sequence P100-N140-P240. P100 and N140 were bilateral potentials with higher amplitude in the contralateral hemisphere and with delayed latency in the ipsilateral side. Moreover, we found a late potential elicited around 200 ms after the stimulation was stopped, which likely encoded the end of tactile input. The analysis of cortical oscillations indicated an initial increase in the power of theta band (4-7 Hz) for 500 ms after the stimulus onset followed a decrease in the power of the alpha band (8-15 Hz) that lasted for the remainder of stimulation. This decrease was prominent in the somatosensory cortex and equally distributed in both contralateral and ipsilateral hemispheres. This study shows that prolonged stimulation of the human fingertip engages the cortex in widespread bilateral processing of tactile information, with different modulations of the theta and alpha bands across time.