Contact of a Finger on Rigid Surfaces and Textiles: Friction Coefficient and Induced Vibrations

The tactile information about object surfaces is obtained through perceived contact stresses and frictioninduced vibrations generated by the relative motion between the fingertip and the touched object. The friction forces affect the skin stress-state distribution during surface scanning, while the sliding contact generates vibrations that propagate in the finger skin activating the receptors (mechanoreceptors) and allowing the brain to identify objects and perceive information about their properties. In this article, the friction coefficient between a real human finger and both rigid surfaces and fabrics is retrieved as a function of the contact parameters (load and scanning speed). Then, the analysis of the vibration spectra is carried out to investigate the features of the induced vibrations, measured on the fingernail, as a function of surface textures and contact parameters. While the friction coefficient measurements on rigid surfaces agree with empirical laws found in literature, the behaviour of the friction coefficient when touching a fabric is more complex, and is mainly the function of the textile constructional properties. Results show that frequency spectrum distribution, when touching a rigid surface, is mainly determined by the relative geometry of the two contact surfaces and by the contact parameters. On the contrary, when scanning a fabric, the structure and the deformation of the textile itself largely affect the spectrum of the induced vibration. Finally, some major features of the measured vibrations (frequency distribution and amplitude) are found to be representative of tactile perception compared to psychophysical and neurophysiologic works in literature

Combining Texture-Derived Vibrotactile Feedback, Concatenative Synthesis and Photogrammetry for Virtual Reality Rendering

(Abstract to follow

Texture is encoded in precise temporal spiking patterns in primate somatosensory cortex

Humans are exquisitely sensitive to the microstructure and material properties of surfaces. In the peripheral nerves, texture information is conveyed via two mechanisms: coarse textural features are encoded in spatial patterns of activation that reflect their spatial layout, and fine features are encoded in highly repeatable, texture-specific temporal spiking patterns evoked as the skin moves across the surface. Here, we examined whether this temporal code is preserved in the responses of neurons in somatosensory cortex. We scanned a diverse set of everyday textures across the fingertip of awake macaques while recording the responses evoked in individual cortical neurons. We found that temporal spiking patterns are highly repeatable across multiple presentations of the same texture, with millisecond precision. As a result, texture identity can be reliably decoded from the temporal patterns themselves, even after information carried in the spike rates is eliminated. However, the combination of rate and timing is more informative than either code in isolation. The temporal precision of the texture response is heterogenous across cortical neurons and depends on the submodality composition of their input and on their location along the somatosensory neuraxis. Furthermore, temporal spiking patterns in cortex dilate and contract with decreases and increases in scanning speed, respectively, and this systematic relationship between speed and patterning may contribute to the observed perceptual invariance to speed. Finally, we find that the quality of a texture percept can be better predicted when these temporal patterns are taken into consideration. We conclude that high-precision spike timing complements rate-based signals to encode texture in somatosensory cortex

The temporal pattern of impulses in primary afferents analogously encodes touch and hearing information

An open question in neuroscience is the contribution of temporal relations between individual impulses in primary afferents in conveying sensory information. We investigated this question in touch and hearing, while looking for any shared coding scheme. In both systems, we artificially induced temporally diverse afferent impulse trains and probed the evoked perceptions in human subjects using psychophysical techniques. First, we investigated whether the temporal structure of a fixed number of impulses conveys information about the magnitude of tactile intensity. We found that clustering the impulses into periodic bursts elicited graded increases of intensity as a function of burst impulse count, even though fewer afferents were recruited throughout the longer bursts. The interval between successive bursts of peripheral neural activity (the burst-gap) has been demonstrated in our lab to be the most prominent temporal feature for coding skin vibration frequency, as opposed to either spike rate or periodicity. Given the similarities between tactile and auditory systems, second, we explored the auditory system for an equivalent neural coding strategy. By using brief acoustic pulses, we showed that the burst-gap is a shared temporal code for pitch perception between the modalities. Following this evidence of parallels in temporal frequency processing, we next assessed the perceptual frequency equivalence between the two modalities using auditory and tactile pulse stimuli of simple and complex temporal features in cross-sensory frequency discrimination experiments. Identical temporal stimulation patterns in tactile and auditory afferents produced equivalent perceived frequencies, suggesting an analogous temporal frequency computation mechanism. The new insights into encoding tactile intensity through clustering of fixed charge electric pulses into bursts suggest a novel approach to convey varying contact forces to neural interface users, requiring no modulation of either stimulation current or base pulse frequency. Increasing control of the temporal patterning of pulses in cochlear implant users might improve pitch perception and speech comprehension. The perceptual correspondence between touch and hearing not only suggests the possibility of establishing cross-modal comparison standards for robust psychophysical investigations, but also supports the plausibility of cross-sensory substitution devices

Dynamic coupling between whisking, barrel cortex, and hippocampus during texture discrimination: A role for slow rhythms

Increasing amounts of work have demonstrated that brain rhythms might constitute clocking mechanisms against which to coordinate sequences of neural firing; such rhythms may be essential to the coding operations performed by the local networks. The sequence of operations underlying a tactile discrimination task in rats requires the animal to integrate two streams of information, those coming from the environment and, from reference memory the rules that dictate the correct response. The current study is a follow up on the work which has described the hippocampal representation of the tactile guided task. We have used a well-established texture discrimination task, in which rats have to associate two stimuli with two different reward locations. We placed microelectrodes in primary somatosensory cortex and the CA1 region of hippocampus to perform recordings of spiking activity and local field potentials when the animal touched the discriminandum as well as when he was in a resting state. We also performed recording on an arena in which the animal moved freely and did not perform any task. Earlier work has demonstrated that tactile signals reach the hippocampus during texture discrimination, presumably through the somatosensory cortex. We predicted that neurons in the primary somatosensory cortex (S1) are entrained to the oscillatory theta rhythm that permeates the hippocampus. Our expectation is that such coherence could serve to increase the reliability of synaptic transmission, linking the acquisition of new sensory information with associative processes. We addressed the following issues: Is the timing of action potentials in S1 modulated by the ongoing hippocampal theta rhythm? If so, is the occurrence of this modulation aligned in time to the period in which the hippocampus acquires tactile signals? We also predicted that the 10-Hz whisking that characterizes the acquisition of texture information would be more strongly phase locked to theta rhythm than the whisking in the air that is not accompanied by any explicit tactile task. We speculate that such phase locking could be a means to synchronize sensory and hippocampal processing. The notion that the coordination between brain areas might be related to the rhythmic of sensorimotor cycles is particularly appealing. We have found that the firing of 18% of barrel cells was significantly modulated by hippocampal theta during the half-second period of active tactile discrimination. Importantly, we found that during periods of rest interleaved in the session, neurons significantly decreased the degree of phase-locking with respect to touch. We hypothesize that areas involved with motivational processes as basal ganglia could gate the entrainment during task related epochs. S1 neurons were classified as those excited by contact with the discriminandum, and those not excited by contact. The firing of both sorts of neurons was modulated by CA1 theta rhythm during exploration of the texture. However the theta phase to which they fired preferentially was opposite; contact-responsive neurons tended to fire in the upward phases of the cycle whereas contact non-responsive neurons tended to fire in the downward phase of the cycle suggesting that theta rhythm might have the function of temporally separating sensory cortical neurons according to their functional properties and the information they carry. By clustering touch-sensitive neurons to a certain time window and separating them from \u2018non-informative\u2019 neurons, theta rhythm could increase the efficiency not only of information tranfer to hippocampus but also the efficiency of information encoding/decoding. We also found phase and amplitude relationships between whisking and hippocampal theta during the goal-directed tactile task; the relationships disappear when the animal moves along an open arena, still actively whisking but not engaged in the texture discrimination task. We were able to show, for the first time to our knowledge, that CA1 theta rhythm can exert a behavioral state-dependent modulatory effect on sensory cortex. S1 neuron firing and whisking activity are entrained to hippocampal theta rhythm when the animal collects meaningful tactile information from the environment

Hybridism: a practice-led investigation

Keele University PhD Thesi

Performing Surfaces: Designing Research-creation for Agentive Embodiment

In this discussion I first outline material intersections of bodies and everyday objects that script habitual choreographies as affective extensions of the body. I then consider the dynamism of the shifts between habitual consciousness and non-consciousness as a site of agentive, embodied transformation within contexts of movement, multiples and sensory experience. I engage willfulness, stickiness, clumsiness and apparatuses of sensory notation as potential techniques for transforming consciousness and non-consciousness in habitual action. Finally, I invite readers to recreate the sensory dimensions of their own habitual experience by experimenting with some 'do it yourself ' exercises

Cortical Orchestra Conducted by Purpose and Function

학위논문(박사)--서울대학교 대학원 :자연과학대학 협동과정 뇌과학전공,2020. 2. 정천기.촉각과 자기수용감각은 우리의 생존 및 일상생활에 절대적인 영향을 미치는 중요한 감각 기능이다. 말초신경계에서 이 두 가지 기능들에 필요한 정보를 수집하고 전달하는 기계적 수용기 및 그 구심성 신경들에 대한 신호 전달 메커니즘 및 그 특징들은 상대적으로 잘 알려져 있는 편이다. 그러나, 촉각과 자기수용감각을 형성하기 위한 인간 뇌의 피질에서의 정보 처리 메커니즘에 대하여 우리가 현재 알고 있는 바는 극히 일부분이다. 이 논문에서 제시하는 일련의 연구들은 인간 뇌 피질 단계에서 촉각과 자기수용감각의 지각적 처리과정에 대한 거시적 신경계 정보처리 메커니즘을 다룬다. 첫 번째 연구에서는 뇌피질뇌파를 이용하여 인간 일차 및 이차 체성감각 피질에서 인공적인 자극과 일상생활에서 접할 수 있는 자극을 포함하는 다양한 진동촉감각 및 질감 자극에 대한 거시적 신경계 정보처리 특성을 밝혔다. 이 연구에서는 일차 및 이차 체성감각 피질의 촉감각 주파수 특이적인 하이-감마 영역 신경활동이 자극 주파수에 따라 각각 상이한 시간적 다이나믹스를 가지고 변화하는 것을 확인하였다. 또한, 이러한 하이-감마 활동은 성긴 질감과 미세한 입자감을 가진 자연스러운 질감 자극에 대해서도 진동촉감각의 경우와 유사한 패턴을 보였다. 이러한 결과들은 인간의 진동촉감각이 매우 단순한 형태에 자극일지라도 대뇌 체성감각 시스템에 있어 거시적인 다중 영역에서의 계층적 정보처리를 동반한다는 점을 시사한다. 두 번째 연구에서는 인간의 움직임과 관련된 두정엽 영역에서의 하이-감마 뇌활성이 자기수용감각과 같은 말초신경계로부터의 체성감각 피드백을 주로 반영하는지, 아니면 움직임 준비 및 제어를 위한 피질 간 신경 프로세스에 대한 활동을 반영하는지를 조사하였다. 연구 결과, 자발적 운동 중 대뇌 운동감각령에서의 하이-감마 활동은 일차 체성감각피질이 일차 운동피질보다 더 지배적인 것으로 나타났다. 또한 이 연구에서는, 움직임과 관련된 일차 체성감각피질에서의 하이-감마 뇌활동은 말초신경계로부터의 자기수용감각과 촉각에 대한 신경계 정보처리를 주로 반영하는 것을 밝혔다. 이러한 연구들을 바탕으로, 마지막 연구에서는 인간 대뇌에서의 체성감각 지각 프로세스에 대한 거시적 피질 간 네트워크를 규명하고자 하였다. 이를 위해, 51명의 뇌전증 환자에게서 체성감각을 유발했던 뇌피질전기자극 데이터와 46명의 환자에게서 촉감각 자극 및 운동 수행 중에 측정한 뇌피질뇌파 하이-감마 매핑 데이터를 종합적으로 분석하였다. 그 결과, 체성감각 지각 프로세스는 대뇌에서 넓은 영역에 걸쳐 분포하는 체성감각 관련 네트워크의 신경 활성을 수반한다는 것을 알아냈다. 또한, 뇌피질전기자극을 통한 대뇌 지도와 하이-감마 매핑을 통한 대뇌 지도는 서로 상당한 유사성을 보였다. 흥미롭게도, 뇌피질전기자극과 하이-감마 활동을 종합한 뇌지도들로부터 체성감각 관련 뇌 영역의 공간적 분포가 체성감각 기능에 따라 서로 달랐고, 그에 해당하는 각 영역들은 서로 뚜렷하게 다른 시간적 다이나믹스를 가지고 순차적으로 활성화되었다. 이러한 결과들은 체성감각에 대한 거시적 신경계 프로세스가 그 지각적 기능에 따라 뚜렷이 다른 계층적 네트워크를 가진다는 점을 시사한다. 더 나아가, 본 연구에서의 결과들은 체성감각 시스템의 지각-행동 관련 신경활동 흐름에 관한 이론적인 가설에 대하여 설득력 있는 증거를 제시하고 있다.Tactile and proprioceptive perceptions are crucial for our daily life as well as survival. At the peripheral level, the transduction mechanisms and characteristics of mechanoreceptive afferents containing information required for these functions, have been well identified. However, our knowledge about the cortical processing mechanism for them in human is limited. The present series of studies addressed the macroscopic neural mechanism for perceptual processing of tactile and proprioceptive perception in human cortex. In the first study, I investigated the macroscopic neural characteristics for various vibrotactile and texture stimuli including artificial and naturalistic ones in human primary and secondary somatosensory cortices (S1 and S2, respectively) using electrocorticography (ECoG). I found robust tactile frequency-specific high-gamma (HG, 50–140 Hz) activities in both S1 and S2 with different temporal dynamics depending on the stimulus frequency. Furthermore, similar HG patterns of S1 and S2 were found in naturalistic stimulus conditions such as coarse/fine textures. These results suggest that human vibrotactile sensation involves macroscopic multi-regional hierarchical processing in the somatosensory system, even during the simplified stimulation. In the second study, I tested whether the movement-related HG activities in parietal region mainly represent somatosensory feedback such as proprioception from periphery or primarily indicate cortico-cortical neural processing for movement preparation and control. I found that sensorimotor HG activities are more dominant in S1 than in M1 during voluntary movement. Furthermore, the results showed that movement-related HG activities in S1 mainly represent proprioceptive and tactile feedback from periphery. Given the results of previous two studies, the final study aimed to identify the large-scale cortical networks for perceptual processing in human. To do this, I combined direct cortical stimulation (DCS) data for eliciting somatosensation and ECoG HG band (50 to 150 Hz) mapping data during tactile stimulation and movement tasks, from 51 (for DCS mapping) and 46 patients (for HG mapping) with intractable epilepsy. The results showed that somatosensory perceptual processing involves neural activation of widespread somatosensory-related network in the cortex. In addition, the spatial distributions of DCS and HG functional maps showed considerable similarity in spatial distribution between high-gamma and DCS functional maps. Interestingly, the DCS-HG combined maps showed distinct spatial distributions depending on the somatosensory functions, and each area was sequentially activated with distinct temporal dynamics. These results suggest that macroscopic neural processing for somatosensation has distinct hierarchical networks depending on the perceptual functions. In addition, the results of the present study provide evidence for the perception and action related neural streams of somatosensory system. Throughout this series of studies, I suggest that macroscopic somatosensory network and structures of our brain are intrinsically organized by perceptual function and its purpose, not by somatosensory modality or submodality itself. Just as there is a purpose for human behavior, so is our brain.PART I. INTRODUCTION 1 CHAPTER 1: Somatosensory System 1 1.1. Mechanoreceptors in the Periphery 2 1.2. Somatosensory Afferent Pathways 4 1.3. Cortico-cortical Connections among Somatosensory-related Areas 7 1.4. Somatosensory-related Cortical Regions 8 CHAPTER 2: Electrocorticography 14 2.1. Intracranial Electroencephalography 14 2.2. High-Gamma Band Activity 18 CHAPTER 3: Purpose of This Study 24 PART II. EXPERIMENTAL STUDY 26 CHAPTER 4: Apparatus Design 26 4.1. Piezoelectric Vibrotactile Stimulator 26 4.2. Magnetic Vibrotactile Stimulator 29 4.3. Disc-type Texture Stimulator 33 4.4. Drum-type Texture Stimulator 36 CHAPTER 5: Vibrotactile and Texture Study 41 5.1. Introduction 42 5.2. Materials and Methods 46 5.2.1. Patients 46 5.2.2. Apparatus 47 5.2.3. Experimental Design 49 5.2.4. Data Acquisition and Preprocessing 50 5.2.5. Analysis 51 5.3. Results 54 5.3.1. Frequency-specific S1/S2 HG Activities 54 5.3.2. S1 HG Attenuation during Flutter and Vibration 62 5.3.3. Single-trial Vibration Frequency Classification 64 5.3.4. S1/S2 HG Activities during Texture Stimuli 65 5.4. Discussion 69 5.4.1. Comparison with Previous Findings 69 5.4.2. Tactile Frequency-dependent Neural Adaptation 70 5.4.3. Serial vs. Parallel Processing between S1 and S2 72 5.4.4. Conclusion of Chapter 5 73 CHAPTER 6: Somatosensory Feedback during Movement 74 6.1. Introduction 75 6.2. Materials and Methods 79 6.2.1. Subjects 79 6.2.2. Tasks 80 6.2.3. Data Acquisition and Preprocessing 82 6.2.4. S1-M1 HG Power Difference 85 6.2.5. Classification 86 6.2.6. Timing of S1 HG Activity 86 6.2.7. Correlation between HG and EMG signals 87 6.3. Results 89 6.3.1. HG Activities Are More Dominant in S1 than in M1 89 6.3.2. HG Activities in S1 Mainly Represent Somatosensory Feedback 94 6.4. Discussion 100 6.4.1. S1 HG Activity Mainly Represents Somatosensory Feedback 100 6.4.2. Further Discussion and Future Direction in BMI 102 6.4.3. Conclusion of Chapter 6 103 CHAPTER 7: Cortical Maps of Somatosensory Function 104 7.1. Introduction 106 7.2. Materials and Methods 110 7.2.1. Participants 110 7.2.2. Direct Cortical Stimulation 114 7.2.3. Classification of Verbal Feedbacks 115 7.2.4. Localization of Electrodes 115 7.2.5. Apparatus 116 7.2.6. Tasks 117 7.2.7. Data Recording and Processing 119 7.2.8. Mapping on the Brain 120 7.2.9. ROI-based Analysis 122 7.3. Results 123 7.3.1. DCS Mapping 123 7.3.2. Three and Four-dimensional HG Mapping 131 7.3.3. Neural Characteristics among Somatosensory-related Areas 144 7.4. Discussion 146 7.4.1. DCS on the Non-Primary Areas 146 7.4.2. Two Streams of Somatosensory System 148 7.4.3. Functional Role of ventral PM 151 7.4.4. Limitation and Perspective 152 7.4.5. Conclusion of Chapter 7 155 PART III. CONCLUSION 156 CHAPTER 8: Conclusion and Perspective 156 8.1. Perspective and Future Work 157 References 160 Abstract in Korean 173Docto