850 research outputs found

    Low-fi skin vision: A case study in rapid prototyping a sensory substitution system

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    We describe the design process we have used to develop a minimal, twenty vibration motor Tactile Vision Sensory Substitution (TVSS) system which enables blind-folded subjects to successfully track and bat a rolling ball and thereby experience 'skin vision'. We have employed a low-fi rapid prototyping approach to build this system and argue that this methodology is particularly effective for building embedded interactive systems. We support this argument in two ways. First, by drawing on theoretical insights from robotics, a discipline that also has to deal with the challenge of building complex embedded systems that interact with their environments; second, by using the development of our TVSS as a case study: describing the series of prototypes that led to our successful design and highlighting what we learnt at each stage

    LifeChair: A Conductive Fabric Sensor-Based Smart Cushion for Actively Shaping Sitting Posture.

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    The LifeChair is a smart cushion that provides vibrotactile feedback by actively sensing and classifying sitting postures to encourage upright posture and reduce slouching. The key component of the LifeChair is our novel conductive fabric pressure sensing array. Fabric sensors have been explored in the past, but a full sensing solution for embedded real world use has not been proposed. We have designed our system with commercial use in mind, and as a result, it has a high focus on manufacturability, cost-effectiveness and adaptiveness. We demonstrate the performance of our fabric sensing system by installing it into the LifeChair and comparing its posture detection accuracy with our previous study that implemented a conventional flexible printed PCB-sensing system. In this study, it is shown that the LifeChair can detect all 11 postures across 20 participants with an improved average accuracy of 98.1%, and it demonstrates significantly lower variance when interfacing with different users. We also conduct a performance study with 10 participants to evaluate the effectiveness of the LifeChair device in improving upright posture and reducing slouching. Our performance study demonstrates that the LifeChair is effective in encouraging users to sit upright with an increase of 68.1% in time spent seated upright when vibrotactile feedback is activated

    Cortical Orchestra Conducted by Purpose and Function

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๋‡Œ๊ณผํ•™์ „๊ณต,2020. 2. ์ •์ฒœ๊ธฐ.์ด‰๊ฐ๊ณผ ์ž๊ธฐ์ˆ˜์šฉ๊ฐ๊ฐ์€ ์šฐ๋ฆฌ์˜ ์ƒ์กด ๋ฐ ์ผ์ƒ์ƒํ™œ์— ์ ˆ๋Œ€์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ค‘์š”ํ•œ ๊ฐ๊ฐ ๊ธฐ๋Šฅ์ด๋‹ค. ๋ง์ดˆ์‹ ๊ฒฝ๊ณ„์—์„œ ์ด ๋‘ ๊ฐ€์ง€ ๊ธฐ๋Šฅ๋“ค์— ํ•„์š”ํ•œ ์ •๋ณด๋ฅผ ์ˆ˜์ง‘ํ•˜๊ณ  ์ „๋‹ฌํ•˜๋Š” ๊ธฐ๊ณ„์  ์ˆ˜์šฉ๊ธฐ ๋ฐ ๊ทธ ๊ตฌ์‹ฌ์„ฑ ์‹ ๊ฒฝ๋“ค์— ๋Œ€ํ•œ ์‹ ํ˜ธ ์ „๋‹ฌ ๋ฉ”์ปค๋‹ˆ์ฆ˜ ๋ฐ ๊ทธ ํŠน์ง•๋“ค์€ ์ƒ๋Œ€์ ์œผ๋กœ ์ž˜ ์•Œ๋ ค์ ธ ์žˆ๋Š” ํŽธ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์ด‰๊ฐ๊ณผ ์ž๊ธฐ์ˆ˜์šฉ๊ฐ๊ฐ์„ ํ˜•์„ฑํ•˜๊ธฐ ์œ„ํ•œ ์ธ๊ฐ„ ๋‡Œ์˜ ํ”ผ์งˆ์—์„œ์˜ ์ •๋ณด ์ฒ˜๋ฆฌ ๋ฉ”์ปค๋‹ˆ์ฆ˜์— ๋Œ€ํ•˜์—ฌ ์šฐ๋ฆฌ๊ฐ€ ํ˜„์žฌ ์•Œ๊ณ  ์žˆ๋Š” ๋ฐ”๋Š” ๊ทนํžˆ ์ผ๋ถ€๋ถ„์ด๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ ์ œ์‹œํ•˜๋Š” ์ผ๋ จ์˜ ์—ฐ๊ตฌ๋“ค์€ ์ธ๊ฐ„ ๋‡Œ ํ”ผ์งˆ ๋‹จ๊ณ„์—์„œ ์ด‰๊ฐ๊ณผ ์ž๊ธฐ์ˆ˜์šฉ๊ฐ๊ฐ์˜ ์ง€๊ฐ์  ์ฒ˜๋ฆฌ๊ณผ์ •์— ๋Œ€ํ•œ ๊ฑฐ์‹œ์  ์‹ ๊ฒฝ๊ณ„ ์ •๋ณด์ฒ˜๋ฆฌ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๋‹ค๋ฃฌ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‡Œํ”ผ์งˆ๋‡ŒํŒŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ธ๊ฐ„ ์ผ์ฐจ ๋ฐ ์ด์ฐจ ์ฒด์„ฑ๊ฐ๊ฐ ํ”ผ์งˆ์—์„œ ์ธ๊ณต์ ์ธ ์ž๊ทน๊ณผ ์ผ์ƒ์ƒํ™œ์—์„œ ์ ‘ํ•  ์ˆ˜ ์žˆ๋Š” ์ž๊ทน์„ ํฌํ•จํ•˜๋Š” ๋‹ค์–‘ํ•œ ์ง„๋™์ด‰๊ฐ๊ฐ ๋ฐ ์งˆ๊ฐ ์ž๊ทน์— ๋Œ€ํ•œ ๊ฑฐ์‹œ์  ์‹ ๊ฒฝ๊ณ„ ์ •๋ณด์ฒ˜๋ฆฌ ํŠน์„ฑ์„ ๋ฐํ˜”๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ์ผ์ฐจ ๋ฐ ์ด์ฐจ ์ฒด์„ฑ๊ฐ๊ฐ ํ”ผ์งˆ์˜ ์ด‰๊ฐ๊ฐ ์ฃผํŒŒ์ˆ˜ ํŠน์ด์ ์ธ ํ•˜์ด-๊ฐ๋งˆ ์˜์—ญ ์‹ ๊ฒฝํ™œ๋™์ด ์ž๊ทน ์ฃผํŒŒ์ˆ˜์— ๋”ฐ๋ผ ๊ฐ๊ฐ ์ƒ์ดํ•œ ์‹œ๊ฐ„์  ๋‹ค์ด๋‚˜๋ฏน์Šค๋ฅผ ๊ฐ€์ง€๊ณ  ๋ณ€ํ™”ํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ด๋Ÿฌํ•œ ํ•˜์ด-๊ฐ๋งˆ ํ™œ๋™์€ ์„ฑ๊ธด ์งˆ๊ฐ๊ณผ ๋ฏธ์„ธํ•œ ์ž…์ž๊ฐ์„ ๊ฐ€์ง„ ์ž์—ฐ์Šค๋Ÿฌ์šด ์งˆ๊ฐ ์ž๊ทน์— ๋Œ€ํ•ด์„œ๋„ ์ง„๋™์ด‰๊ฐ๊ฐ์˜ ๊ฒฝ์šฐ์™€ ์œ ์‚ฌํ•œ ํŒจํ„ด์„ ๋ณด์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋“ค์€ ์ธ๊ฐ„์˜ ์ง„๋™์ด‰๊ฐ๊ฐ์ด ๋งค์šฐ ๋‹จ์ˆœํ•œ ํ˜•ํƒœ์— ์ž๊ทน์ผ์ง€๋ผ๋„ ๋Œ€๋‡Œ ์ฒด์„ฑ๊ฐ๊ฐ ์‹œ์Šคํ…œ์— ์žˆ์–ด ๊ฑฐ์‹œ์ ์ธ ๋‹ค์ค‘ ์˜์—ญ์—์„œ์˜ ๊ณ„์ธต์  ์ •๋ณด์ฒ˜๋ฆฌ๋ฅผ ๋™๋ฐ˜ํ•œ๋‹ค๋Š” ์ ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ธ๊ฐ„์˜ ์›€์ง์ž„๊ณผ ๊ด€๋ จ๋œ ๋‘์ •์—ฝ ์˜์—ญ์—์„œ์˜ ํ•˜์ด-๊ฐ๋งˆ ๋‡Œํ™œ์„ฑ์ด ์ž๊ธฐ์ˆ˜์šฉ๊ฐ๊ฐ๊ณผ ๊ฐ™์€ ๋ง์ดˆ์‹ ๊ฒฝ๊ณ„๋กœ๋ถ€ํ„ฐ์˜ ์ฒด์„ฑ๊ฐ๊ฐ ํ”ผ๋“œ๋ฐฑ์„ ์ฃผ๋กœ ๋ฐ˜์˜ํ•˜๋Š”์ง€, ์•„๋‹ˆ๋ฉด ์›€์ง์ž„ ์ค€๋น„ ๋ฐ ์ œ์–ด๋ฅผ ์œ„ํ•œ ํ”ผ์งˆ ๊ฐ„ ์‹ ๊ฒฝ ํ”„๋กœ์„ธ์Šค์— ๋Œ€ํ•œ ํ™œ๋™์„ ๋ฐ˜์˜ํ•˜๋Š”์ง€๋ฅผ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ, ์ž๋ฐœ์  ์šด๋™ ์ค‘ ๋Œ€๋‡Œ ์šด๋™๊ฐ๊ฐ๋ น์—์„œ์˜ ํ•˜์ด-๊ฐ๋งˆ ํ™œ๋™์€ ์ผ์ฐจ ์ฒด์„ฑ๊ฐ๊ฐํ”ผ์งˆ์ด ์ผ์ฐจ ์šด๋™ํ”ผ์งˆ๋ณด๋‹ค ๋” ์ง€๋ฐฐ์ ์ธ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ ์ด ์—ฐ๊ตฌ์—์„œ๋Š”, ์›€์ง์ž„๊ณผ ๊ด€๋ จ๋œ ์ผ์ฐจ ์ฒด์„ฑ๊ฐ๊ฐํ”ผ์งˆ์—์„œ์˜ ํ•˜์ด-๊ฐ๋งˆ ๋‡Œํ™œ๋™์€ ๋ง์ดˆ์‹ ๊ฒฝ๊ณ„๋กœ๋ถ€ํ„ฐ์˜ ์ž๊ธฐ์ˆ˜์šฉ๊ฐ๊ฐ๊ณผ ์ด‰๊ฐ์— ๋Œ€ํ•œ ์‹ ๊ฒฝ๊ณ„ ์ •๋ณด์ฒ˜๋ฆฌ๋ฅผ ์ฃผ๋กœ ๋ฐ˜์˜ํ•˜๋Š” ๊ฒƒ์„ ๋ฐํ˜”๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ๋“ค์„ ๋ฐ”ํƒ•์œผ๋กœ, ๋งˆ์ง€๋ง‰ ์—ฐ๊ตฌ์—์„œ๋Š” ์ธ๊ฐ„ ๋Œ€๋‡Œ์—์„œ์˜ ์ฒด์„ฑ๊ฐ๊ฐ ์ง€๊ฐ ํ”„๋กœ์„ธ์Šค์— ๋Œ€ํ•œ ๊ฑฐ์‹œ์  ํ”ผ์งˆ ๊ฐ„ ๋„คํŠธ์›Œํฌ๋ฅผ ๊ทœ๋ช…ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด, 51๋ช…์˜ ๋‡Œ์ „์ฆ ํ™˜์ž์—๊ฒŒ์„œ ์ฒด์„ฑ๊ฐ๊ฐ์„ ์œ ๋ฐœํ–ˆ๋˜ ๋‡Œํ”ผ์งˆ์ „๊ธฐ์ž๊ทน ๋ฐ์ดํ„ฐ์™€ 46๋ช…์˜ ํ™˜์ž์—๊ฒŒ์„œ ์ด‰๊ฐ๊ฐ ์ž๊ทน ๋ฐ ์šด๋™ ์ˆ˜ํ–‰ ์ค‘์— ์ธก์ •ํ•œ ๋‡Œํ”ผ์งˆ๋‡ŒํŒŒ ํ•˜์ด-๊ฐ๋งˆ ๋งคํ•‘ ๋ฐ์ดํ„ฐ๋ฅผ ์ข…ํ•ฉ์ ์œผ๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ์ฒด์„ฑ๊ฐ๊ฐ ์ง€๊ฐ ํ”„๋กœ์„ธ์Šค๋Š” ๋Œ€๋‡Œ์—์„œ ๋„“์€ ์˜์—ญ์— ๊ฑธ์ณ ๋ถ„ํฌํ•˜๋Š” ์ฒด์„ฑ๊ฐ๊ฐ ๊ด€๋ จ ๋„คํŠธ์›Œํฌ์˜ ์‹ ๊ฒฝ ํ™œ์„ฑ์„ ์ˆ˜๋ฐ˜ํ•œ๋‹ค๋Š” ๊ฒƒ์„ ์•Œ์•„๋ƒˆ๋‹ค. ๋˜ํ•œ, ๋‡Œํ”ผ์งˆ์ „๊ธฐ์ž๊ทน์„ ํ†ตํ•œ ๋Œ€๋‡Œ ์ง€๋„์™€ ํ•˜์ด-๊ฐ๋งˆ ๋งคํ•‘์„ ํ†ตํ•œ ๋Œ€๋‡Œ ์ง€๋„๋Š” ์„œ๋กœ ์ƒ๋‹นํ•œ ์œ ์‚ฌ์„ฑ์„ ๋ณด์˜€๋‹ค. ํฅ๋ฏธ๋กญ๊ฒŒ๋„, ๋‡Œํ”ผ์งˆ์ „๊ธฐ์ž๊ทน๊ณผ ํ•˜์ด-๊ฐ๋งˆ ํ™œ๋™์„ ์ข…ํ•ฉํ•œ ๋‡Œ์ง€๋„๋“ค๋กœ๋ถ€ํ„ฐ ์ฒด์„ฑ๊ฐ๊ฐ ๊ด€๋ จ ๋‡Œ ์˜์—ญ์˜ ๊ณต๊ฐ„์  ๋ถ„ํฌ๊ฐ€ ์ฒด์„ฑ๊ฐ๊ฐ ๊ธฐ๋Šฅ์— ๋”ฐ๋ผ ์„œ๋กœ ๋‹ฌ๋ž๊ณ , ๊ทธ์— ํ•ด๋‹นํ•˜๋Š” ๊ฐ ์˜์—ญ๋“ค์€ ์„œ๋กœ ๋šœ๋ ทํ•˜๊ฒŒ ๋‹ค๋ฅธ ์‹œ๊ฐ„์  ๋‹ค์ด๋‚˜๋ฏน์Šค๋ฅผ ๊ฐ€์ง€๊ณ  ์ˆœ์ฐจ์ ์œผ๋กœ ํ™œ์„ฑํ™”๋˜์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋“ค์€ ์ฒด์„ฑ๊ฐ๊ฐ์— ๋Œ€ํ•œ ๊ฑฐ์‹œ์  ์‹ ๊ฒฝ๊ณ„ ํ”„๋กœ์„ธ์Šค๊ฐ€ ๊ทธ ์ง€๊ฐ์  ๊ธฐ๋Šฅ์— ๋”ฐ๋ผ ๋šœ๋ ท์ด ๋‹ค๋ฅธ ๊ณ„์ธต์  ๋„คํŠธ์›Œํฌ๋ฅผ ๊ฐ€์ง„๋‹ค๋Š” ์ ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋” ๋‚˜์•„๊ฐ€, ๋ณธ ์—ฐ๊ตฌ์—์„œ์˜ ๊ฒฐ๊ณผ๋“ค์€ ์ฒด์„ฑ๊ฐ๊ฐ ์‹œ์Šคํ…œ์˜ ์ง€๊ฐ-ํ–‰๋™ ๊ด€๋ จ ์‹ ๊ฒฝํ™œ๋™ ํ๋ฆ„์— ๊ด€ํ•œ ์ด๋ก ์ ์ธ ๊ฐ€์„ค์— ๋Œ€ํ•˜์—ฌ ์„ค๋“๋ ฅ ์žˆ๋Š” ์ฆ๊ฑฐ๋ฅผ ์ œ์‹œํ•˜๊ณ  ์žˆ๋‹ค.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, 50โ€“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

    How brain-computer interface technology may improve the diagnosis of the disorders of consciousness: A comparative study

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    ObjectiveClinical assessment of consciousness relies on behavioural assessments, which have several limitations. Hence, disorder of consciousness (DOC) patients are often misdiagnosed. In this work, we aimed to compare the repetitive assessment of consciousness performed with a clinical behavioural and a Brain-Computer Interface (BCI) approach. Materials and methodsFor 7 weeks, sixteen DOC patients participated in weekly evaluations using both the Coma Recovery Scale-Revised (CRS-R) and a vibrotactile P300 BCI paradigm. To use the BCI, patients had to perform an active mental task that required detecting specific stimuli while ignoring other stimuli. We analysed the reliability and the efficacy in the detection of command following resulting from the two methodologies. ResultsOver repetitive administrations, the BCI paradigm detected command following before the CRS-R in seven patients. Four clinically unresponsive patients consistently showed command following during the BCI assessments. ConclusionBrain-Computer Interface active paradigms might contribute to the evaluation of the level of consciousness, increasing the diagnostic precision of the clinical bedside approach. SignificanceThe integration of different diagnostic methods leads to a better knowledge and care for the DOC

    Tactile Data Entry for Extravehicular Activity

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    In the task-saturated environment of extravehicular activity (EVA), an astronaut's ability to leverage suit-integrated information systems is limited by a lack of options for data entry. In particular, bulky gloves inhibit the ability to interact with standard computing interfaces such as a mouse or keyboard. This paper presents the results of a preliminary investigation into a system that permits the space suit gloves themselves to be used as data entry devices. Hand motion tracking is combined with simple finger gesture recognition to enable use of a virtual keyboard, while tactile feedback provides touch-based context to the graphical user interface (GUI) and positive confirmation of keystroke events. In human subject trials, conducted with twenty participants using a prototype system, participants entered text significantly faster with tactile feedback than without (p = 0.02). The results support incorporation of vibrotactile information in a future system that will enable full touch typing and general mouse interactions using instrumented EVA gloves

    Biomechanical Texture Coding and Transmission of Texture Information in Rat Whiskers

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    Classically, texture discrimination has been thought to be based on โ€˜globalโ€™ codes, i.e. frequency (signal analysis based on Fourier analysis) or intensity (signal analysis based on averaging), which both rely on integration of the vibrotactile signal across time and/or space. Recently, a novel โ€˜localโ€™ coding scheme based on the waveform of frictional movements, discrete short- lasting kinematic events (i.e. stick-slip movements called slips) has been formulated. In the first part of my study I performed biomechanical measurements of relative movements of a rat vibrissa across sandpapers of different roughness. My major finding is that the classic global codes convey some information about texture identity but are consistently outperformed by the slip-based local code. Moreover, the slip code also surpasses the global ones in coding for active scanning parameters. This is remarkable as it suggests that the slip code would explicitly allow the whisking rat to optimize perception by selecting goal-specific scanning strategies. I therefore provide evidence that short stick-slip events may contribute to the perceptual mechanism by which rodent vibrissa code surface roughness. In the second part, I studied the biomechanics of how such events are transmitted from tip to follicle where mechano-transduction occurs. For this purpose, ultra-fast videography recording of the entire beam of a plucked rat whisker rubbing across sandpaper was employed. I found that slip events are conveyed almost instantly from tip to follicle while amplifying moments by a factor of about 1000. From these results, I argue that the mechanics of the whisker serve as a passive ampli๏ฌcation device that faithfully represents stick-slip events to the neuronal receptors. Using measures of correlation, I moreover found that amongst the kinematic 8 variables, acceleration portrays dynamic variables (forces) best. The time series of acceleration at the base of the whisker provided a fair proxy to the time series of forces (dynamical variables) acting on the whisker base. Acceleration measurements (easily done via videography) may therefore provide an access to at least the relative amplitude of forces. This may be important for future work in behaving animals, where dynamical variables are notoriously difficult to measure
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