ERP evidence of tactile texture processing: Effects of roughness and movement

Ballesteros, Soledad; Muñoz, Francisco; Sebastián, Manuel; García, Beatriz Defez; Avilés, José Manuel Reales

doi:10.1109/whc.2009.4810901

Cited by 22 publications

(19 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, for both the rub and tap conditions, the sensitivity of discriminating a textured surface increased with the increase in roughness level. This reflects the importance of the roughness in the perception and discrimination of textured surfaces as has been shown previously 25,26 . Additionally, the cortical processing of roughness discrimination follows two schemes, cognitive and sensory-based processing.…”

Section: Discussionsupporting

confidence: 79%

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

Eldeeb

Weber

Ting

et al. 2020

Sci Rep

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionsupporting

confidence: 79%

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

Eldeeb

Weber

Ting

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…3F): the stimulus roughness linearly modulated the latency of frontal potentials, i.e., smoother surfaces generated delayed potentials. The prefrontal activation could be a result of some level of cognitive-based processing as shown in (Burton et al, 1997) and therefore the observed behavior could be related to the ability of the subject to recognize fine/coarse surfaces: a coarse surface is easier to discriminate and thus might evoke an earlier response (Ballesteros et al, 2009). Previous studies described the involvement of P240 also in different cognitive tasks and experimental settings.…”

Section: Roughness Modulation In the Time Domainmentioning

confidence: 95%

“…Furthermore, the EEG allows monitoring Somatosensory-Evoked Potentials (SEPs), which represent the direct cortical response of the Central Nervous System to sensory stimuli (Allison et al, 1992;Salenius et al, 1997). SEP characterization is also fundamental to identify which components are involved in the roughness discrimination task and which ones reproduce the same features across stimuli (Ballesteros et al, 2009;Munoz et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Bilateral cortical representation of tactile roughness

et al. 2018

View full text Add to dashboard Cite

Roughness is the most important feature for texture discrimination. Here we investigate how the bilateral cortical representation of touch is modulated by tactile roughness by analyzing the neural responses elicited by stimuli with various coarseness levels ranging from fine to medium. A prolonged stimulation was delivered to 10 healthy subjects by passively sliding tactile stimuli under the fingertip while recording the EEG to study the modulation of Somatosensory Evoked Potentials (SEPs) as well as activity in the theta and alpha bands. Elicited long-latency SEPs, namely bilateral P100-N140 and frontal P240 were consistent across stimuli. On the contrary, the temporal lag N140 - P240 was nonlinearly modulated both in contralateral and ipsilateral sides, in agreement with literature. Using a time-frequency analysis approach, we identified a theta band power increase in the [0 0.5]s interval and a partially overlapped power decrease in the alpha band which lasted throughout the stimulation. The estimated time these two phenomena were overlapped was comparable across stimuli, whereas a linear decrease in alpha band amplitude was reported when increasing the stimulus roughness in both contralateral and ipsilateral sides. This study showed that the selected tactile stimuli generated physiological bilateral responses that were modulated in a diversified way according to the stimulus roughness and side. Specifically, we identified sensory processing features (i.e., theta and alpha time overlap) invariant to the stimulus roughness (i.e., associated to a basic cortical mechanism of touch) and roughness-dependent cortical outputs comparable in the contralateral and ipsilateral sides that confirm a bilateral processing of tactile information.

show abstract

“…Indeed, the level of friction or the degree of roughness affects the characteristics of this perception [3] and, consequently, the brain state [4]. The activation of somatosensory cortex, posterior cingulate cortex, central-parietal sites, lateral parietal operculum, insula, lateral prefrontal cortex, and supplementary motor area have been reported by several research during tactile roughness discrimination [5][6][7]. Some differences were also observed between event-related potential (ERP) components (N100, N200, and P300) during touching surfaces with different roughness levels [8].…”

Section: Introductionmentioning

confidence: 91%

Detection of Static, Dynamic, and No Tactile Friction Based on Non-linear dynamics of EEG Signals: A Preliminary Study

Baghdadi

Amiri

2020

Preprint

View full text Add to dashboard Cite

Touching an object leads to a frictional interaction between the skin and the object. There are two kinds of friction: the first contact that leads to static friction and the dragging phase that leads to dynamic friction. No study has been performed to show the effect of friction type on EEG signals.The main goal of the current study is to investigate the effect of tactile friction on non-linear features of EEG signals. Participants performed a tactile task that each of its trials had three states: the sensation of 1) static friction, 2) dynamic friction, and 3) no friction. During the experiment, EEG signals were recorded, and different linear and non-linear EEG indices were extracted and analyzed to find the effect of the tactile friction on EEG signals.Linear features such as spectral features were not a good choice to distinguish between the states.However, non-linear features such as Lyapunov exponent, Higuchi's dimension, and Hurst exponent had the potential to separate the mentioned states. Results also showed signs of predictability (negative Lyapunov exponent) in the signals recorded during dynamic friction and the existence of long-range dependency (memory) in EEG signals recorded during all states. The complexity of the tactile system in Theta band was also higher than the Delta band.The results of this research not only increase our knowledge about brain non-linear dynamics in response to tactile friction but also lead to a design of a preliminary system that can automatically detect friction between the skin and surfaces.

show abstract

ERP evidence of tactile texture processing: Effects of roughness and movement

Cited by 22 publications

References 29 publications

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

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

Bilateral cortical representation of tactile roughness

Detection of Static, Dynamic, and No Tactile Friction Based on Non-linear dynamics of EEG Signals: A Preliminary Study

Contact Info

Product

Resources

About