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

Eldeeb, Safaa; Weber, Douglas J.; Ting, Jordyn E; Demir, Andaç; Erdoğmuş, Deni̇z; Akçakaya, Murat

doi:10.1038/s41598-020-77439-7

Cited by 10 publications

(15 citation statements)

References 35 publications

(64 reference statements)

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“…Power changes in theta, alpha, and beta frequency bands for the C3 electrode during discrimination and response periods, as well as the changes in ratios of frequencies, are in line with previous studies of tactile function. Tactile discrimination is often associated with changes in EEG activity recorded from the electrode C3/4, namely with the appearance of event related potentials or power changes in alpha and beta frequency bands [17][18][19][20][21][22][23][24][28][29]. For example, a relevant contribution of mu (8-15 Hz) and beta (16-30 Hz) frequency bands has been demonstrated for texture discrimination using a support vector machine classi er in electrodes recording from primary and secondary somatosensory cortex [23].…”

Section: Discussionmentioning

confidence: 99%

“…Tactile discrimination is often associated with changes in EEG activity recorded from the electrode C3/4, namely with the appearance of event related potentials or power changes in alpha and beta frequency bands [17][18][19][20][21][22][23][24][28][29]. For example, a relevant contribution of mu (8-15 Hz) and beta (16-30 Hz) frequency bands has been demonstrated for texture discrimination using a support vector machine classi er in electrodes recording from primary and secondary somatosensory cortex [23]. In addition, other complex functions such as orienting attention to an upcoming tactile event have also been associated with alpha and beta band modulation in sensorimotor regions [30].…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies of EEG activity in other tactile discrimination paradigms indicate the engagement of a complex network of regions involving the somatosensory cortex, prefrontal cortex, and the parietal cortex, typically in alpha, mu, and beta frequency bands [17][18][19][20][21][22][23][24]. These studies have, mostly, explored EEG correlates of tactile processing using stimulation current, textures, or vibratory stimuli; due to their ability to generate EEG evoked responses.…”

Section: Introductionmentioning

confidence: 99%

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Neurophysiological correlates of tactile width discrimination in humans

Pais-Vieira

Mehrab

et al. 2022

Preprint

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Tactile information processing requires the integration of sensory, motor, and cognitive information. Width discrimination has been extensively studied in rodents, but not in humans. Here, we describe Electroencephalography (EEG) signals in humans performing a tactile width discrimination task. Comparison of changes in Spectral Power Density during two different periods of the task corresponding to the discrimination of the tactile stimulus and the motor response, revealed the engagement of a complex network associated with the electrodes recording from fronto-temporo-parieto-occipital areas and across multiple frequency bands. Analysis of ratios of higher [Ratio1:(0.5-20Hz)/(0.5-45Hz)] or lower frequencies [Ratio2: (0.5-4.5Hz)/(0.5-9Hz)], also revealed that the periods of tactile discrimination and motor response, were accompanied by changes in these ratios. Further analysis of the tactile discrimination period demonstrated that ratios of frequencies recorded from electrodes located in fronto-parietal regions were correlated to tactile width discrimination performance between subjects, while the dynamics in parietal and occipital electrodes were correlated to the changes in performance within subjects. These results support the notion that a bilateral asymmetrical widespread network is associated with tactile width discrimination.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neurophysiological correlates of tactile width discrimination in humans

Pais-Vieira

Mehrab

et al. 2022

Preprint

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“…Electroencephalography (EEG) studies showed that EEG contains patterns that change when the amount or roughness of tactile feedback changes [32,[43][44][45][46][47][48][49][50][51][52][53]. Genna et al showed an increase in the power of the theta band (4-7 Hz) for 500 ms after the stimulus onset, which was distributed across the cortex, with a focus around the contralateral somatosensory region.…”

Section: Introductionmentioning

confidence: 99%

EEG guided electrical stimulation parameters generation from texture force profiles

Eldeeb¹,

Akçakaya²

2022

J. Neural Eng.

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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.

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“…On the contrary, limited number of studies have employed deep learning in neurohaptics. An attempt was made to classify the surface texture during active exploration task ( Eldeeb et al, 2020 ) on a single EEG trial basis using Support Vector Machine (SVM) with features that are manually extracted from the raw EEG data. Another study developed a CNN model to identify the type of haptic interaction (passive vs. active) during visuo-haptic task on a single EEG trial basis as well ( Alsuradi and Eid, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

An ensemble deep learning approach to evaluate haptic delay from a single trial EEG data

Alsuradi

Eid

2022

Front. Robot. AI

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Haptic technologies are becoming increasingly valuable in Human-Computer interaction systems as they provide means of physical interaction with a remote or virtual environment. One of the persistent challenges in tele-haptic systems, communicating haptic information over a computer network, is the synchrony of the delivered haptic information with the rest of the sensory modalities. Delayed haptic feedback can have serious implications on the user performance and overall experience. Limited research efforts have been devoted to studying the implication of haptic delay on the human neural response and relating it to the overall haptic experience. Deep learning could offer autonomous brain activity interpretation in response to a haptic experience such as haptic delay. In this work, we propose an ensemble of 2D CNN and transformer models that is capable of detecting the presence and redseverity of haptic delay from a single-trial Electroencephalography data. Two EEG-based experiments involving visuo-haptic interaction tasks are proposed. The first experiment aims to collect data for detecting the presence of haptic delay during discrete force feedback using a bouncing ball on a racket simulation, while the second aims to collect data for detecting the severity level (none, mild, moderate, severe) of the haptic delay during continuous force feedback via grasping/releasing of an object in a bucket. The ensemble model showed a promising performance with an accuracy of 0.9142 ± 0.0157 for detecting haptic delay during discrete force feedback and 0.6625 ± 0.0067 for classifying the severity of haptic delay during continuous force feedback (4 levels). These results were obtained based on training the model with raw EEG data as well as their wavelet transform using several wavelet kernels. This study is a step forward towards developing cognitive evaluation of the user experience while interaction with haptic interfaces.

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

Cited by 10 publications

References 35 publications

Neurophysiological correlates of tactile width discrimination in humans

Neurophysiological correlates of tactile width discrimination in humans

EEG guided electrical stimulation parameters generation from texture force profiles

An ensemble deep learning approach to evaluate haptic delay from a single trial EEG data

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