ArticleScientific reports2020
EEG-based trial-by-trial texture classification during active touch.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Evaluating haptic experience using EEG and deep learning across multiple modalities: linking stimulus and self-reports.Frontiers in neuroscience · 2026Article
- Electroencephalography-based neural indicators of texture preference for cosmetic formulations.Frontiers in neuroscience · 2025Article
- Fronto-Central Changes in Multiple Frequency Bands in Active Tactile Width Discrimination Task.Brain sciences · 2024Article
- Processing the fine-grained features of tactile textures involves the primary somatosensory cortex.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- EEG-based functional connectivity for tactile roughness discrimination.Cognitive neurodynamics · 2023Article
- Neural correlates of thermal stimulation during active touch.Frontiers in neuroscience · 2023Article
- Neurophysiological correlates of tactile width discrimination in humans.Frontiers in human neuroscience · 2023Article
- EEG guided electrical stimulation parameters generation from texture force profiles.Journal of neural engineering · 2022Article
- An ensemble deep learning approach to evaluate haptic delay from a single trial EEG data.Frontiers in robotics and AI · 2022Article
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Authors and funding
6 authors.
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Abstract
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.
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