Brain Dynamics Induced by Pleasant/Unpleasant Tactile Stimuli Conveyed by Different Fabrics

Evaluation of Post-Stroke Impairment in Fine Tactile Sensation by Electroencephalography (EEG)-Based Machine Learning

Electroencephalography (EEG)-based measurements of fine tactile sensation produce large amounts of data, with high costs for manual evaluation. In this study, an EEG-based machine-learning (ML) model with support vector machine (SVM) was established to automatically evaluate post-stroke impairments in fine tactile sensation. Stroke survivors (n = 12, stroke group) and unimpaired participants (n = 15, control group) received stimulations with cotton, nylon, and wool fabrics to the different upper limbs of a stroke participant and the dominant side of the control. The average and maximal values of relative spectral power (RSP) of EEG in the stimulations were used as the inputs to the SVM-ML model, which was first optimized for classification accuracies for different limb sides through hyperparameter selection (γ, C) in radial basis function (RBF) kernel and cross-validation during cotton stimulation. Model generalization was investigated by comparing accuracies during stimulations with different fabrics to different limbs. The highest accuracies were achieved with (γ = 21, C = 23) for the RBF kernel (76.8%) and six-fold cross-validation (75.4%), respectively, in the gamma band for cotton stimulation; these were selected as optimal parameters for the SVM-ML model. In model generalization, significant differences in the post-stroke fabric stimulation accuracies were shifted to higher (beta/gamma) bands. The EEG-based SVM-ML model generated results similar to manual evaluation of cortical responses to fabric stimulations; this may aid automatic assessments of post-stroke fine tactile sensations.

The Role of Haptic Stimuli on Affective Reading: a Pilot Study

The affective role of touch has opened new perspectives in human-machine interaction. This paper presents an emotion recognition algorithm to investigate the role of tactile stimuli conveyed through a wearable haptic system during affective reading. To this end, a group of 32 healthy volunteers underwent an emotional stimulation by reading affective texts, with and without the concurrent presence of pleasant haptic stimuli. Throughout the experiment, autonomic nervous system dynamics was quantified through heart rate variability (HRV) and electrodermal activity (EDA) analyses. EDA and HRV features were then used as input of a SVM-RFE learning algorithm for an automatic recognition of neutral and arousing texts. The affective recognition of the reading was performed in the presence or absence of the haptic stimulation. Results show that the affective perception induced by the neutral and arousing reading were discriminated with a significantly improved accuracy (+14.5%) when a caress-like haptic stimulus was conveyed to the user.