EEG oscillations during caress-like affective haptic elicitation

Emotion recognition while applying cosmetic cream using deep learning from EEG data; cross-subject analysis

We report a deep learning-based emotion recognition method using EEG data collected while applying cosmetic creams. Four creams with different textures were randomly applied, and they were divided into two classes, “like (positive)” and “dislike (negative)”, according to the preference score given by the subject. We extracted frequency features using well-known frequency bands, i.e., alpha, beta and low and high gamma bands, and then we created a matrix including frequency and spatial information of the EEG data. We developed seven CNN-based models: (1) inception-like CNN with four-band merged input, (2) stacked CNN with four-band merged input, (3) stacked CNN with four-band parallel input, and stacked CNN with single-band input of (4) alpha, (5) beta, (6) low gamma, and (7) high gamma. The models were evaluated by the Leave-One-Subject-Out Cross-Validation method. In like/dislike two-class classification, the average accuracies of all subjects were 73.2%, 75.4%, 73.9%, 68.8%, 68.0%, 70.7%, and 69.7%, respectively. We found that the classification performance is higher when using multi-band features than when using single-band feature. This is the first study to apply a CNN-based deep learning method based on EEG data to evaluate preference for cosmetic creams.

Increased functional connectivity within alpha and theta frequency bands in dysphoria: A resting-state EEG study

BACKGROUND

The understanding of neurophysiological correlates underlying the risk of developing depression may have a significant impact on its early and objective identification. Research has identified abnormal resting-state electroencephalography (EEG) power and functional connectivity patterns in major depression. However, the entity of dysfunctional EEG dynamics in dysphoria is yet unknown.

METHODS

32-channel EEG was recorded in 26 female individuals with dysphoria and in 38 age-matched, female healthy controls. EEG power spectra and alpha asymmetry in frontal and posterior channels were calculated in a 4-minute resting condition. An EEG functional connectivity analysis was conducted through phase locking values, particularly mean phase coherence.

RESULTS

While individuals with dysphoria did not differ from controls in EEG spectra and asymmetry, they exhibited dysfunctional brain connectivity. Particularly, in the theta band (4-8 Hz), participants with dysphoria showed increased connectivity between right frontal and central areas and right temporal and left occipital areas. Moreover, in the alpha band (8-12 Hz), dysphoria was associated with increased connectivity between right and left prefrontal cortex and between frontal and central-occipital areas bilaterally.

LIMITATIONS

All participants belonged to the female gender and were relatively young. Mean phase coherence did not allow to compute the causal and directional relation between brain areas.

CONCLUSIONS

An increased EEG functional connectivity in the theta and alpha bands characterizes dysphoria. These patterns may be associated with the excessive self-focus and ruminative thinking that typifies depressive symptoms. EEG connectivity patterns may represent a promising measure to identify individuals with a higher risk of developing depression.

Mirror-touch experiences in the infant brain

Several adult studies have proved the existence of a shared neural circuit in the somatosensory cortices that responds to both the body being touched and the sight of the body being touched. Despite the fundamental role of touch in infancy, the existence of similar visuo-tactile mirroring processes, supporting both felt and seen touch, still needs an in-depth empirical investigation. To this aim, we explored 8-month-olds mu desynchronization over somatosensory sites in response to felt and observed touch in a live experimental setting. EEG desynchronization (6-8 Hz mu frequency range) was measured during three experimental conditions: i) infants were stroked on their right hand by a parent (Touch condition); ii) infants observed a right hand being stroked (Observation Touch condition); iii) infants observed a right hand moving over the left hand without making contact (Action Control condition). Mu desynchronization of somatosensory sites contralateral to the hand being stroked emerged in response to both Touch and Observation Touch conditions, but not in the Action control condition. Further, greater mu desynchronization was found in the Touch and Observation Touch conditions as compared to the Action control condition. Our results highlight the early involvement of a shared somatosensory system, likely supporting infants' understanding of others' 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.

EEG Processing to Discriminate Transitive-Intransitive Motor Imagery Tasks: Preliminary Evidences using Support Vector Machines

It is known that brain dynamics significantly changes during motor imagery tasks of upper limb involving different kind of interactions with an object. Nevertheless, an automatic discrimination of transitive (i.e., actions involving an object) and intransitive (i.e., meaningful gestures that do not include the use of objects) imaginary actions using EEG dynamics has not been performed yet. In this study we exploit measures of EEG spectra to automatically discern between imaginary transitive and intransitive movements of the upper limb. To this end, nonlinear support vector machine algorithms are used to properly combine EEG-derived features, while a recursive feature elimination procedure highlights the most discriminant cortical regions and associated EEG frequency oscillations. Results show the significance of $\gamma ( 30 -45$ Hz) oscillations over the fronto-occipital and ipsilateral-parietal areas for the automatic classification of transitive-intransitive imaginary upper limb movements with a satisfactory accuracy of 70.97%.

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

In this study, we investigated brain dynamics from electroencephalographic (EEG) signals during affective tactile stimulation conveyed by the dynamical contact with different fabrics. Thirty-three healthy subjects (16 females) were enrolled to interact with a haptic device able to mimic caress-like stimuli conveyed by strips of different fabrics moved back and forth at different velocities. Specifically, two velocity levels (i.e., 9.4 and 65 mm/sec) and two kinds of fabric (i.e., burlap and silk) were selected to deliver pleasant and unpleasant affective elicitations, according to subjects' self-assessment. EEG power spectra and functional connectivity were then calculated and analyzed. Experimental results, reported in terms of p-value topographic maps, demonstrated that caresses administered through unpleasant fabrics increased brain activity in the θ (4-8 Hz), α (8-14 Hz), and β (14-30 Hz) bands, whereas the use of pleasant fabrics enhanced functional connections in specific areas (e.g., frontal, occipital, and temporal cortices) depending on the oscillations frequency and caressing velocity. Furthermore, we adopted K-NN algorithms to automatically recognize the pleasantness of the haptic stimulation at a single-subject level using EEG power spectra, achieving a recognition accuracy up to 74.24%. Finally, we showed how brain oscillation power in the α and β bands over contralateral frontal- and central-cortex were the most informative features characterizing the pleasantness of a tactile stimulus on the forearm.

EEG Hyperconnectivity Study on Saxophone Quartet Playing in Ensemble

A professional quartet of saxophonists playing in ensemble provides a perfect scenario to study the eventual occurrence of synchronous oscillatory brain activity across subjects. Here, we applied hyperscanning methodologies for simultaneously recordings of electroencephalographic (EEG) signals from four professional saxophonists while they observ an audiovideo recording of their own previous musical performance. An ad-hoc musical composition was written for the study. At debriefing, the subjects were asked to answer two questionnaires to assess their empathy trait and the musical leadership. In order to estimate the hyperconnectivity of each musician we proposed a measure which combines phase synchronization index of brain oscillations and graph theory framework. The inter-connectivity level of each musician was statistically compared. Statistical results revealed a significant lower hyperconnectivity in the left Brodmann area 44 for the Soprano with respect to the other three members. Recent theories attributed this brain region (Broca's area) to music generation, empathy processes and communication. We hypothesize a relationship between brain-to-brain connectivity level and the musical role within the quartet.