Evaluation Of Automated Eye Blink Artefact Removal Using Stacked Dense Autoencoder

The presence of artefacts in Electroencephalograph (EEG) signals can have a considerable impact on the information they portray. In this comparative study, the automated removal of eye blink artefacts using the constrained latent representation of a stacked dense autoencoders (SDAE) and comparing its ability to that of the manual independent component analysis (ICA) approach was evaluated. A comparative evaluation of 5 stacked dense autoencoder architectures lead to a chosen architecture for which the ability to automatically detect and remove eye blink artefacts were both statistically and humanistically evaluated. The ability of the stacked dense autoencoder was statistically evaluated with the manual approach of ICA using the correlation coefficient, a comparative affect on the SNR using both approaches and a humanistic evaluation using visual inspections of the components of the stacked dense autoencoder reconstruction to that of the post ICA reconstruction where an inverse RMSE allowed for a further statistical evaluation of this comparison. It was found that the stacked dense autoencoder was unable to reconstruct random signal segments in any meaningful capacity when compared to that of ICA

Role of Emotion and Attention in Variations in Sexual Desire

Thesis (PhD) - Indiana University, Psychology, 2006Little is known about why individuals vary in their levels of sexual desire. Information processing models, like Barlow's model of sexual functioning, suggest that individuals with higher sexual desire attend more and respond with more pleasant emotions to sexual cues than individuals with lower levels of sexual desire. In this study, 66 participants (33 female) completed a dot detection task, viewing time measure, and evoked response potential (ERP) measures of attention captured by sexual stimuli, and they completed startle eyeblink modulation, retrahens auriculum modulation, stimulus ratings, and electroencephalography power band measures indexing the valence of emotional response to affective stimuli. Participants with high levels of sexual desire were slower to detect targets in the dot detection task that replaced sexual pictures and in the presence of any sexual stimuli and also evinced higher ERP responses to all emotional stimuli. However, sexual desire groups did not differ in their psychophysiological measures of affective modulation nor in their ratings of sexual stimuli. The results suggest that the amount of attention captured by sexual stimuli is a stronger predictor of a person's sexual desire level than the valence of the emotional responses elicited by such stimuli

Wearable Brain-Computer Interface Instrumentation for Robot-Based Rehabilitation by Augmented Reality

An instrument for remote control of the robot by wearable brain-computer interface (BCI) is proposed for rehabilitating children with attention-deficit/hyperactivity disorder (ADHD). Augmented reality (AR) glasses generate flickering stimuli, and a single-channel electroencephalographic BCI detects the elicited steady-state visual evoked potentials (SSVEPs). This allows benefiting from the SSVEP robustness by leaving available the view of robot movements. Together with the lack of training, a single channel maximizes the device's wearability, fundamental for the acceptance by ADHD children. Effectively controlling the movements of a robot through a new channel enhances rehabilitation engagement and effectiveness. A case study at an accredited rehabilitation center on ten healthy adult subjects highlighted an average accuracy higher than 83%, with information transfer rate (ITR) up to 39 b/min. Preliminary further tests on four ADHD patients between six- and eight-years old provided highly positive feedback on device acceptance and attentional performance

Positive emotional reactions to loved names

This is a non-reviewed, non-corrected version. It doesn't include figures. The final version of the manuscript can be consulted at DOI: 10.1111/psyp.13363Studies concerning personal attachment have successfully used loved familiar faces to prompt positive affective and physiological reactions. On the other hand, the processing of emotional words has been associated with a pattern of peripheral and central physiological responses equivalent to those found with affective pictures. The objective of this study was to assess whether the passive viewing of loved names would produce a pattern of subjective and physiological reactivity similar to that produced by the passive viewing of loved faces. The results showed that compared to neutral (unknown) and famous names, loved names produced a biphasic pattern of heart rate deceleration-acceleration, heightened skin conductance and zygomaticus muscle activity, inhibition of corrugator muscle activity, and potentiation of the startle reflex response. This pattern of physiological responses was accompanied by subjective reports of higher positive affect and arousal for loved names than for neutral and famous ones. These findings highlight not only the similarity but also the differences between the affective processing of identity recognition by loved faces and names

A systematic review and meta-analysis of the evidence for unaware fear conditioning

Whether fear conditioning can take place without contingency awareness is a topic of continuing debate and conflicting findings have been reported in the literature. This systematic review provides a critical assessment of the available evidence. Specifically, a search was conducted to identify articles reporting fear conditioning studies in which the contingency between conditioned stimuli (CS) and the unconditioned stimulus (US) was masked, and in which CS-US contingency awareness was assessed. A systematic assessment of the methodological quality of the included studies (k = 41) indicated that most studies suffered from methodological limitations (i.e., poor masking procedures, poor awareness measures, researcher degrees of freedom, and trial-order effects), and that higher quality predicted lower odds of studies concluding in favor of contingency unaware fear conditioning. Furthermore, meta-analytic moderation analyses indicated no evidence for a specific set of conditions under which contingency unaware fear conditioning can be observed. Finally, funnel plot asymmetry and p-curve analysis indicated evidence for publication bias. We conclude that there is no convincing evidence for contingency unaware fear conditioning

A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system’s performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear

Automatic Stress Classification With Pupil Diameter Analysis

This article proposes a method based on wavelet transform and neural networks for relating pupillary behavior to psychological stress. The proposed method was tested by recording pupil diameter and electrodermal activity during a simulated driving task. Self-report measures were also collected. Participants performed a baseline run with the driving task only, followed by three stress runs where they were required to perform the driving task along with sound alerts, the presence of two human evaluators, and both. Self-reports and pupil diameter successfully indexed stress manipulation, and significant correlations were found between these measures. However, electrodermal activity did not vary accordingly. After training, the four-way parallel neural network classifier could guess whether a given unknown pupil diameter signal came from one of the four experimental trials with 79.2% precision. The present study shows that pupil diameter signal has good discriminating power for stress detection

Musical training predicts cerebello-hippocampal coupling during music listening.

Cerebello-hippocampal interactions occur during accurate spatiotemporal prediction of movements. In the context of music listening, differences in cerebello-hippocampal functional connectivity may result from differences in predictive listening accuracy. Using functional MRI, we studied differences in this network between 18 musicians and 18 nonmusicians while they listened to music. Musicians possess a predictive listening advantage over nonmusicians, facilitated by strengthened coupling between produced and heard sounds through lifelong musical experience. Thus, we hypothesized that musicians would exhibit greater functional connectivity than nonmusicians as a marker of accurate online predictions during music listening. To this end, we estimated the functional connectivity between cerebellum and hippocampus as modulated by a perceptual measure of the predictability of the music. Results revealed increased predictability-driven functional connectivity in this network in musicians compared with nonmusicians, which was positively correlated with the length of musical training. Findings may be explained by musicians’ improved predictive listening accuracy. Our findings advance the understanding of cerebellar integrative function.Peer reviewe

Rethinking Eye-blink: Assessing Task Difficulty through Physiological Representation of Spontaneous Blinking

Continuous assessment of task difficulty and mental workload is essential in improving the usability and accessibility of interactive systems. Eye tracking data has often been investigated to achieve this ability, with reports on the limited role of standard blink metrics. Here, we propose a new approach to the analysis of eye-blink responses for automated estimation of task difficulty. The core module is a time-frequency representation of eye-blink, which aims to capture the richness of information reflected on blinking. In our first study, we show that this method significantly improves the sensitivity to task difficulty. We then demonstrate how to form a framework where the represented patterns are analyzed with multi-dimensional Long Short-Term Memory recurrent neural networks for their non-linear mapping onto difficulty-related parameters. This framework outperformed other methods that used hand-engineered features. This approach works with any built-in camera, without requiring specialized devices. We conclude by discussing how Rethinking Eye-blink can benefit real-world applications