Decoding Attentional State to Faces and Scenes Using EEG Brainwaves

Attention is the ability to facilitate processing perceptually salient information while blocking the irrelevant information to an ongoing task. For example, visual attention is a complex phenomenon of searching for a target while filtering out competing stimuli. In the present study, we developed a new Brain-Computer Interface (BCI) platform to decode brainwave patterns during sustained attention in a participant. Scalp electroencephalography (EEG) signals using a wireless headset were collected in real time during a visual attention task. In our experimental protocol, we primed participants to discriminate a sequence of composite images. Each image was a fair superimposition of a scene and a face image. The participants were asked to respond to the intended subcategory (e.g., indoor scenes) while withholding their responses for the irrelevant subcategories (e.g., outdoor scenes). We developed an individualized model using machine learning techniques to decode attentional state of the participant based on their brainwaves. Our model revealed the instantaneous attention towards face and scene categories. We conducted the experiment with six volunteer participants. The average decoding accuracy of our model was about 77%, which was comparable with a former study using functional magnetic resonance imaging (fMRI). The present work was an attempt to reveal momentary level of sustained attention using EEG signals. The platform may have potential applications in visual attention evaluation and closed-loop brainwave regulation in future

Brain informed transfer learning for categorizing construction hazards

A transfer learning paradigm is proposed for "knowledge" transfer between the human brain and convolutional neural network (CNN) for a construction hazard categorization task. Participants' brain activities are recorded using electroencephalogram (EEG) measurements when viewing the same images (target dataset) as the CNN. The CNN is pretrained on the EEG data and then fine-tuned on the construction scene images. The results reveal that the EEG-pretrained CNN achieves a 9 % higher accuracy compared with a network with same architecture but randomly initialized parameters on a three-class classification task. Brain activity from the left frontal cortex exhibits the highest performance gains, thus indicating high-level cognitive processing during hazard recognition. This work is a step toward improving machine learning algorithms by learning from human-brain signals recorded via a commercially available brain-computer interface. More generalized visual recognition systems can be effectively developed based on this approach of "keep human in the loop"

Traditional SETA No More: Investigating the Intersection Between Cybersecurity and Cognitive Neuroscience

We investigated the role automated behavior plays in contributing to security breaches. Using different forms of phishing, combined with multiple neurophysiological tools, we were able to more fully understand the approaches participants took when they engaged with a phishing campaign. The four participants of this pilot study ranged in their individual characteristics of gender and IT experience while controlling for age. It seems the biggest factor for awareness and successfully resisting a phishing campaign may be proximity of security training to engagement with that campaign. Neurophysiological tools helped illustrate the thought processes behind participants’ statements and actions; combined with consideration of individual characteristics, these tools help shed more light on human behavior. In the future, we plan to further enhance our testing environment by incorporating an emergent model that considers work task complexity and incorporate more industry participants with a range of IT experience

The Impact of Changes to Daylight Illumination level on Architectural experience in Offices Based on VR and EEG

This study investigates the influence of varying illumination levels on architectural experiences by employing a comprehensive approach that combines self-reported assessments and neurophysiological measurements. Thirty participants were exposed to nine distinct illumination conditions in a controlled virtual reality environment. Subjective assessments, collected through questionnaires in which participants were asked to rate how pleasant, interesting, exciting, calming, complex, bright and spacious they found the space. Objective measurements of brain activity were collected by electroencephalogram (EEG). Data analysis demonstrated that illumination levels significantly influenced cognitive engagement and different architectural experience indicators. This alignment between subjective assessment and EEG data underscores the relationship between illuminance and architectural experiences. The study bridges the gap between quantitative and qualitative assessments, providing a deeper understanding of the intricate connection between lighting conditions and human responses. These findings contribute to the enhancement of environmental design based on neuroscientific insights, emphasizing the critical role of well-considered daylighting design in positively influencing occupants' cognitive and emotional states within built environments

New frontiers in neuromarketing research:Benefit and potential applications of GRAIL

Recent years has seen an explosion in the application of neuroscience techniques to market research, known as neuromarketing. The aim of this paper is to contribute to both theoretical and practical aspects of neuromarketing research by presenting a new and innovative neuroscience tool for studying marketing-relevant behavior, namely GRAIL. GRAIL combines different devices (e.g. EEG, ET, facial EMG) into one single real-time device. It can help researchers and practitioners to measure physiological responses (external reflexes) and brain activity (internal reflexes) simultaneously. We argue that this new tool can improve neuromarketing research in several ways, namely in reducing the costs of neuromarketing research, improving the efficiency and accuracy of neuromarketing experiments, and recreating real-life purchase experiences using virtual reality and personalized scenarios

Bestial boredom: a biological perspective on animal boredom and suggestions for its scientific investigation

Boredom is likely to have adaptive value in motivating exploration and learning, and many animals may possess the basic neurological mechanisms to support it. Chronic inescapable boredom can be extremely aversive, and understimulation can harm neural, cognitive and behavioural flexibility. Wild and domesticated animals are at particular risk in captivity, which is often spatially and temporally monotonous. Yet biological research into boredom has barely begun, despite having important implications for animal welfare, the evolution of motivation and cognition, and for human dysfunction at individual and societal levels. Here I aim to facilitate hypotheses about how monotony affects behaviour and physiology, so that boredom can be objectively studied by ethologists and other scientists. I cover valence (pleasantness) and arousal (wakefulness) qualities of boredom, because both can be measured, and I suggest boredom includes suboptimal arousal and aversion to monotony. Because the suboptimal arousal during boredom is aversive, individuals will resist low arousal. Thus, behavioural indicators of boredom will, seemingly paradoxically, include signs of increasing drowsiness, alongside bouts of restlessness, avoidance and sensation-seeking behaviour. Valence and arousal are not, however, sufficient to fully describe boredom. For example, human boredom is further characterized by a perception that time ‘drags’, and this effect of monotony on time perception can too be behaviourally assayed in animals. Sleep disruption and some abnormal behaviour may also be caused by boredom. Ethological research into this emotional phenomenon will deepen understanding of its causes, development, function and evolution, and will enable evidence-based interventions to mitigate human and animal boredom

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 153)

This bibliography lists 175 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1976