Blink To Win: Blink Patterns of Video Game Players Are Connected to Expertise

In this study, we analyzed the blinking behavior of players in a video game tournament. Our aim was to test whether spontaneous blink patterns differ across levels of expertise. We used blink rate, blink duration, blink frequency, and eyelid movements represented by the Eye Aspect Ratio (EAR) to train a machine learning classifier to discriminate between different levels of expertise. Classifier performance was highly influenced by features such as the mean, standard deviation and median EAR. Moreover, further analysis suggests that blinking rate and blink duration are likely to increase along with the level of expertise. We speculate this may be indicative of a reduction in cognitive load and lowered stress of expert players. In general, our results suggest that EAR and blink patterns can be used to identify different levels of expertise of video game players

Testing of a Consumer-Grade EEG Device for Computer Control

Brain-computer interfaces (BCI) offer the ability to control a computer with just the power of thought; electroencephalography (EEG) is the main method for recording such thoughts. Emotiv Inc. is a technology company which sells consumer-grade EEG devices, promising accessible BCI for general use. This study had participants use the Emotiv Insight, the lower-end EEG device, to play a video game, and compared the results against the Emotiv EPOC+, the more reliable but expensive EEG device. Results showed that the Insight performed probably worse than the EPOC; combining the results with previous literature point towards avenues of improvement for the Insight, including software, training, and comfort

EEG analysis for understanding stress based on affective model basis function

Coping with stress has shown to be able to avoid many complications in medical condition. In this paper we present an alternative method in analyzing and understanding stress using the four basic emotions of happy, calm, sad and fear as our basis function. Electroencephalogram (EEG) signals were captured from the scalp of the brain and measured in responds to various stimuli from the four basic emotions to stimulating stress base on the IAPS emotion stimuli. Features from the EEG signals were extracted using the Kernel Density Estimation (KDE) and classified using the Multilayer Perceptron (MLP), a neural network classifier to obtain accuracy of the subject’s emotion leading to stress. Results have shown the potential of using the basic emotion basis function to visualize the stress perception as an alternative tool for engineers and psychologist. Keywords: Electroencephalography (EEG), Kernel Density Estimation (KDE), Multi-layer Perceptron (MLP), Valance (V), Arousal (A

A framework to estimate cognitive load using physiological data

Cognitive load has been widely studied to help understand human performance. It is desirable to monitor user cognitive load in applications such as automation, robotics, and aerospace to achieve operational safety and to improve user experience. This can allow efficient workload management and can help to avoid or to reduce human error. However, tracking cognitive load in real time with high accuracy remains a challenge. Hence, we propose a framework to detect cognitive load by non-intrusively measuring physiological data from the eyes and heart. We exemplify and evaluate the framework where participants engage in a task that induces different levels of cognitive load. The framework uses a set of classifiers to accurately predict low, medium and high levels of cognitive load. The classifiers achieve high predictive accuracy. In particular, Random Forest and Naive Bayes performed best with accuracies of 91.66% and 85.83% respectively. Furthermore, we found that, while mean pupil diameter change for both right and left eye were the most prominent features, blinking rate also made a moderately important contribution to this highly accurate prediction of low, medium and high cognitive load. The existing results on accuracy considerably outperform prior approaches and demonstrate the applicability of our framework to detect cognitive load

Neural correlates of flow, boredom, and anxiety in gaming: An electroencephalogram study

Games are engaging and captivating from a human-computer interaction (HCI) perspective as they can facilitate a highly immersive experience. This research examines the neural correlates of flow, boredom, and anxiety during video gaming. A within-subject experimental study (N = 44) was carried out with the use of electroencephalogram (EEG) to assess the brain activity associated with three states of user experience - flow, boredom, and anxiety - in a controlled gaming environment. A video game, Tetris, was used to induce flow, boredom, and anxiety. A 64 channel EEG headset was used to track changes in activation patterns in the frontal, temporal, parietal, and occipital lobes of the players\u27 brains during the experiment. EEG signals were pre-processed and Fast Fourier Transformation values were extracted and analyzed. The results suggest that the EEG potential in the left frontal lobe is lower in the flow state than in the resting and boredom states. The occipital alpha is lower in the flow state than in the resting state. Similarly, the EEG theta in the left parietal lobe is lower during the flow state than the resting state. However, the EEG theta in the frontal-temporal region of the brain is higher in the flow state than in the anxiety state. The flow state is associated with low cognitive load, presence of attention levels, and loss of self-consciousness when compared to resting and boredom states --Abstract, page iii

The Ocular Surface Control of Blinking, Tearing and Sensation

Thesis (Ph.D.) - Indiana University, Optometry, 2014Dry eye is a common condition that affects millions in the US and worldwide. It is considered to be a multifactorial disease of the tear film and ocular surface and is associated with symptoms of ocular discomfort and visual disturbance. Low blink rate has been identified as a potential risk factor for the development of dry eye because it can result in increased evaporative loss from the tear film. Failure of tear secretion has also been recognized as one of the main factors for dry eye development, characterized as low tear volume and slow tear turnover rate. Both factors in turn may lead to increased tear film hyperosmolarity and instability, which are considered core mechanisms of dry eye. In the natural condition, the ocular surface is mainly protected by blinking and tear secretion in that the newly secreted tears flow into the upper and lower meniscus and the blink spreads the new tear film from the meniscus to the ocular surface. Therefore, the ocular surface control over blinking and tear secretion is important in the etiology of the dry eye condition. In this proposal, we develop a laboratory model using human subjects to test how input from the ocular surface affects both blinking and tear secretion. We hypothesize that ocular surface stimuli will activate corneal receptors to signal a high blink rate, reflex tear secretion and ocular sensations of discomfort. These probably act together for the purpose of preventing ocular damage. These results will help us to understand the manner in which the ocular surface responds to adverse stimuli, which may ultimately lead toward further development of treatments or methods in dry eye patients

Data S1: Data

We present the evaluation of two well-known, low-cost consumer-grade EEG devices: the Emotiv EPOC and the Neurosky MindWave. Problems with using the consumer-grade EEG devices (BCI illiteracy, poor technical characteristics, and adverse EEG artefacts) are discussed. The experimental evaluation of the devices, performed with 10 subjects asked to perform concentration/relaxation and blinking recognition tasks, is given. The results of statistical analysis show that both devices exhibit high variability and non-normality of attention and meditation data, which makes each of them difficult to use as an input to control tasks. BCI illiteracy may be a significant problem, as well as setting up of the proper environment of the experiment. The results of blinking recognition show that using the Neurosky device means recognition accuracy is less than 50%, while the Emotiv device has achieved a recognition accuracy of more than 75%; for tasks that require concentration and relaxation of subjects, the Emotiv EPOC device has performed better (as measured by the recognition accuracy) by ∼9%. Therefore, the Emotiv EPOC device may be more suitable for control tasks using the attention/meditation level or eye blinking than the Neurosky MindWave device