Motor restrictions impair divergent thinking during walking and during sitting

Creativity, specifically divergent thinking, has been shown to benefit from unrestrained walking. Despite these findings, it is not clear if it is the lack of restriction that leads to the improvement. Our goal was to explore the effects of motor restrictions on divergent thinking for different movement states. In addition, we assessed whether spontaneous eye blinks, which are linked to motor execution, also predict performance. In experiment 1, we compared the performance in Guilford’s alternate uses task (AUT) during walking vs. sitting, and analysed eye blink rates during both conditions. We found that AUT scores were higher during walking than sitting. Albeit eye blinks differed significantly between movement conditions (walking vs. sitting) and task phase (baseline vs. thinking vs. responding), they did not correlate with task performance. In experiment 2 and 3, participants either walked freely or in a restricted path, or sat freely or fixated on a screen. When the factor restriction was explicitly modulated, the effect of walking was reduced, while restriction showed a significant influence on the fluency scores. Importantly, we found a significant correlation between the rate of eye blinks and creativity scores between subjects, depending on the restriction condition. Our study shows a movement state-independent effect of restriction on divergent thinking. In other words, similar to unrestrained walking, unrestrained sitting also improves divergent thinking. Importantly, we discuss a mechanistic explanation of the effect of restriction on divergent thinking based on the increased size of the focus of attention and the consequent bias towards flexibility.

A LabVIEW Instrument Aimed for the Research on Brain-Computer Interface by Enabling the Acquisition, Processing, and the Neural Networks based Classification of the Raw EEG Signal Detected by the Embedded NeuroSky Biosensor

This paper proposes several LabVIEW applications to accomplish the data acquisition, processing, features extraction and real-time classification of the electroencephalographic (EEG) signal detected by the embedded sensor of the NeuroSky Mindwave Mobile headset. The LabVIEW applications are aimed at the implementation of a Brain-Computer Interface system, which is necessary to people with neuromotor disabilities. It is analyzed a novel approach regarding the preparation and automatic generation of the EEG dataset by identifying the most relevant multiple mixtures between selected EEG rhythms (both time and frequency domains of raw signal, delta, theta, alpha, beta, gamma) and extracted statistical features (mean, median, standard deviation, route mean square, Kurtosis coefficient and others). The acquired raw EEG signal is processed and segmented into temporal sequences corresponding to the detection of the multiple voluntary eye-blinks EEG patterns. The main LabVIEW application accomplished the optimal real-time artificial neural networks techniques for the classification of the EEG temporal sequences corresponding to the four states: 0 - No Eye-Blink Detected; 1 - One Eye-Blink Detected; 2 – Two Eye-Blinks Detected and 3 – Three Eye-Blinks Detected. Nevertheless, the application can be used to classify other EEG patterns corresponding to different cognitive tasks, since the whole functionality and working principle could estimate the labels associated with various classes.

Eye Blink Classification for Assisting Disability to Communicate Using Bagging and Boosting

Disability is a physical or mental impairment. People with disability have more barriers to do certain activity than those without disability. Moreover, several conditions make them having difficulty to communicate with other people. Currently, researchers have helped people with disabilities by developing brain-computer interface (BCI) technology, which uses artifact on electroencephalograph (EEG) as a communication tool using blinks. Research on eye blinks has only focused on the threshold and peak amplitude, while the difference in how many blinks can be detected using peak amplitude has not been the focus yet. This study used primary data taken using a Muse headband on 15 subjects. This data was used as a dataset classified using bagging (random forest) and boosting (XGBoost) methods with python; 80% of the data was allocated for learning and 20% was for testing. The classified data was divided into ten times of testing, which were then averaged. The number of eye blinks’ classification results showed that the accuracy value using random forest was 77.55%, and the accuracy result with the XGBoost method was 90.39%. The result suggests that the experimental model is successful and can be used as a reference for making applications that help people to communicate by differentiating the number of eye blinks. This research focused on developing the number of eye blinks. However, in this study, only three blinking were used so that further research could increase these number.

Blink Rate vs Inner Self-perceived Cognitive State while using Virtual Reality Applications with Haptic Devices

On average, humans blink between eight and 21 times per minute while resting. Eye actions are influenced by external and internal stimuli. This can be utilized to measure internal cognition specifically focus and attention while performing tasks. In this experiment, an individual’s self-perceived focus, attention and interaction level is collected and then correlated with eye blinks rate. The subjects are observed while performing experiments using haptic devices in a virtual environment. The experiment was modulated based on network quality and hardware quality to further quantify the effect of each scenario on subjects’ intrinsic and extrinsic focus indicators. The experiment quantified the blinks and formulated a correlation between subjects’ own perception of the event using statistical analysis. It can be found that there is an acceptable correlation between certain indicators, network quality, fatigue, stress, focus and enjoyability of the experiments. A good quality hardware and network did enhance the experience in all the subjects indicating a need for enhanced services for haptic and immersed mixed reality activities directly affecting the cognition especially in education tools. It can be inferred that eye blink rate can be used as an additional tool for measuring the cognition of individuals experience using haptic and virtual reality tools.

Video based eye blink analysis for psychological state determination

The status of mental health and mood of human beings are well comprehensible by careful observation of movements of different body parts. Eye being the most prominent body part, analysis of different eye parameters such as blink, gaze, opening and closing rate provides important clues on mood status as well as mental health conditions. The present work can be viewed from a statistical and machine learning perspective that utilizes eye blink information to study the mental health status of a person. By using appropriate image processing techniques eye blinks of different subjects were collected through an experimental setup. The setup contained a recording environment where each participant was required to watch two videos of opposite emotions, i.e., joy and sad during different time settings. From the recorded videos of each participant, eye blinks were extracted and investigated. On analyzing the blink rates thoroughly, using statistical and machine learning means we observed; 1) an increase in number of eye blinks when the mood of a participant swings from sad to joy and 2) a significantly smaller number of blinks in depressed participants than the normal participants while in sad mood.