Human Attention Level Research Articles

Understanding the Level of Human Attention for Cognitive Analysis to Transplant the Human Behavioral Mechanism to Robots

Understanding the Level of Human Attention for Cognitive Analysis to Transplant the Human Behavioral Mechanism to Robots

A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level

Attention is the mental awareness of human on a particular object or a piece of information. The level of attention indicates how intense the focus is on an object or an instance. In this study, several types of human attention level have been observed. After introducing image segmentation and detection technique for facial features, eyeball movement and gaze estimation were measured. Eye movement were assessed using the video data, and a total of 10197 data instances were manually labelled for the attention level. Then Artificial Neural Network (ANN) and Recurrent Neural Network-Long Short Term Memory (LSTM) based Deep learning (DL) architectures have been proposed for analysing the data. Next, the trained DL model has been implanted into a robotic system that is capable of detecting various features; ultimately leading to the calculation of visual attention for reading, browsing, and writing purposes. This system is capable of checking the attention level of the participants and also can detect if participants are present or not. Based on a certain level of visual focus of attention (VFOA), this system interacts with the person, generates awareness and establishes verbal or visual communication with that person. The proposed ML techniques have achieved almost 99.24% validation accuracy and 99.43% test accuracy. It is also shown in the comparative study that, since the dataset volumes are limited, ANN is more suitable for attention level calculation than RNN-LSTM. We hope that the implemented robotic structure manifests the real-world implication of the proposed method.

Electroencephalogram-Based Attention Level Classification Using Convolution Attention Memory Neural Network

Attentive learning is an important feature of the learning process. It provides a beneficial learning experience and plays a key role in generating positive learning outcomes. Most studies widely applied electroencephalogram (EEG) to measure human attention level. Although most studies use EEG handcrafted features and statistical methods to classify attention level, a more effective feature learning technique is still needed. In this paper, we aim to analyze participants’ EEG signals through a deep learning model and classify those signals as showing either attentive or inattentive behaviors. To carry out this research, we initially conducted a background study on attention and its detection in EEG. After that, we design a Troxler’s fading experiment and use an EEG device to collect data on participants’ attentive and inattentive behaviors during the test. The collected EEG data will be analyzed using a Convolution Attention Memory Neural Network (CAMNN) model to classify participants’ attention level. The proposed CAMNN model is optimized with Vector-to-Vector (Vec2Vec) modeling, where the model can be learned through deep neural networks in an end-to-end approach. The result shows that our model can achieve 92% accuracy and 0.92 F1 score which outperforms several existing neural network models such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN), Deep Learning with Convolutional Neural Networks (deep ConvNets), and Compact Convolutional Network for EEG-based BCIs (EEGNet). This research can be useful for those who are interested in developing attention level monitoring or biofeedback system in areas such as educational classroom learning, medical research, and industrial operator.

Automation by Brain Sense Through Eeg Waves

Brain Computer Interface (BCI) is one of the communication channels used to make an interaction between human brain and digital computer to control/operate the external devices without having any touch of muscular body part. This provides the easiness in operations and also helpful to elder and decibel people. This is useful for people who cannot operate the peripheral devices using our normal muscular body parts. The proposed system aims to control home appliances (like bulb, fanatic.) with the help of Human Attention Level which come sunder on – invasive method of brains signal measurement. This attention is being measured by Neuro Sky Headset. Attention level values are ranges from 1 to 100. Attentionmeansuser’ level of mental focus which occurs during intense concentration and directed (but stable) mental activity. So, to get attention values user should observe the object (or focus on to the object). For demonstration purpose here are used one bulb and one fan.

Evaluation of the Tiller Switch Layout of a Tractor Using Eye-Fixation Related Potentials.

The human-machine interface (HMI) of a tractor cockpit includes not only the steering wheel and pedals, but also many levers and switches. According to a report from the National Agriculture and Food Research Organization of Japan, the HMI layout often differs from the operator's expectation. This gap between expectations and reality has led to operator errors and market complaints. However, few reports have ergonomically evaluated these arrangements. Our previous work showed that this gap can be evaluated by the amplitude of the P300 component of the event-related potentials (ERPs) for discrimination tasks by using a picture of the vehicle cockpit on the display. However, in real-world driving and operation situations, eye movements occur. In such cases, the eye-fixation related potential (EFRP) may be used. The EFRP appears at the end of a saccade, which is a rapid movement of the eyes between fixation points. Its positive component, called the lambda response, is reported to reflect the human attention level to visual targets. We hypothesize that if the HMI layout differs from the user's expectation, operation becomes difficult and the user's attention to the target during operation will be reduced. In order to confirm this hypothesis, an experiment was conducted during the actual operation of a tractor while changing the tiller switch position. The participants were 13 adult males with experience in tractor operations. The amplitude of the lambda wave was reduced when the switch position was different from the user's expectations, reflecting the decrease in attention caused by the increased difficulty of operating the tiller. We suggest that the amplitude of the lambda response can be used as the evaluation index for optimizing the switch position.

Optimized Complex Network Method (OCNM) for Improving Accuracy of Measuring Human Attention in Single-Electrode Neurofeedback System.

A neurofeedback system adjusting an individual's attention is an effective treatment for attention-deficit/hyperactivity disorder (ADHD). In current studies, an accurate measure of the level of human attention is one of the key issues that arouse much interest. This paper proposes a novel optimized complex network method (OCNM) for measuring an individual's attention level using single-electrode electroencephalography (EEG) signals. A time-delay embedding algorithm was used to reconstruct EEG data epochs into nodes of the OCNM network. Euclidean distances were calculated between each two nodes to decide edges of the network. Three key parameters influencing OCNM, i.e., delaying time, embedding dimension, and connection threshold, were optimized for each individual. The average degree and clustering coefficient of the constructed network were extracted as a feature vector and were classified into two patterns of concentration and relaxation using an LDA classifier. In the offline experiments of six subjects, the classification performance was tested and compared with an attention meter method (AMM) and an α + β + δ + θ + R method. The experimental results showed that the proposed OCNM achieved the highest accuracy rate (80.67% versus 70.58% and 68.88%). This suggests that the proposed method can potentially be used for EEG-based neurofeedback systems with a single electrode.

EEG predicts the attention level of elderly measured by RBANS

Purpose This study aims to investigate the correlation between neural indexes of attention and behavioral indexes of attention and detect the most informative period of brain activity in which the strongest correlation with attentive performance (behavioral index) exists. Finally, to further validate the findings, this paper aims at the prediction of different levels of attention function based on the attention score obtained from repeatable battery for the assessment of neurophysiological status (RBANS). Design/methodology/approach The present paper analyzes electroencephalogram (EEG) signals recorded by a single prefrontal channel from 105 elderly subjects while they were responding to Stroop color test which is an attention-demanded task. Beside Stroop test, subjects also performed RBANS which provides their level of functionality in different domains including attention. After data acquisition (EEG during Stroop test and RBANS attention score), the authors extract the spectral features of EEG as neural indexes of attention and subjects’ reaction time in response to Stroop test as behavioral index of attention. Then, they explore the correlation between these post-cue frequency band oscillations of EEG with elderly response time (RT). Next, the authors exploit these findings to classify RBANS attention score. Findings The observations of this study suggest that there is significant negative correlation between alpha gamma ratio (AGR) and RT (p < 0.0001), theta beta ratio (TBR) is positively correlated with subjects’ RT (p < 0.0001), these correlations are stronger in a 500ms period right after triggering the cue (question onset in Stroop test), and 4) TBR and AGR can be effectively used to predict RBANS attention score. Research limitations/implications Because of the experiment design, the pre-cue EEG of the next trail was very much overlapped with the post-cue EEG of the current trail. Therefore, the authors could analyze only post-cue EEG. In future study, it would be interesting to investigate the predictability of subject’s future performance from pre-cue EEG and mental preparation. Practical implications This study provides an insight into the research on detection of human attention level from EEG instead of conventional neurophysiological tests. It has also potential to be used in implementation of feasible and efficient EEG-based brain computer interface training systems for elderly. Originality/value To the best of the authors’ knowledge, this study is among very few attempts for early prediction of cognitive decline in the domain of attention from brain activity (EEG) instead of conventional tests which are prone to human errors.

Multimodal human attention detection for reading from facial expression, eye gaze, and mouse dynamics

Affective computing has recently become an important area in human-computer interaction research. Techniques have been developed to enable computers to understand human affects or emotions, in order to predict human intention more precisely and provide better service to user to enhance user experience. In this paper, we investigate into the detection of human attention level as a useful from of human affect, which could be influential in intelligent e-learning applications. We adopt ubiquitous hardware available in most computer systems, namely, webcam and mouse. Information from multiple input modalities is fused together for effective human attention detection. We invite human subjects to carry out experiments in reading articles when being imposed upon different kinds of distraction to induce them into different levels of attention. Machine-learning techniques are applied to identify useful features to recognize human attention level by building up user-independent models. Our result indicate performance improvement with multimodal inputs from webcam and mouse over that of a single device. We believe that our work has revealed and intersting affective computing direction with potential application in e-learning.