Regression approach for automatic detection of attention lapses (original) (raw)
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EEG Waves Studying Intensively to Recognize the Human Attention Behavior
2023 IEEE International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 2023
The term "attention" is used in many different ways to describe cognitive and behavioral processes. Various neuronal activities in the brain are linked to attention. In psychology, "attention" refers to the focus of awareness on a particular phenomenon while disregarding other inputs. The effects of attention on the brain's lobes are diverse. The electrophysiological analysis of brain activity highlights constraints in comprehending human behavior. Electroencephalography (EEG) provides rich data that can be used to investigate the variability in the EEG frequency spectrum. Previous studies have utilized classification accuracy and EEG to measure attentiveness. Aims: This work aims to quantify attention across a range of EEG frequency bands and asymmetry wave perspectives, as well as identify the frequency wave that is most impacted by attention. Methods: The study was approved by the ethics committee and compared the features of EEG under two states: relaxation and attentiveness. Ten participants were given electroencephalograms (EEGs). The Stroop Color Word Test was used to compare two EEGderived feature sessions through statistical analysis and correlation coefficient. Results: The results show a relationship between alpha, beta, and gamma waves and attention level. Delta and theta waves decrease in response to attention, while alpha, beta, and gamma waves increase. The beta power asymmetry has a significant impact on attention compared to other waves. Conclusion: This approach could expedite clinical research and improve patient safety for individuals with ADHD or Alzheimer's.
Generalizability of EEG-based Mental Attention Modeling with Multiple Cognitive Tasks
2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Attention is the foundation of a person's cognitive function. The attention level can be measured and quantified from the electroencephalogram (EEG). For the study of attention detection and quantification, we researchers usually ask the subjects to perform a cognitive test with distinct attentional and inattentional mental states. Different attention tasks are available in the literature, but there is no empirical evaluation to quantitatively compare the attention detection performance among the tasks. We designed an experiment with three typical cognitive tests: Stroop, Eriksen Flanker, and Psychomotor Vigilance Task (PVT), which are arranged in a random order in multiple trials. Data were collected from ten subjects. We used six standard band power features to classify the attention levels in four evaluation scenarios for both subject-specific and subject-independent cases. With cross-validation for the subject-independent case, we achieved a classification accuracy of 61.6%, 63.7% and 65.9% for PVT, Stroop and Flanker tasks respectively. We achieved the highest accuracy of 74.1% and 65.9% for the Flanker test in the subject-dependent and subject-independent cases respectively. Our evaluation shows no statistically significant differences in classification accuracy among the three distinct cognitive tasks. Our study highlights that EEG-based attention recognition can generalize across subjects and cognitive tasks.
Human Attention and Electroencephalograms
2022
MATLAB is an advanced numerical calculation tool that is widely used by engineers and scientists. MATLAB is popular in such elds as image processing, signal processing, communications, and automation systems. MATLAB e ciently re ects changes in research results. The use of the electroencephalogram (EEG) is an important method for exploring human brain activity. It provides useful evaluation data about the changeability of the EEG frequency band. By simplifying the programming environment and improving the EEG results, EEG-MATLAB coding was developed in this study. Ten recordings of subjects were performed using EEGs. The EEG features were compared under two conditions: relax and un-relax. For statistical analysis, a correlation coe cient was used to correlate the two sessions with EEG-extracted features. The objective of this study was to help researchers in EEG analysis to run the code and compare the EEG bands: delta (up to 4-Hz), theta (4-8-Hz), alpha (8-15-Hz), beta (15-32-Hz), and gamma (≥ 32-Hz) waves. The results of this study can also be used for any analysis that employs EEGs in mental status research.
Classification of EEG signals to identify variations in attention during motor task execution
Journal of neuroscience methods, 2017
Brain-computer interface (BCI) systems in neuro-rehabilitation use brain signals to control external devices. User status such as attention affects BCI performance; thus detecting the user's attention drift due to internal or external factors is essential for high detection accuracy. An auditory oddball task was applied to divert the users' attention during a simple ankle dorsiflexion movement. Electroencephalogram signals were recorded from eighteen channels. Temporal and time-frequency features were projected to a lower dimension space and used to analyze the effect of two attention levels on motor tasks in each participant. Then, a global feature distribution was constructed with the projected time-frequency features of all participants from all channels and applied for attention classification during motor movement execution. Time-frequency features led to significantly better classification results with respect to the temporal features, particularly for electrodes locat...
Neural network analysis of event related potentials and electroencephalogram predicts vigilance
1993
Automated monitoring of vigilance in attention intensive tasks such as air traffic control or sonar operation is highly desirable. As the operator monitors the instrument, the instrument would monitor the operator, insuring against lapses. We have taken a first step toward this goal by using feedforward neural networks trained with backpropagation to interpret event related potentials (ERPs) and electroencephalogram (EEG) associated with periods of high and low vigilance. The accuracy of our system on an ERP data set averaged over 28 minutes was 96%, better than the 83% accuracy obtained using linear discriminant analysis. Practical vigilance monitoring will require prediction over shorter time periods. We were able to average the ERP over as little as 2 minutes and still get 90% correct prediction of a vigilance measure. Additionally, we achieved similarly good performance using segments of EEG power spectrum as short as 56 sec.
A real-time EEG-based BCI system for attention recognition in ubiquitous environment
Proceedings of 2011 international workshop on Ubiquitous affective awareness and intelligent interaction - UAAII '11, 2011
Several types of biological signal, such as Electroencephalogram (EEG), electrooculogram(EOG), electrocardiogram(ECG), electromyogram (EMG), skin temperature variation and electrodermal activity, may be used to measure a human subject's attention level. Generally electroencephalogram (EEG) is considered the most effective and objective indicator of attention level. However, few systems based on EEG have actually been developed to measure attention levels. In this paper we describe a pervasive system, based on an electroencephalogram (EEG) Brain-Computer Interface, which measures attention level. After demonstrating the effectiveness of our system we then go on to compare our approach with traditional approaches. In our study, three attention levels were classified by a KNN classifier based on the Self-Assessment Manikin (SAM) model. In our experiment, subjects were given several mental tasks to undertake and asked to report on their attention level during the tasks using a set of attention classifications. The average accuracy rate is shown to reach 57.03% after seven sessions' EEG training. Moreover, our system works in real-time while maintaining this accuracy. This is demonstrated by our time performance evaluation results which show that the time latency is short enough for our system to recognize attention in real-time.
Driver Attention Assessment Using Physiological Measures from EEG, ECG, and EDA Signals
Sensors
In this paper, we consider the evaluation of the mental attention state of individuals driving in a simulated environment. We tested a pool of subjects while driving on a highway and trying to overcome various obstacles placed along the course in both manual and autonomous driving scenarios. Most systems described in the literature use cameras to evaluate features such as blink rate and gaze direction. In this study, we instead analyse the subjects’ Electrodermal activity (EDA) Skin Potential Response (SPR), their Electrocardiogram (ECG), and their Electroencephalogram (EEG). From these signals we extract a number of physiological measures, including eye blink rate and beta frequency band power from EEG, heart rate from ECG, and SPR features, then investigate their capability to assess the mental state and engagement level of the test subjects. In particular, and as confirmed by statistical tests, the signals reveal that in the manual scenario the subjects experienced a more challen...
Attention Level Detection System Based on Brain Computer Interface (BCI)
International Journal of Scientific Research in Science, Engineering and Technology, 2023
The human brain provides several functions such as expressing emotions, controlling the rate of breathing, etc., and their study has aroused the interest of scientists for many years. In this project, we propose a method to assess and quantify human attention and its impact on learning. In our study, we used a Brain-Computer Interface (BCI) capable of detecting brain state variations, whether distracted or not and displaying corresponding electroencephalograms (EEGs). The BCI headset comprising of surface EEG electrodes is attached to the user's head to acquire the brainwaves. The signal received by the BCI headset is processed to remove external noise. The calculated frequencies are then compared to the threshold frequencies of the brain state and a specific decision like whether a person is in an active or distracted state, and the data is then recorded in the cloud.
IEEE Journal of Biomedical and Health Informatics, 2020
Resting-state brain networks represent the intrinsic state of the brain during the majority of cognitive and sensorimotor tasks. However, no study has yet presented concise predictors of task-induced vigilance variability from spectrospatial features of the pre-task, resting-state electroencephalograms (EEG). We asked ten healthy volunteers (6 females, 4 males) aged from 22 to 45.5 to participate in 105-minute fixed-sequence-varying-duration sessions of sustained attention to response task (SART). A novel and adaptive vigilance scoring scheme was designed based on the performance and response time in consecutive trials, and demonstrated large inter-participant variability in terms of maintaining consistent tonic performance. Multiple linear regression using feature relevance analysis obtained significant predictors of the mean cumulative vigilance score (CVS), mean response time, and variabilities of these scores from the resting-state, bandpower ratios of EEG signals, p<0.05. Single-layer neural networks trained with cross-validation also captured different associations for the beta sub-bands. Increase in the gamma (28-48 Hz) and upper beta (24-28 Hz) ratios from the left central and temporal regions predicted slower reactions and more inconsistent vigilance as explained by the increased activation of default mode network (DMN) and differences between the high-and low-attention networks at temporal regions (Brodmanns areas 35 and 36). Higher ratios of parietal alpha (8-12 Hz) from the Brodmann's areas 18, 19, and 37 during the eyes-open states predicted slower responses but more consistent CVS and reactions associated with the superior ability in vigilance maintenance. The proposed framework and these first findings on the most stable and significant attention predictors from the intrinsic EEG power ratios can be used to model attention variations during the calibration sessions of BCI applications and vigilance monitoring systems.
EEG-Based Attention Tracking During Distracted Driving
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2015
Distracted driving might lead to many catastrophic consequences. Developing a countermeasure to track drivers' focus of attention (FOA) and engagement of operators in dual (multi)-tasking conditions is thus imperative. Ten healthy volunteers participated in a dual-task experiment that comprised two tasks: a lane-keeping driving task and a mathematical problem-solving task (e.g., 24+15=37?) during which their electroencephalogram (EEG) and behaviors were concurrently recorded. Independent Component Analysis (ICA) was employed as a spatial filter to separate the contributions of independent sources from the recorded EEG data. The power spectra of six components (i.e., frontal, central, parietal, occipital, left motor, and right motor) extracted from single-task conditions were fed into support vector machine (SVM) based on the radial basis function (RBF) kernel to build an FOA assessment system. The system achieved 84.6±5.8% and 86.2±5.4% classification accuracies in detecting the...