EEG Waves Studying Intensively to Recognize the Human Attention Behavior (original) (raw)
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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.
Electroencephalography (EEG)-derived markers to Measure Components of Attention Processing
2017
Although extensively studied for decades, attention system remains an interesting challenge in neuroscience field. The Attention Network Task (ANT) has been developed to provide a measure of the efficiency for the three attention components identified in the Posner’s theoretical model: alerting, orienting and executive control. Here we propose a study on 15 healthy subjects who performed the ANT. We combined advanced methods for connectivity estimation on electroencephalographic (EEG) signals and graph theory with the aim to identify neuro-physiological indices describing the most important features of the three networks correlated with behavioral performances. Our results provided a set of band-specific connectivity indices able to follow the behavioral task performances among subjects for each attention component as defined in the ANT paradigm. Extracted EEG-based indices could be employed in future clinical applications to support the behavioral assessment or to evaluate the infl...
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.
EEG predicts the attention level of elderly measured by RBANS
International Journal of Crowd Science
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.
Analysis of propagation of multi-channel EEG in the test of sustained attention
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2010
the psychological construct 'sustained attention' describes a basic component of attention characterized by the subject's readiness to detect rarely and unpredictably occurring signals over prolonged periods of time. In this study, six healthy volunteers underwent a sustained attention to response task (SART), while their electroencephalographic (EEG) were recorded contemporarily. Directed Transfer Function (DTF) was used as estimator for direction of propagation of EEG function coupling. The results of DTF showed that the information flux within EEG functional coupling changed when attention condition changed from inattention state to sustained attention state, principally at alpha and beta rhythms. The DTF could be used to evaluate sustained attention condition and they might be used for research on damage of attention mechanisms of ADHD and TBI diseases in future.
Sustained Attention in Real Classroom Settings: An EEG Study
Frontiers in human neuroscience, 2017
Sustained attention is a process that enables the maintenance of response persistence and continuous effort over extended periods of time. Performing attention-related tasks in real life involves the need to ignore a variety of distractions and inhibit attention shifts to irrelevant activities. This study investigates electroencephalography (EEG) spectral changes during a sustained attention task within a real classroom environment. Eighteen healthy students were instructed to recognize as fast as possible special visual targets that were displayed during regular university lectures. Sorting their EEG spectra with respect to response times, which indicated the level of visual alertness to randomly introduced visual stimuli, revealed significant changes in the brain oscillation patterns. The results of power-frequency analysis demonstrated a relationship between variations in the EEG spectral dynamics and impaired performance in the sustained attention task. Across subjects and sessi...
EEG spectral feature markers as an indicator of human cognitive process
Information technologies allow using modern and timely effective analyses of EEG waves and the methods of data processing that allows effective usage of this method into pedagogically and psychologically oriented researches. Aim of this study was to develop and validate method of EEG signal spectral properties usage in the investigations of the process of cognition in the process of the perception of music by the choice of professional studies. 23 research participants took part in the researchthe students of the University of Latvia, the division of participants "non-musician" and "musician". The EEG recording synchronized with the musical signal using the generated synchronization signal that given to one of the unipolar input channels of the EEG equipment. The research analyses the basic rhythm of EEG the changes of the maximum frequency and the wave frequency power in the processes connected with the perception and cognition of music for 15 seconds long intervals. During the time of listening to the chorus songs, the range frequency of the range rhythm of alpha and beta does not change to the musicians but during the time of listening to the instrumental music it increases but it was more vivid in the range of beta frequency. Non-musicians reacted differently-while listening to chorus songs and instrumental music the frequency of alfa waves of EEG increased, but the beta wave frequency decreased. EEG as a method of investigation is recommended for pedagogical research to evaluate the neurological functions in the cognitive processes.
2020
Attention is one of the main cognitive skills that is constantly used in everyday life. However, various factors can be diminished and even blocked by various disorders, diseases or behaviors that affect people's performance. To analyze the brain signals, the Neurosky MindWave EEG device is required, this device determines the levels of attention of people when they perform some activity and Arduino for data capture. This article compares data obtained from reading the level of care of people of different ages and sex using the Neurosky and Arduino Uno devices. The results obtained show that women (sex) and adults (age) have greater stability of attention over time, and that men (sex) and youth (age) get to obtain higher levels of attention.
Induced alpha band power changes in the human EEG and attention
Neuroscience Letters, 1998
Induced alpha power (in a lower, intermediate and upper band) which is deprived from evoked electroencephalograph (EEG) activity was analyzed in an oddball task in which a warning signal (WS) preceded a target or non-target. The lower band, reflecting phasic alertness, desynchronizes only in response to the WS and target. The intermediate band, reflecting expectancy, desynchronizes about 1 s before a target or non-target appears. Upper alpha desynchronizes only after a target is presented and, thus, reflects the performance of the task which was to count the targets. Thus, only slower alpha frequencies reflect attentional demands such as alertness and expectancy.
Alpha Activity in EEG and Intelligence
International Journal of Advanced Information Technology, 2012
Intelligence of a human being in general is considered as to its variations in the ability to learn, to function in society, and to behave according to contemporary social expectations Intelligence of a human being is associated with brain the brain is considered as the most complex biological existent structure. Electroencephalograph (EEG) is an instrument used for recording the electrical activity of the brain. EEG is the variation of the electrical fields in the cortex or on the surface of scalp caused by the physiological activities of the brain. EEG is currently the most widely adopted method for assessing brain activities. Detecting the changes of these waves is critical for understanding of brain function. In clinical applications, spontaneous EEG signals can be divided into several rhythms according to their frequency. They are δ rhythm (0.1-4Hz), θ rhythm (4-8Hz), α rhythm (8-13), β and rhythm (13-30Hz). The EEG signals have close relationships with the cerebral diseases, mental status and human qualities like intelligence. As a consequence it is very useful to analyze process and classify the EEG signal on the basis of frequency bands and then extract their underlying features and so as to correlate with normal and abnormal functioning of brain, sleep, mental status and also with intelligence. In this paper we propose to conduct pointed literature survey of alpha activity and intelligence correlation. We propose to conduct test on subjects by computer, EEG interface. We propose to conclude from practical experimentation, whether there is a correlation between alpha activity power and intelligence.