scholarly journals Characterizing Focused Attention and Working Memory Using EEG

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3743 ◽  
Author(s):  
Zainab Mohamed ◽  
Mohamed El Halaby ◽  
Tamer Said ◽  
Doaa Shawky ◽  
Ashraf Badawi
Keyword(s):  
Working Memory ◽  
Adaptive Learning ◽  
Cognitive Skills ◽  
Classification Problem ◽  
Focused Attention ◽  
Cognitive Profiles ◽  
Eeg Signals ◽  
Cognitive States ◽  
Wearable Eeg ◽  
Eeg Data

Detecting the cognitive profiles of learners is an important step towards personalized and adaptive learning. Electroencephalograms (EEG) have been used to detect the subject’s emotional and cognitive states. In this paper, an approach for detecting two cognitive skills, focused attention and working memory, using EEG signals is proposed. The proposed approach consists of the following main steps: first, subjects undergo a scientifically-validated cognitive assessment test that stimulates and measures their full cognitive profile while putting on a 14-channel wearable EEG headset. Second, the scores of focused attention and working memory are extracted and encoded for a classification problem. Third, the collected EEG data are analyzed and a total of 280 time- and frequency-domain features are extracted. Fourth, several classifiers were trained to correctly classify and predict three levels (low, average, and high) of the two cognitive skills. The classification accuracies that were obtained on 86 subjects were 84% and 81% for the focused attention and working memory, respectively. In comparison with similar approaches, the obtained results indicate the generalizability and suitability of the proposed approach for the detection of these two skills. Thus, the presented approach can be used as a step towards adaptive learning where real-time adaptation is to be done according to the predicted levels of the measured cognitive skills.

scholarly journals The Automatic Detection of Cognition Using EEG and Facial Expressions

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3516
Author(s):  
Mohamed El Kerdawy ◽  
Mohamed El Halaby ◽  
Afnan Hassan ◽  
Mohamed Maher ◽  
Hatem Fayed ◽  
...  
Keyword(s):  
Facial Expressions ◽  
Adaptive Learning ◽  
Cognitive Skills ◽  
Performance Measure ◽  
Focused Attention ◽  
Cognitive Profiles ◽  
Cognitive Assessments ◽  
Cognitive States ◽  
Monitoring Tools ◽  
Adaptive Learning Systems

Detecting cognitive profiles is critical to efficient adaptive learning systems that automatically adjust the content delivered depending on the learner’s cognitive states and skills. This study explores electroencephalography (EEG) and facial expressions as physiological monitoring tools to build models that detect two cognitive states, namely, engagement and instantaneous attention, and three cognitive skills, namely, focused attention, planning, and shifting. First, while wearing a 14-channel EEG Headset and being videotaped, data has been collected from 127 subjects taking two scientifically validated cognitive assessments. Second, labeling was performed based on the scores obtained from the used tools. Third, different shallow and deep models were experimented in the two modalities of EEG and facial expressions. Finally, the best performing models for the analyzed states are determined. According to the used performance measure, which is the f-beta score with beta = 2, the best obtained results for engagement, instantaneous attention, and focused attention are EEG-based models with 0.86, 0.82, and 0.63 scores, respectively. As for planning and shifting, the best performing models are facial expressions-based models with 0.78 and 0.81, respectively. The obtained results show that EEG and facial expressions contain important and different cues and features about the analyzed cognitive states, and hence, can be used to automatically and non-intrusively detect them.

scholarly journals Efficient electro encephelogram classification system using support vector machine classifier and adaptive learning technique

2022 ◽  
Vol 25 (1) ◽  
pp. 291
Author(s):  
Virupaxi Balachandra Dalal ◽  
Satish S. Bhairannawar
Keyword(s):  
Support Vector Machine ◽  
Adaptive Learning ◽  
Classification System ◽  
Training Data ◽  
Support Vector ◽  
Detection Accuracy ◽  
Eeg Signals ◽  
The Neural Network ◽  
Eeg Data ◽  
Learning Technique

Complex <span>modern signal processing is used to automate the analysis of electro encephelogram (EEG) signals. For the diagnosis of seizures, approaches that are simple and precise may be preferable rather than difficult and time-consuming. In this paper, efficient EEG classification system using support vector machine (SVM) and Adaptive learning technique is proposed. The database EEG signals are subjected to temporal and spatial filtering to remove unwanted noise and to increase the detection accuracy of the classifier by selecting the specific bands in which most of the EEG data are present. The neural network based SVM is used to classify the test EEG data with respect to training data. The cost-sensitive SVM with proposed Adaptive learning classifies the EEG signals where the adaptive learning with probability based function helps in prediction of the future samples and this leads in improving the accuracy with detection time. The detection accuracy of the proposed algorithm is compared with existing which shows that the proposed algorithm can classify the EEG signal more </span>effectively.

scholarly journals Effects of context on the neural correlates of attention in a college classroom

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Jennie K. Grammer ◽  
Keye Xu ◽  
Agatha Lenartowicz
Keyword(s):  
Behavioral Observation ◽  
Alpha Power ◽  
Learning Context ◽  
Eeg Signals ◽  
Cognitive States ◽  
Eeg Data ◽  
Self Reports ◽  
Gamma Power ◽  
The Impact ◽  
Student Attention

AbstractActivities that are effective in supporting attention have the potential to increase opportunities for student learning. However, little is known about the impact of instructional contexts on student attention, in part due to limitations in our ability to measure attention in the classroom, typically based on behavioral observation and self-reports. To address this issue, we used portable electroencephalography (EEG) measurements of neural oscillations to evaluate the effects of learning context on student attention. The results suggest that attention, as indexed by lower alpha power as well as higher beta and gamma power, is stronger during student-initiated activities than teacher-initiated activities. EEG data revealed different patterns in student attention as compared to standardized coding of attentional behaviors. We conclude that EEG signals offer a powerful tool for understanding differences in student cognitive states as a function of classroom instruction that are unobservable from behavior alone.

Basic numerical processing, calculation, and working memory in children with dyscalculia and/or ADHD symptoms

2016 ◽  
Vol 44 (5) ◽  
pp. 365-375 ◽  
Author(s):  
Jörg-Tobias Kuhn ◽  
Elena Ise ◽  
Julia Raddatz ◽  
Christin Schwenk ◽  
Christian Dobel
Keyword(s):  
Working Memory ◽  
Processing Speed ◽  
Verbal Working Memory ◽  
Typically Developing ◽  
Cognitive Profiles ◽  
Adhd Symptoms ◽  
Memory Processing ◽  
Numerical Processing ◽  
Hyperactivity Disorder ◽  
Neurocognitive Measures

Abstract. Objective: Deficits in basic numerical skills, calculation, and working memory have been found in children with developmental dyscalculia (DD) as well as children with attention-deficit/hyperactivity disorder (ADHD). This paper investigates cognitive profiles of children with DD and/or ADHD symptoms (AS) in a double dissociation design to obtain a better understanding of the comorbidity of DD and ADHD. Method: Children with DD-only (N = 33), AS-only (N = 16), comorbid DD+AS (N = 20), and typically developing controls (TD, N = 40) were assessed on measures of basic numerical processing, calculation, working memory, processing speed, and neurocognitive measures of attention. Results: Children with DD (DD, DD+AS) showed deficits in all basic numerical skills, calculation, working memory, and sustained attention. Children with AS (AS, DD+AS) displayed more selective difficulties in dot enumeration, subtraction, verbal working memory, and processing speed. Also, they generally performed more poorly in neurocognitive measures of attention, especially alertness. Children with DD+AS mostly showed an additive combination of the deficits associated with DD-only and A_Sonly, except for subtraction tasks, in which they were less impaired than expected. Conclusions: DD and AS appear to be related to largely distinct patterns of cognitive deficits, which are present in combination in children with DD+AS.


scholarly journals Implementation and Evaluation of a Serious Game for Working Memory Enhancement

2020 ◽  
Vol 10 (24) ◽  
pp. 9128
Author(s):  
Agisilaos Chaldogeridis ◽  
Thrasyvoulos Tsiatsos
Keyword(s):  
Working Memory ◽  
Video Game ◽  
Cognitive Skills ◽  
Intervention Program ◽  
Evaluation Process ◽  
Serious Game ◽  
Memory Enhancement ◽  
Targeted Interventions ◽  
Mental Abilities ◽  
Key Aspects

The amount of information which can be stored in the human brain is limited and dependent on memory capacity. Over the last few years there has been a trend in training cognitive skills, not only to prevent cognitive decline, which is inevitable as a person grows older, but also to increase or at least preserve mental abilities that will allow a person to function at a higher cognitive level. Memory is one of those key aspects among cognitive skills that has a significant role in a person’s mental performance. Specifically, focus is given to Working Memory (WM), as evidence has shown that it can be increased by applying targeted interventions. An intervention program like this is the main object of this current paper. Using a Serious Game (SG), we designed and created a video game which targets WM training. Its effectiveness was tested and evaluated through an evaluation process where forty people participated in a seven-week training program. Post-results showed that participants had an increase in their WM performance, especially those who had lower scores at the pre-test, while those with high pre-test scores just preserved their initial status. Additionally, all participants agreed that the game is fun and enjoyable to play and that it helps them to increase WM performance.

An Efficient Deep Learning Paradigm for Deceit Identification Test on EEG Signals

2021 ◽  
Vol 12 (3) ◽  
pp. 1-20
Author(s):  
Damodar Reddy Edla ◽  
Shubham Dodia ◽  
Annushree Bablani ◽  
Venkatanareshbabu Kuppili
Keyword(s):  
Brain Computer Interface ◽  
Wavelet Packet ◽  
Stimulus Onset ◽  
Computer Interface ◽  
Band Pass Filter ◽  
Eeg Signals ◽  
Pass Filter ◽  
Eeg Data ◽  
Identification Test ◽  
Band Pass

Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field of entertainment to medical. In this article, a novel Deceit Identification Test is proposed based on the Electroencephalogram signals to identify and analyze the human behavior. Deceit identification test is based on P300 signals, which have a positive peak from 300 ms to 1,000 ms of the stimulus onset. The aim of the experiment is to identify and classify P300 signals with good classification accuracy. For preprocessing, a band-pass filter is used to eliminate the artifacts. The feature extraction is carried out using “symlet” Wavelet Packet Transform (WPT). Deep Neural Network (DNN) with two autoencoders having 10 hidden layers each is applied as the classifier. A novel experiment is conducted for the collection of EEG data from the subjects. EEG signals of 30 subjects (15 guilty and 15 innocent) are recorded and analyzed during the experiment. BrainVision recorder and analyzer are used for recording and analyzing EEG signals. The model is trained for 90% of the dataset and tested for 10% of the dataset and accuracy of 95% is obtained.

The Roles of Initial Mathematics, Reading, and Cognitive Skills in Subsequent Mathematics Performance: A Meta-Analytic Structural Equation Modeling Approach

2021 ◽  
pp. 003465432110545
Author(s):  
Xin Lin ◽  
Sarah R. Powell
Keyword(s):  
Working Memory ◽  
Structural Equation Modeling ◽  
Cognitive Skills ◽  
Structural Equation ◽  
Meta Analysis ◽  
Time Lag ◽  
Mathematics Performance ◽  
Equation Modeling ◽  
Word Problem Solving ◽  
Significant Moderator

In the present meta-analysis, we systematically investigated the relative contributions of students’ initial mathematics, reading, and cognitive skills on subsequent mathematics performance measured at least 3 months later. With one-stage meta-analytic structural equation modeling, we conducted analyses based on 580,437 students from 265 independent samples and 250 studies. Findings suggested fluency in both mathematics and reading, as well as working memory, yielded greater impacts on subsequent mathematics performance. Age emerged as a significant moderator in the model, such that the effects of comprehensive mathematics and working memory on subsequent mathematics increased with age, whereas attention and self-regulation’s impacts declined with age. Time lag between assessments also emerged as a significant moderator, such that the effects of word-problem solving and word recognition accuracy decreased as the time lag increased, whereas vocabulary, attention, and self-regulation’s effects increased as the time lag increased.

scholarly journals Hot and cold cognitive disturbances in antidepressant-free patients with major depressive disorder: a NeuroPharm study

2020 ◽  
pp. 1-10
Author(s):  
V. H. Dam ◽  
D. S. Stenbæk ◽  
K. Köhler-Forsberg ◽  
C. Ip ◽  
B. Ozenne ◽  
...  
Keyword(s):  
Working Memory ◽  
Major Depressive Disorder ◽  
Reaction Time ◽  
Depressive Disorder ◽  
Healthy Controls ◽  
Cognitive Profiles ◽  
Depression Severity ◽  
Major Depressive ◽  
Depressed Patients ◽  
Cognitive Disturbances

Abstract Background Cognitive disturbances are common and disabling features of major depressive disorder (MDD). Previous studies provide limited insight into the co-occurrence of hot (emotion-dependent) and cold (emotion-independent) cognitive disturbances in MDD. Therefore, we here map both hot and cold cognition in depressed patients compared to healthy individuals. Methods We collected neuropsychological data from 92 antidepressant-free MDD patients and 103 healthy controls. All participants completed a comprehensive neuropsychological test battery assessing hot cognition including emotion processing, affective verbal memory and social cognition as well as cold cognition including verbal and working memory and reaction time. Results The depressed patients showed small to moderate negative affective biases on emotion processing outcomes, moderate increases in ratings of guilt and shame and moderate deficits in verbal and working memory as well as moderately slowed reaction time compared to healthy controls. We observed no correlations between individual cognitive tasks and depression severity in the depressed patients. Lastly, an exploratory cluster analysis suggested the presence of three cognitive profiles in MDD: one characterised predominantly by disturbed hot cognitive functions, one characterised predominantly by disturbed cold cognitive functions and one characterised by global impairment across all cognitive domains. Notably, the three cognitive profiles differed in depression severity. Conclusion We identified a pattern of small to moderate disturbances in both hot and cold cognition in MDD. While none of the individual cognitive outcomes mapped onto depression severity, cognitive profile clusters did. Overall cognition-based stratification tools may be useful in precision medicine approaches to MDD.

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