ArticleSensors (Basel, Switzerland)2018
Characterizing Focused Attention and Working Memory Using EEG.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 65 citations in OpenAlex.
- Hybrid EEG Feature Learning Method for Cross-Session Human Mental Attention State Classification.Brain sciences · 2025Article
- Electroencephalography-based psychological and physiological effects of winter virtual forest trail landscapes on youth.Scientific reports · 2025Article
- Who is WithMe? EEG features for attention in a visual task, with auditory and rhythmic support.Frontiers in neuroscience · 2024Article
- Decoding the cognitive states of attention and distraction in a real-life setting using EEG.Scientific reports · 2022Article
- EEG based cognitive task classification using multifractal detrended fluctuation analysis.Cognitive neurodynamics · 2021Article
- Assessment of mental fatigue and stress on electronic sport players with data fusion.Medical & biological engineering & computing · 2021Article
- Improvement of Attention in Elementary School Students through Fixation Focus Training Activity.International journal of environmental research and public health · 2020Article
- The Automatic Detection of Cognition Using EEG and Facial Expressions.Sensors (Basel, Switzerland) · 2020Article
- Classification of Pain Event Related Potential for Evaluation of Pain Perception Induced by Electrical Stimulation.Sensors (Basel, Switzerland) · 2020Article
- Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning.Sensors (Basel, Switzerland) · 2019Article
- Using Eye Tracking to Assess Gaze Concentration in Meditation.Sensors (Basel, Switzerland) · 2019Article
- Parallel Mechanism of Spectral Feature-Enhanced Maps in EEG-Based Cognitive Workload Classification.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.