Analysis of Electroencephalography (EEG) Signals and Its Categorization–A Study
TL;DR: Electroencephalography signals and its characterization with respect to various states of human body and experimental setup used in EEG analysis are focused on.
About: This article is published in Procedia Engineering.The article was published on 2012-01-01 and is currently open access. It has received 201 citations till now. The article focuses on the topics: EEG-fMRI & Electroencephalography.
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TL;DR: The study presents a brief comparison of various functional neuroimaging techniques, revealing the excellent Neuroimaging capabilities of EEG signals such as high temporal resolution, inexpensiveness, portability, and non-invasiveness as compared to the other techniques such as positron emission tomography, magnetoencephalogram, functional magnetic resonance imaging, and transcranial magnetic stimulation.
113 citations
TL;DR: Generalizes a methodology for building machine learning pipelines for multimodal educational data, using a modularized approach, namely the "grey‐box" approach and demonstrates that fusion of eye‐tracking, facial expressions and arousal data provide the best prediction of effort and performance in adaptive learning settings.
Abstract: Students' on‐task engagement during adaptive learning activities has a significant effect on their performance, and at the same time, how these activities influence students' behavior is reflected in their effort exertion. Capturing and explaining effortful (or effortless) behavior and aligning it with learning performance within contemporary adaptive learning environments, holds the promise to timely provide proactive and actionable feedback to students. Using sophisticated machine learning (ML) algorithms and rich learner data, facilitates inference‐making about several behavioral aspects (including effortful behavior) and about predicting learning performance, in any learning context. Researchers have been using ML methods in a "black‐box" approach, ie, as a tool where the input data is the learner data and the output is a given class from the chosen construct. This work proposes a methodological shift from the "black‐box" approach to a "grey‐box" approach that bridges the hypothesis/literature‐driven (feature extraction) "white‐box" approach with the computation/data‐driven (feature fusion) "black‐box" approach. This will allow us to utilize data features that are educationally and contextually meaningful. This paper aims to extend current methodological paradigms, and puts into practice the proposed approach in an adaptive self‐assessment case study taking advantage of new, cutting‐edge, interdisciplinary work on building pipelines for educational data, using innovative tools and techniques. Practitioner NotesWhat is already known about this topic Capturing and measuring learners' engagement and behavior using physiological data has been explored during the last years and exhibits great potential.Effortless behavioral patterns commonly exhibited by learners, such as "cheating," "guessing" or "gaming the system" counterfeit the learning outcome.Multimodal data can accurately predict learning engagement, performance and processes.What this paper adds Generalizes a methodology for building machine learning pipelines for multimodal educational data, using a modularized approach, namely the "grey‐box" approach.Showcases that fusion of eye‐tracking, facial expressions and arousal data provide the best prediction of effort and performance in adaptive learning settings.Highlights the importance of fusing data from different channels to obtain the most suited combinations from the different multimodal data streams, to predict and explain effort and performance in terms of pervasiveness, mobility and ubiquity.Implications for practice and/or policy Learning analytics researchers shall be able to use an innovative methodological approach, namely the "grey‐box," to build machine learning pipelines from multimodal data, taking advantage of artificial intelligence capabilities in any educational context.Learning design professionals shall have the opportunity to fuse specific features of the multimodal data to drive the interpretation of learning outcomes in terms of physiological learner states.The constraints from the educational contexts (eg, ubiquity, low‐cost) shall be catered using the modularized gray‐box approach, which can also be used with standalone data sources. [ABSTRACT FROM AUTHOR]
62 citations
Cites background from "Analysis of Electroencephalography ..."
...For example, the alpha band power has been associated with attention (Huang, Jung, & Makeig, 2007), the lower-beta band is related tomemory and theta is related to cognitive load (Kumar & Bhuvaneswari, 2012)....
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TL;DR: In this article, a new approach for extension of univariate iterative filtering (IF) for decomposing a signal into intrinsic mode functions (IMFs) or oscillatory modes is proposed for multivariate multi-component signals.
59 citations
TL;DR: The mean phase coherence method, based on the “phase-locking value,” was the most frequently used functional estimation technique in the reviewed studies and the unweighted functional brain network has received substantially more attention in the literature than the weighted network.
Abstract: Graph theory analysis, a mathematical approach, has been applied in brain connectivity studies to explore the organization of network patterns. The computation of graph theory metrics enables the characterization of the stationary behavior of electroencephalogram (EEG) signals that cannot be explained by simple linear methods. The main purpose of this study was to systematically review the graph theory applications for mapping the functional connectivity of the EEG data in neuroergonomics. Moreover, this article proposes a pipeline for constructing an unweighted functional brain network from EEG data using both source and sensor methods. Out of 57 articles, our results show that graph theory metrics used to characterize EEG data have attracted increasing attention since 2006, with the highest frequency of publications in 2018. Most studies have focused on cognitive tasks in comparison with motor tasks. The mean phase coherence method, based on the “phase-locking value,” was the most frequently used functional estimation technique in the reviewed studies. Furthermore, the unweighted functional brain network has received substantially more attention in the literature than the weighted network. The global clustering coefficient and characteristic path length were the most prevalent metrics for differentiating between global integration and local segregation, and the small-worldness property emerged as a compelling metric for the characterization of information processing. This review provides insight into the use of graph theory metrics to model functional brain connectivity in the context of neuroergonomics research.
48 citations
Cites background from "Analysis of Electroencephalography ..."
...The temporal lobe is associated with memory, speech, and the recognition of auditory stimuli, whereas the occipital lobe is related to visual responses [27], [29]–[35]....
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TL;DR: In this article , a spectral feature-based two-layer LSTM network model was proposed for automatic prediction of epileptic seizures using long-term multichannel EEG signals, which makes use of spectral power and mean spectrum amplitude features of delta, theta, alpha, beta, and gamma bands of 23-channel EEG spectrum for this task.
Abstract: Abstract Epilepsy is a chronic nervous disorder, which disturbs the normal daily routine of an epileptic patient due to sudden seizure onset. In this era of smart healthcare, automated seizure prediction techniques could assist the patients, their family, and medical personnel to control and manage these seizures. This paper proposes a spectral feature-based two-layer LSTM network model for automatic prediction of epileptic seizures using long-term multichannel EEG signals. This model makes use of spectral power and mean spectrum amplitude features of delta, theta, alpha, beta, and gamma bands of 23-channel EEG spectrum for this task. Initially, the proposed single-layer and two-layer LSTM models have been evaluated for EEG segments having durations in the range of 5–50 s for 24 epileptic subjects, out of which EEG segments of 30 s duration are found to be useful for accurate seizure prediction using two-layer LSTM model. Afterwards, to validate the performance of this classifier, the spectral features of 30 s duration EEG segments are fed to random forest, decision tree, k-nearest neighbour, support vector machine, and naive Bayes classifiers, which are empowered with grid search-based parameter estimation. Finally, the iterative simulation results and comparison with recently published existing techniques firmly reveal that the proposed two-layer LSTM model with EEG spectral features is an effective technique for accurately predicting seizures in real time with an average classification accuracy of 98.14%, average sensitivity of 98.51%, and average specificity of 97.78%, thereby enabling the epileptic patients to have a better quality of life.
36 citations
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TL;DR: This review concentrates on scalp-recorded direct current potentials that appear as event-related potentials (ERPs) in humans that have been recorded on the scalp for the first time.
Abstract: This review concentrates on scalp-recorded direct current potentials that appear as event-related potentials (ERPs)
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