Classification of Confusion Level Using EEG Data and Artificial Neural Networks

Renosa, Claire Receli M.; Vicerra, Ryan Rhay P.

doi:10.1109/hnicem48295.2019.9072766

Cited by 12 publications

(7 citation statements)

References 21 publications

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“…An ANN is a machine learning model inspired by the structure and function of the human brain, which has been used for EEG classification. For example, based on the EEG data collected by Wang et al [20], Reñosa et al used the power spectrum of all the brain wave frequencies as features and ANNs to classify low, medium and high levels of confusion [28]. The end-to-end approach can build a classifier from raw EEG data without handcrafted features, which is useful when it is unclear what features to extract [60,61].…”

Section: Eeg-based Emotion Detection Methodsmentioning

confidence: 99%

“…Most EEG applications for educational purposes are noninvasive. The primary EEG frequencies consist of six wave patterns: delta (2-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), lower gamma , and upper gamma (70-150 Hz) [34]. But there are no clear lines that separate the bands.…”

Section: Cognitive States and Band Power Variationsmentioning

confidence: 99%

“…For example, Liang et al studied alpha band changes of confusion states induced by problem-solving and reasoning [27]. Reñosa et al used the power spectrum of all EEG frequencies as features and artificial neural networks (ANNs) to classify low, medium and high levels of confusion [28]. Zhou et al [6] used a commercially available device to collect EEG data and proposed an end-toend method to classify two states: confused and nonconfused, using a labeling technique based on participants' self-assessment.…”

Section: Introductionmentioning

confidence: 99%

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Confused or not: decoding brain activity and recognizing confusion in reasoning learning using EEG

Wang

Zhang

et al. 2023

J. Neural Eng.

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Objective: Confusion is the primary epistemic emotion in the learning process, influencing students' engagement and whether they become frustrated or bored. However, research on confusion in learning is still in its early stages, and there is a need to better understand how to recognize it and what EEG signals indicate its occurrence. The present work investigates confusion during reasoning learning using EEG, and aims to fill this gap with a multidisciplinary approach combining educational psychology, neuroscience and computer science. Approach: First, we design an experiment to actively and accurately induce confusion in reasoning. Second, we propose a subjective and objective joint labeling technique to address the label noise issue. Third, to confirm that the confused state can be distinguished from the non-confused state, we compare and analyze the mean band power of confused and unconfused states across five typical bands. Finally, we present an EEG database for confusion analysis, together with benchmark results from conventional (Naive Bayes, SVM, Random Forest, and ANN) and end-to-end (LSTM, ResNet, and EEGNet) machine learning methods.Main results: Findings revealed: 1. Significant differences in the power of delta, theta, alpha, beta and lower gamma between confused and non-confused conditions; 2. A higher attentional and cognitive load when participants were confused; and 3. The Random Forest algorithm with time-domain features achieved a high accuracy/F1 score (88.06%/0.88 for the subject-dependent approach and 84.43%/0.84 for the subject-independent approach) in the binary classification of the confused and non-confused states.Significance: The study advances our understanding of confusion and provides practical insights for recognizing and analyzing it in the learning process. It extends existing theories on the differences between confused and non-confused states during learning and contributes to the cognitive-affective model. The research enables researchers, educators, and practitioners to monitor confusion, develop adaptive systems, and test recognition approaches.

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Section: Eeg-based Emotion Detection Methodsmentioning

confidence: 99%

Section: Cognitive States and Band Power Variationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Confused or not: decoding brain activity and recognizing confusion in reasoning learning using EEG

Wang

Zhang

et al. 2023

J. Neural Eng.

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“…It records numeric values, determined by proprietary algorithms, reflecting the user's mental states like attention and meditation. The device also logs numeric values for frequency bands, capturing data from 0 to 60Hz every half-second [15]. It has 14 metallic electrodes mounted on a plastic base, meticulously positioned on the scalp in accordance with the internationally recognized 10-20 system [22].…”

Section: A Neurosky Mindsetmentioning

confidence: 99%

“…The device also logs numeric values for frequency bands, capturing data from 0 to 60Hz every half-second [17]. It has 14 metallic electrodes mounted on a plastic base, meticulously positioned on the scalp in accordance with the internationally recognized 10-20 system [25].…”

Section: Neurosky Mindsetmentioning

confidence: 99%

ODL-BCI: Optimal deep learning model for brain-computer interface to classify students confusion via hyperparameter tuning

Miah,

Habiba,

Kabir

2023

Preprint

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Brain-computer interface (BCI) research has gained attention in education contexts, offering the potential to monitor and enhance student’s cognitive states and this study focuses on developing an optimal deep learning model, ODL-BCI, for real-time classification of students’ concentration levels. The model incorporates hyper-parameter tuning techniques and utilizes the publicly available “Confused student EEG brainwave data” dataset. We propose a deep learning model with hyperparameters optimized through Bayesian optimization. The model’s architecture is constructed with an input layer, several hidden layers, and an output layer. The number of nodes and activation functions in the hidden layers is determined using Bayesian optimization. The learning rate is also optimized for each layer. The proposed model is evaluated and compared with several standard machine learning classifiers, including Decision Tree, AdaBoost, Bagging, MLP, Naïve Bayes, Random Forest, SVM, and XG Boost, on an EEG confusion dataset. Experimental results demonstrate that the optimized deep learning model outperforms all other classifiers, achieving an accuracy of 74 percent. The model’s effectiveness in accurately classifying students’ concentration levels highlights its potential as a valuable tool in educational settings. This research contributes to the advancement of BCI technology, providing insights into the optimization of deep learning models for EEG-based cognitive assessment. Future work involves exploring the model’s generalizability on larger datasets and extending its applicability to other BCI applications.

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Mental Confusion Prediction in E-Learning Contexts with EEG and Machine Learning

Trigka,

Dritsas,

Mylonas

2023

Lecture Notes in Networks and Systems

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Classification of Confusion Level Using EEG Data and Artificial Neural Networks

Cited by 12 publications

References 21 publications

Confused or not: decoding brain activity and recognizing confusion in reasoning learning using EEG

Confused or not: decoding brain activity and recognizing confusion in reasoning learning using EEG

ODL-BCI: Optimal deep learning model for brain-computer interface to classify students confusion via hyperparameter tuning

Mental Confusion Prediction in E-Learning Contexts with EEG and Machine Learning

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