Advanced Learner Assistance System's (ALAS) Recent Results

Aguilar-Herrera, Aime Judith; Delgado-Jimenez, Esther Aimee; Candela-Leal, Milton Osiel; Olivas-Martínez, Gustavo; Alvarez-Espinosa, Gabriela Jazmin; Ramirez–Moreno, M.A.; Lozoya-Santos, Jorge de J.; Ramirez‐Mendoza, Ricardo A.

doi:10.1109/ieeeconf53024.2021.9733770

Cited by 6 publications

(7 citation statements)

References 15 publications

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“…Depending on the tasks being undertaken, specific autonomic responses are generated by the human body, with adequate machine learning classification extracting ECG and EDA measurements in a non-invasive manner, and it is possible to identify the type of task being performed [ 110 ]. EEG and cardiac activity have also been used to address the issue of the effects of different learning and teaching methods on the learning process and cognitive state of students with the hopes of implementing personalized learning experiences in the future [ 111 , 112 ].…”

Section: Resultsmentioning

confidence: 99%

Wearable Biosensor Technology in Education: A Systematic Review

Hernández-Mustieles,

Lima-Carmona,

Pacheco-Ramírez

et al. 2024

Sensors

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Wearable Biosensor Technology (WBT) has emerged as a transformative tool in the educational system over the past decade. This systematic review encompasses a comprehensive analysis of WBT utilization in educational settings over a 10-year span (2012–2022), highlighting the evolution of this field to address challenges in education by integrating technology to solve specific educational challenges, such as enhancing student engagement, monitoring stress and cognitive load, improving learning experiences, and providing real-time feedback for both students and educators. By exploring these aspects, this review sheds light on the potential implications of WBT on the future of learning. A rigorous and systematic search of major academic databases, including Google Scholar and Scopus, was conducted in accordance with the PRISMA guidelines. Relevant studies were selected based on predefined inclusion and exclusion criteria. The articles selected were assessed for methodological quality and bias using established tools. The process of data extraction and synthesis followed a structured framework. Key findings include the shift from theoretical exploration to practical implementation, with EEG being the predominant measurement, aiming to explore mental states, physiological constructs, and teaching effectiveness. Wearable biosensors are significantly impacting the educational field, serving as an important resource for educators and a tool for students. Their application has the potential to transform and optimize academic practices through sensors that capture biometric data, enabling the implementation of metrics and models to understand the development and performance of students and professors in an academic environment, as well as to gain insights into the learning process.

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Section: Resultsmentioning

confidence: 99%

Wearable Biosensor Technology in Education: A Systematic Review

Hernández-Mustieles,

Lima-Carmona,

Pacheco-Ramírez

et al. 2024

Sensors

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“…This EEG device offers a noise-canceling mode, and each one was purchased for USD 400, making it an affordable option that is highly convenient and practical for carrying out experiments under real-life settings. It has been used in previous investigations to estimate the level of mental fatigue in students using EEG signals and machine learning models, thus functioning in a system that analyzes biometric signals in real time [ 3 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

“…Studies investigating brain synchrony in both competitive and collaborative scenarios have already been conducted, and similar experimental designs demonstrate that collaborative tasks exhibit higher inter-brain synchrony and show better affinity in results [ 33 , 36 ]. The applied models and architectural foundation of the code were derived from the Advanced Learner Assistance System (ALAS) [ 3 ], employing techniques that have undergone statistical validation [ 4 ]. The focus is now shifted towards transforming the system into a complete real-time algorithm, enhancing its applicability in educational scenarios, considering that it has been investigated how B2B synchrony is related to students’ class engagement and how it implicates attention mechanisms that can impact teaching effectiveness [ 12 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

“…The aim of this work is to present the implementation of a real-time algorithm for estimating brain synchronization using wireless EEG. The architecture for synchronized data acquisition is based on the Advanced Learner Assistance System (ALAS) [ 3 ], adding new real-time processing capabilities. The brain-to-brain (B2B) feature extraction follows the methods presented by Ramírez-Moreno et al [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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Implementation of a Real-Time Brain-to-Brain Synchrony Estimation Algorithm for Neuroeducation Applications

Mendoza-Armenta,

Blanco-Téllez,

García-Alcántar

et al. 2024

Sensors

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This study centers on creating a real-time algorithm to estimate brain-to-brain synchronization during social interactions, specifically in collaborative and competitive scenarios. This type of algorithm can provide useful information in the educational context, for instance, during teacher–student or student–student interactions. Positioned within the context of neuroeducation and hyperscanning, this research addresses the need for biomarkers as metrics for feedback, a missing element in current teaching methods. Implementing the bispectrum technique with multiprocessing functions in Python, the algorithm effectively processes electroencephalography signals and estimates brain-to-brain synchronization between pairs of subjects during (competitive and collaborative) activities that imply specific cognitive processes. Noteworthy differences, such as higher bispectrum values in collaborative tasks compared to competitive ones, emerge with reliability, showing a total of 33.75% of significant results validated through a statistical test. While acknowledging progress, this study identifies areas of opportunity, including embedded operations, wider testing, and improved result visualization. Beyond academia, the algorithm’s utility extends to classrooms, industries, and any setting involving human interactions. Moreover, the presented algorithm is shared openly, to facilitate implementations by other researchers, and is easily adjustable to other electroencephalography devices. This research not only bridges a technological gap but also contributes insights into the importance of interactions in educational contexts.

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“…These metrics were selected to offer a comprehensive understanding of model performance and to ensure a reliable choice was made. Metrics were calculated as in Aguilar-Herrera et al ( 2021 ), where True Positives (TP), True Negatives (TN), False Positives (FP) or type-I errors, and False Negatives (FN) or type-II errors are used to build up these metrics.…”

Section: Methodsmentioning

confidence: 99%

Real-time EEG-based emotion recognition for neurohumanities: perspectives from principal component analysis and tree-based algorithms

Blanco-Ríos,

Candela-Leal,

Orozco-Romo

et al. 2024

Front. Hum. Neurosci.

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Within the field of Humanities, there is a recognized need for educational innovation, as there are currently no reported tools available that enable individuals to interact with their environment to create an enhanced learning experience in the humanities (e.g., immersive spaces). This project proposes a solution to address this gap by integrating technology and promoting the development of teaching methodologies in the humanities, specifically by incorporating emotional monitoring during the learning process of humanistic context inside an immersive space. In order to achieve this goal, a real-time emotion recognition EEG-based system was developed to interpret and classify specific emotions. These emotions aligned with the early proposal by Descartes (Passions), including admiration, love, hate, desire, joy, and sadness. This system aims to integrate emotional data into the Neurohumanities Lab interactive platform, creating a comprehensive and immersive learning environment. This work developed a ML, real-time emotion recognition model that provided Valence, Arousal, and Dominance (VAD) estimations every 5 seconds. Using PCA, PSD, RF, and Extra-Trees, the best 8 channels and their respective best band powers were extracted; furthermore, multiple models were evaluated using shift-based data division and cross-validations. After assessing their performance, Extra-Trees achieved a general accuracy of 94%, higher than the reported in the literature (88% accuracy). The proposed model provided real-time predictions of VAD variables and was adapted to classify Descartes' six main passions. However, with the VAD values obtained, more than 15 emotions can be classified (reported in the VAD emotion mapping) and extend the range of this application.

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Advanced Learner Assistance System's (ALAS) Recent Results

Cited by 6 publications

References 15 publications

Wearable Biosensor Technology in Education: A Systematic Review

Wearable Biosensor Technology in Education: A Systematic Review

Implementation of a Real-Time Brain-to-Brain Synchrony Estimation Algorithm for Neuroeducation Applications

Real-time EEG-based emotion recognition for neurohumanities: perspectives from principal component analysis and tree-based algorithms

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