Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551014
Pertenecen a esta colección Tesis y Trabajos de grado de los Doctorados correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Psychophysiological evaluation of an online method for learning aimed at children with reading and mathematical difficulties(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-07-04) Corona González, César Emmanuel; Alonso Valerdi, Luz María; emipsanchez; Gómez Velázquez, Fabiola Reveca; Ramírez Moreno, Mauricio Adolfo; Ericka Janet Rechy Ramírez; School of Engineering and Sciences; Campus Monterrey; Ibarra Zárate, David IsaacThe present research aims to evaluate the effectiveness of Smartick, a serious game that includes an online method for learning, aimed at improving educational outcomes among children with reading or math difficulties. Although serious games are increasingly used in academic settings, many lack a strong pedagogical foundation, making it difficult to identify truly effective tools. To address this gap, 76 children aged 7 to 13 were recruited for this study (𝐱̅=𝟗.𝟖𝟖,𝐒𝐃=𝟏.𝟒𝟒) who come from unfavorable socioeconomic conditions and family environments. The methodology was structured in four stages. In stage 1, a screening evaluation for reading and math skills, where two groups were formed, reading difficulties and math difficulties. This assessment addressed (1) reading ability, (2) mathematical fluency, (3) calculation, (4) orthographic errors, (5) phonological errors, and (6) selective attention. Then, stage 2 consisted of a first psychometric and electrophysiological evaluation. The reading difficulties group underwent psychometric assessments focused on reading speed and reading comprehension, whereas the math difficulties group were assessed in math knowledge. Moreover, IQ was estimated for both groups. After that, EEG recordings were collected for each child in baseline state and while performing either a reading or math activity. Additionally, EEG task performance was considered in the process regarding correct answers and response time. During stage 3, each child was reallocated into the experimental subgroup (reading group, n = 19; math group, n = 19), where Smartick must be used, or the control subgroup (reading group, n = 16; math group, n = 22), who did not receive any intervention. Both groups were followed up for 3 months. Finally, stage 4 comprised a second psychometric and EEG assessment. Power Spectral Density was calculated across 15 regions, within theta (4-8 Hz), alpha (8-13 Hz), low beta (13-20 Hz), and high beta (20-30 Hz) bands. Psychometric results suggest that the experimental subgroups improved in reading comprehension (𝒑=𝟎.𝟎𝟑𝟔𝟑) and mathematical knowledge (𝒑=𝟎.𝟎𝟐𝟔𝟏), respectively, compared to control groups. Statistical analysis revealed that children in the RDG experimental group showed significant changes in all bands in left parietal, centroparietal, and temporal areas. However, only small effect size was found in the left temporal area. In contrast, the control group showed no significant difference across any frequency band. On the other hand, no notable EEG synchronization or desynchronization patterns were observed in either the experimental or control groups in the MDG. Effect size revealed that negligible significant differences were found across the bands. On the other hand, the experimental group in the MDG exhibited small effect sizes in the left centroparietal region, known for its role in working memory during mental arithmetic, and the right frontocentral, temporal, and centroparietal regions, which are linked to visuospatial numerical processing. While no patterns indicative of improved learning was identified, this work shows a trend between EEG power values and language-related regions that would be worth investigating in depth. It is suggested that the low IQ levels and adverse conditions of the participants may have limited the children's performance, so a more homogeneous sample in terms of intellectual capacity and socioeconomic status could reveal more significant changes.
- A novel feature extraction methodology using Inter-Trial Coherence framework for signal analysis – A case study applied towards BCI(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11) López Bernal, Diego; Ponce Cruz, Pedro; emipsanchez; Ponce Espinosa, Hiram; López Caudana, Edgar Omar; Bustamante Bello, Martín Rogelio; School of Engineering and Sciences; Campus Ciudad de México; Balderas Silva, David ChristopherSignal classification in environments with low signal-to-noise ratio (SNR) presents a significant challenge across various fields, from industrial monitoring to biomedical appli cations. This work explores a novel methodology aimed at improving classification accuracy in such conditions, using EEG-based Brain-Computer Interfaces (BCIs) for inner speech decoding as a case study. EEG-based Brain-Computer Interfaces (BCIs) have emerged as a promising technology for providing communication channels for individuals with speech disabilities, such as those affected by amyotrophic lateral sclerosis (ALS), stroke, or other neurodegenerative diseases. Inner speech classification, a subset of BCI applications, aims to interpret and translate silent, inner speech into meaningful linguistic information. De spite the potential of BCIs, current methodologies for inner speech classification lack the accuracy needed for practical applications. This work investigates the use of inter-trial coherence (ITC) as a novel feature extraction technique to enhance the accuracy of in ner speech classification in EEG-based BCIs. The study introduces a methodology that integrates ITC within a complex Morlet time-frequency representation framework. EEG recordings from ten participants imagining four distinct words (up, down, right, and left) were processed and analyzed. Five different classification algorithms were evaluated: Ran dom Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), and Naive Bayes (NB). The proposed method achieved no table classification accuracies of 75.70% with RF and 66.25% with SVM, demonstrating significant improvements over traditional feature extraction methods. These findings indi cate that ITC is a viable technique for enhancing the accuracy of inner speech classification in EEG-based BCIs. The results suggest practical implications for improving communica tion and navigation capabilities for individuals with ALS or similar conditions. This work lays the foundation for future research on phase-based feature extraction, opening new avenues for understanding the neural mechanisms underlying inner speech and advancing BCI systems’ accuracy and efficiency
- Classification of EEG signals: an assistance approach for remote rehabilitation for the upper limb(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024) Lazcano Herrera, Alicia Guadalupe; Alfaro Ponce, Mariel; emipsanchez; Chairez Oria, Jorge Isaac; González Mendoza, Miguel; Guzmán Zavaleta, Zobeida Jezabel; School of Engineering and Sciences; Campus Estado de México; Fuentes Aguilar, Rita QuetziquelRehabilitation technologies help disabled people face the many challenges in their daily lives. As a consequence, there has been an increase in the interest in developing technologies such as Human-Computer Interfaces (HCI) and Brain-Computer Interfaces (BCI). These technologies can be triggered by many biosignals and their related studies or extraction techniques, being one of these biosignals the ones related to information on brain activity. Electroencephalography represents electrical brain activity as a form of brain signal; the records produced by this technique are called electroencephalograms (EEG).This technique involves the pickup of the biopotential, the signal conditioning, the signal recording, and the signal analysis, being one of their main goals the observation and analysis of brain responses to sensor stimuli.Despite the many advantages of the use of EEG signals and other technologies for BCI composition, one of the challenges we face is the complexity of interpreting and classifying EEG signals. This is where the use of Artificial Intelligence (AI) and Machine Learning(ML) algorithms becomes crucial. The development of ML algorithms for EEG signal analysis is not just a trend but a necessity in our quest to understand and harness the power of brain signals.Nowadays, to analyze brain signals, algorithms such as Neural networks have been used, and among all the architectures available, Recurrent Neural Networks become popular because they can provide context in their predictions. In this category can be found the Long- Short Term Memory (LSTM) networks, which are NN’s with a memory block that can ”store”information. Using this ML algorithm for the analysis of EEG signals could help develop new technologies that could assist impaired people aided with technologies like remote assistance or remote rehabilitation. The present dissertation aims to apply different techniques which involve Machine Learning (ML) techniques, to analyze, process, and classify EEG signals to integrate the information derived into an application that can be used to apply remote rehabilitation aid. This dissertation is divided into two major axes: one focuses on the EEG signals and analysis and the second axis is focused on the application of ML algorithms for classifying Motor/Imagery(MI) information that could be integrated into a remote rehabilitation application. It will discuss the results obtained in the use of Time-Domain and Frequency-Domain techniques for extraction features of EEG signals in publicly available datasets (Physionet Motor/Imagery dataset) and an acquired dataset that could replicate the information found in the literature, the application of ML algorithms for feature selection, the advantages of the normalization process, the application of Neural Networks (two types, recurrent neural networks, and convolutional neural networks) to classify EEG MI information and how can this be integrated into a platform for remote rehabilitation that helps to avoid the abandonment of therapy and that offers supports to take rehabilitation measures in remote places. These results remark the use of the BiLSTM NNs for EEG MI information classification with an accuracy of 91.25% and the use of the Convolutional Neural Network SquezeenNet with a maximum accuracy reported of 92.23%