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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- 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
