Ciencias Exactas 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 Christopher
Signal 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
Decoding of motor information from noninvasive electroencephalographic signals for brain-computer interfaces
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-01-29) Hernández Rojas, Luis Guillermo; Antelis Ortíz, Javier Mauricio; qro /|bqrotbecerra; Caraza Camacho, Ricardo; Cantillo Negrete, Jessica; Martínez Mozos, Oscar; School of Engineering and Sciences; Campus Estado de México; Mendoza Montoya, Omar
Brain-Computer Interfaces (BCIs) are emerging assistive technologies that provide an artificial communication pathway between the brain and the external world. These systems translate a mental task performed by the user into commands to control external devices using brain signals recorded with invasive or noninvasive techniques. This is remarkably interesting for different applications related with neuromotor rehabilitation field, for example, BCI systems for neurorehabilitation therapies where BCIs provides patients with motor impairments with a non-muscular communication channel that could be used to activate a robot-assisted rehabilitation device. However, there are other applications not related to the neurorehabilitation field where this technology provides an enhancement for the communication between the user and the its environment. An example of this is the BCI’s for automotive applications, where BCI technology is applied as a part of Advanced Driving Assistance Systems to avoid crash vehicle situations. Irrespective of the type of application, movement-related BCI systems use the motor imagery (MI) paradigm as the mental task that the user performs and which the system detects and classifies by generating commands to drive external devices Despite the success of Motor Imagery-based BCI systems, there are some characteristics of these interfaces that are susceptible to be improved. First, to improve the performance of mental task detection, novel classification models can be explored to compare their performance with the conventional classification models used in BCI (such as Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM)). Secondly, there are applications in which the motor imagery paradigm has limitations that avoid the BCI system to be able to detect multiple mental motor tasks related to diverse movements generated by the same limb. In addition, the MI paradigm is not fully adaptable to detect intentions to execute sudden movements, which is important for applications where the objective of BCI is to support and complement the rehabilitation therapies for people with the ability to recover their physical motor functions. Finally, the validation of neurorehabilitation therapies based on BCI online for end users (people with motor disabilities). It is necessary to evaluate the usefulness of this technology in the rehabilitation of patients with motor disabilities. This PhD thesis investigates the detection of information related to movements from non-invasive EEG signals exploring potential solutions to the limitations of conventional Motor BCI systems. The first study explores novel classification models as those based on deep learning which could improve the BCI system robustness and performance. This study aims to compare classical and Deep Neural Networks (DNN) algorithms for the recognition of Motor Imagery (MI) tasks from electroencephalographic (EEG) signals. The second study investigates the detection of emergency braking from driver’s electroencephalographic (EEG) signals that precede the brake pedal actuation. EEG signals were classified using support vector machines (SVM) and convolutional neural networks (CNN) in order to discriminate between braking intention and normal driving. The third study assess the feasibility of recognizing two rehabilitative right upper-limb movements from pre-movement EEG signals. These rehabilitative movements were performed self-selected and self-initiated by the users using a motor rehabilitation robotic device. We proposes diverse anticipatory detection scenarios that discriminate EEG signals corresponding to non-movement state and movement intentions of two same-limb movements. Finally, the last study is focused on the development of a BCI-driven functional electro-stimulation system (FES) aimed at neurorehabilitation of the upper limbs of patients with spinal cord injuries (SCI). Furthermore, clinical benefits of the BCI-FES system in SCI patients are explored by estimating quantitative EEG parameters for motor rehabilitation.

Ciencias Exactas y Ciencias de la Salud

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