Omar Mendoza Montoya - Academia.edu (original) (raw)
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Papers by Omar Mendoza Montoya
F1000Research
Background: As cultural institutions embark in projects oriented to digitise art and archaeologic... more Background: As cultural institutions embark in projects oriented to digitise art and archaeological collections in three dimensions, the need for developing means to access the resulting 3D models has become imperative. Shape recognition techniques developed in the field of computer vision can help in this task. Methods: This paper describes the implementation of three shape descriptors, specifically shape distributions, reflective symmetry and spherical harmonics as part of the development of a search engine that retrieves 3D models from an archaeological database without the need of using keywords as query criteria. Use case: The usefulness of this system is obvious in the context of cultural heritage museums, where it is essential to provide automatic access to archaeological and art collections. The prototype described in this paper uses, as study case, 3D models of archaeological objects belonging to Museo del Templo Mayor, a Mexican institution that preserves one of the larges...
Biosignal Processing and Classification Using Computational Learning and Intelligence, 2022
Dendrite ellipsoidal neurons are a novel and different alternative for classification tasks, givi... more Dendrite ellipsoidal neurons are a novel and different alternative for classification tasks, giving competitive results compared with typical classification methods. Based on k-means++ algorithm, the network allows each dendrite to build a hyperellipsoidal in order to assign each incoming pattern \(x_{i}=(x_{1},x_{2},\ldots ,x_{n})^{T}\) to its respective C class. The main disadvantage of this training algorithm is the lack of accuracy in high dimensional datasets. In this research, we solved this problem by training the dendrite ellipsoidal neuron using stochastic gradient descent. Furthermore, electroencephalography data were acquired during two mental conditions (imaginary movements of the left and right hand) in order to test the new training algorithm. The proposed algorithm outperformed the accuracy acquired by a dendrite ellipsoidal neuron based on k-means++ obtaining 76.02% and 62.77%, respectively. Also, the algorithm was compared with multilayer perceptrons and support vec...
This study aims to compare classical and Morphological-Linear Neural Network (MLNN) algorithms fo... more This study aims to compare classical and Morphological-Linear Neural Network (MLNN) algorithms for the intention recognition to perform different movements from electroencephalographic (EEG) signals. Three classification models were implemented and assessed to decode EEG motor imagery signals: (i) Morphological-Linear Neural Network (MLNN) (ii) Support Vector Machine (SVM) and (iii) Multilayer perceptron (MLP). Real EEG signals recorded during robot-assisted rehabilitation therapy were used to evaluate the performance of the proposed algorithms in the classification of three classes (relax, movement intention A Int A and movement intention B Int B) using multi-CSP based features extracted from EEG signals. The results of a ten-fold cross validation show similar results in terms of classification accuracy for the SVM and MLNN models. However, the number of parameters used in each model varies considerably (the MLNN model use less parameters than the SVM). This study indicates potenti...
Currently, one of the challenges in EEG-based brain-computer interfaces (BCI) for neurorehabilita... more Currently, one of the challenges in EEG-based brain-computer interfaces (BCI) for neurorehabilitation is the recognition of the intention to perform different movements from the same limb. This would allow finer control of neurorehabilitation and motor recovery devices by end-users. To address this issue, we 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. This work proposes anticipatory detection scenarios that discriminate EEG signals corresponding to non-movement state and movement intentions of two same-limb movements. The studied movements were discriminated above the empirical chance levels for all proposed detection scenarios. Percentages of correctly anticipated trials ranged from 64.3% to 77.0%, and the detection times ranged from 620 to 300 ms prior to movement initiation. ...
Sensors
Amyotrophic Lateral Sclerosis (ALS) is one of the most aggressive neurodegenerative diseases and ... more Amyotrophic Lateral Sclerosis (ALS) is one of the most aggressive neurodegenerative diseases and is now recognized as a multisystem network disorder with impaired connectivity. Further research for the understanding of the nature of its cognitive affections is necessary to monitor and detect the disease, so this work provides insight into the neural alterations occurring in ALS patients during a cognitive task (P300 oddball paradigm) by measuring connectivity and the power and latency of the frequency-specific EEG activity of 12 ALS patients and 16 healthy subjects recorded during the use of a P300-based BCI to command a robotic arm. For ALS patients, in comparison to Controls, the results (p < 0.05) were: an increment in latency of the peak ERP in the Delta range (OZ) and Alpha range (PO7), and a decreased power in the Beta band among most electrodes; connectivity alterations among all bands, especially in the Alpha band between PO7 and the channels above the motor cortex. The e...
Frontiers in Neuroscience
This work presents the design, implementation, and evaluation of a P300-based brain-machine inter... more This work presents the design, implementation, and evaluation of a P300-based brain-machine interface (BMI) developed to control a robotic hand-orthosis. The purpose of this system is to assist patients with amyotrophic lateral sclerosis (ALS) who cannot open and close their hands by themselves. The user of this interface can select one of six targets, which represent the flexion-extension of one finger independently or the movement of the five fingers simultaneously. We tested offline and online our BMI on eighteen healthy subjects (HS) and eight ALS patients. In the offline test, we used the calibration data of each participant recorded in the experimental sessions to estimate the accuracy of the BMI to classify correctly single epochs as target or non-target trials. On average, the system accuracy was 78.7% for target epochs and 85.7% for non-target trials. Additionally, we observed significant P300 responses in the calibration recordings of all the participants, including the AL...
Sensors
The P300 paradigm is one of the most promising techniques for its robustness and reliability in B... more The P300 paradigm is one of the most promising techniques for its robustness and reliability in Brain-Computer Interface (BCI) applications, but it is not exempt from shortcomings. The present work studied single-trial classification effectiveness in distinguishing between target and non-target responses considering two conditions of visual stimulation and the variation of the number of symbols presented to the user in a single-option visual frame. In addition, we also investigated the relationship between the classification results of target and non-target events when training and testing the machine-learning model with datasets containing different stimulation conditions and different number of symbols. To this end, we designed a P300 experimental protocol considering, as conditions of stimulation: the color highlighting or the superimposing of a cartoon face and from four to nine options. These experiments were carried out with 19 healthy subjects in 3 sessions. The results showe...
Journal of Neurolinguistics, 2016
Brain and Language, 2011
This study presents evidence suggesting that electrophysiological responses to language-related a... more This study presents evidence suggesting that electrophysiological responses to language-related auditory stimuli recorded at 46 weeks postconceptional age (PCA) are associated with language development, particularly in infants with periventricular leukomalacia (PVL). In order to investigate this hypothesis, electrophysiological responses to a set of auditory stimuli consisting of series of syllables and tones were recorded from a population of infants with PVL at 46 weeks PCA. A communicative development inventory (i.e., parent report) was applied to this population during a follow-up study performed at 14 months of age. The results of this later test were analyzed with a statistical clustering procedure, which resulted in two well-defined groups identified as the high-score (HS) and low-score (LS) groups. The eventinduced power of the EEG data recorded at 46 weeks PCA was analyzed using a dimensionality reduction approach, resulting in a new set of descriptive variables. The LS and HS groups formed well-separated clusters in the space spanned by these descriptive variables, which can therefore be used to predict whether a new subject will belong to either of these groups. A predictive classification rate of 80% was obtained by using a linear classifier that was trained with a leave-one-out cross-validation technique.
Neuroscience Letters, 2012
This study had two goals: (a) to describe the results of the Wechsler Adult Intelligence Scale-II... more This study had two goals: (a) to describe the results of the Wechsler Adult Intelligence Scale-III (WAIS-III) and of the quantitative analyses of electroencephalograms (EEG) in elderly adults (60-84 y.o.) that are both healthy and active, and (b) to explore the relationship between the WAIS-III results and EEG in this group. A correlation analysis was made between WAIS-III scores and the Z-transformed absolute power (AP) and relative power (RP) values of delta, theta, alpha, and beta frequency bands. Lower values of delta and theta and higher values of alpha AP and RP were significantly related to better scores in the WAIS-III subtest scores. There were few significant relationships between WAIS-III scores and beta activity. These results suggest that the WAIS-III could be used as a brain function indicator in this age group, paying special attention to the subtests that correlated most significantly with EEG values.
Brain and Language, 2011
This study presents evidence suggesting that electrophysiological responses to language-related a... more This study presents evidence suggesting that electrophysiological responses to language-related auditory stimuli recorded at 46 weeks postconceptional age (PCA) are associated with language development, particularly in infants with periventricular leukomalacia (PVL). In order to investigate this hypothesis, electrophysiological responses to a set of auditory stimuli consisting of series of syllables and tones were recorded from a population of infants with PVL at 46 weeks PCA. A communicative development inventory (i.e., parent report) was applied to this population during a follow-up study performed at 14 months of age. The results of this later test were analyzed with a statistical clustering procedure, which resulted in two well-defined groups identified as the high-score (HS) and low-score (LS) groups. The eventinduced power of the EEG data recorded at 46 weeks PCA was analyzed using a dimensionality reduction approach, resulting in a new set of descriptive variables. The LS and HS groups formed well-separated clusters in the space spanned by these descriptive variables, which can therefore be used to predict whether a new subject will belong to either of these groups. A predictive classification rate of 80% was obtained by using a linear classifier that was trained with a leave-one-out cross-validation technique.
Journal of Neuroscience Methods, 2013
A method for extracting features that differentiate two populations in a neurophysiological exper... more A method for extracting features that differentiate two populations in a neurophysiological experiment is presented. These features are efficiently computed and amenable of clear interpretation. The features provide maximal separation of the subjects and may be used for classification of new subjects. Examples using simulated and real data are presented.
F1000Research
Background: As cultural institutions embark in projects oriented to digitise art and archaeologic... more Background: As cultural institutions embark in projects oriented to digitise art and archaeological collections in three dimensions, the need for developing means to access the resulting 3D models has become imperative. Shape recognition techniques developed in the field of computer vision can help in this task. Methods: This paper describes the implementation of three shape descriptors, specifically shape distributions, reflective symmetry and spherical harmonics as part of the development of a search engine that retrieves 3D models from an archaeological database without the need of using keywords as query criteria. Use case: The usefulness of this system is obvious in the context of cultural heritage museums, where it is essential to provide automatic access to archaeological and art collections. The prototype described in this paper uses, as study case, 3D models of archaeological objects belonging to Museo del Templo Mayor, a Mexican institution that preserves one of the larges...
Biosignal Processing and Classification Using Computational Learning and Intelligence, 2022
Dendrite ellipsoidal neurons are a novel and different alternative for classification tasks, givi... more Dendrite ellipsoidal neurons are a novel and different alternative for classification tasks, giving competitive results compared with typical classification methods. Based on k-means++ algorithm, the network allows each dendrite to build a hyperellipsoidal in order to assign each incoming pattern \(x_{i}=(x_{1},x_{2},\ldots ,x_{n})^{T}\) to its respective C class. The main disadvantage of this training algorithm is the lack of accuracy in high dimensional datasets. In this research, we solved this problem by training the dendrite ellipsoidal neuron using stochastic gradient descent. Furthermore, electroencephalography data were acquired during two mental conditions (imaginary movements of the left and right hand) in order to test the new training algorithm. The proposed algorithm outperformed the accuracy acquired by a dendrite ellipsoidal neuron based on k-means++ obtaining 76.02% and 62.77%, respectively. Also, the algorithm was compared with multilayer perceptrons and support vec...
This study aims to compare classical and Morphological-Linear Neural Network (MLNN) algorithms fo... more This study aims to compare classical and Morphological-Linear Neural Network (MLNN) algorithms for the intention recognition to perform different movements from electroencephalographic (EEG) signals. Three classification models were implemented and assessed to decode EEG motor imagery signals: (i) Morphological-Linear Neural Network (MLNN) (ii) Support Vector Machine (SVM) and (iii) Multilayer perceptron (MLP). Real EEG signals recorded during robot-assisted rehabilitation therapy were used to evaluate the performance of the proposed algorithms in the classification of three classes (relax, movement intention A Int A and movement intention B Int B) using multi-CSP based features extracted from EEG signals. The results of a ten-fold cross validation show similar results in terms of classification accuracy for the SVM and MLNN models. However, the number of parameters used in each model varies considerably (the MLNN model use less parameters than the SVM). This study indicates potenti...
Currently, one of the challenges in EEG-based brain-computer interfaces (BCI) for neurorehabilita... more Currently, one of the challenges in EEG-based brain-computer interfaces (BCI) for neurorehabilitation is the recognition of the intention to perform different movements from the same limb. This would allow finer control of neurorehabilitation and motor recovery devices by end-users. To address this issue, we 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. This work proposes anticipatory detection scenarios that discriminate EEG signals corresponding to non-movement state and movement intentions of two same-limb movements. The studied movements were discriminated above the empirical chance levels for all proposed detection scenarios. Percentages of correctly anticipated trials ranged from 64.3% to 77.0%, and the detection times ranged from 620 to 300 ms prior to movement initiation. ...
Sensors
Amyotrophic Lateral Sclerosis (ALS) is one of the most aggressive neurodegenerative diseases and ... more Amyotrophic Lateral Sclerosis (ALS) is one of the most aggressive neurodegenerative diseases and is now recognized as a multisystem network disorder with impaired connectivity. Further research for the understanding of the nature of its cognitive affections is necessary to monitor and detect the disease, so this work provides insight into the neural alterations occurring in ALS patients during a cognitive task (P300 oddball paradigm) by measuring connectivity and the power and latency of the frequency-specific EEG activity of 12 ALS patients and 16 healthy subjects recorded during the use of a P300-based BCI to command a robotic arm. For ALS patients, in comparison to Controls, the results (p < 0.05) were: an increment in latency of the peak ERP in the Delta range (OZ) and Alpha range (PO7), and a decreased power in the Beta band among most electrodes; connectivity alterations among all bands, especially in the Alpha band between PO7 and the channels above the motor cortex. The e...
Frontiers in Neuroscience
This work presents the design, implementation, and evaluation of a P300-based brain-machine inter... more This work presents the design, implementation, and evaluation of a P300-based brain-machine interface (BMI) developed to control a robotic hand-orthosis. The purpose of this system is to assist patients with amyotrophic lateral sclerosis (ALS) who cannot open and close their hands by themselves. The user of this interface can select one of six targets, which represent the flexion-extension of one finger independently or the movement of the five fingers simultaneously. We tested offline and online our BMI on eighteen healthy subjects (HS) and eight ALS patients. In the offline test, we used the calibration data of each participant recorded in the experimental sessions to estimate the accuracy of the BMI to classify correctly single epochs as target or non-target trials. On average, the system accuracy was 78.7% for target epochs and 85.7% for non-target trials. Additionally, we observed significant P300 responses in the calibration recordings of all the participants, including the AL...
Sensors
The P300 paradigm is one of the most promising techniques for its robustness and reliability in B... more The P300 paradigm is one of the most promising techniques for its robustness and reliability in Brain-Computer Interface (BCI) applications, but it is not exempt from shortcomings. The present work studied single-trial classification effectiveness in distinguishing between target and non-target responses considering two conditions of visual stimulation and the variation of the number of symbols presented to the user in a single-option visual frame. In addition, we also investigated the relationship between the classification results of target and non-target events when training and testing the machine-learning model with datasets containing different stimulation conditions and different number of symbols. To this end, we designed a P300 experimental protocol considering, as conditions of stimulation: the color highlighting or the superimposing of a cartoon face and from four to nine options. These experiments were carried out with 19 healthy subjects in 3 sessions. The results showe...
Journal of Neurolinguistics, 2016
Brain and Language, 2011
This study presents evidence suggesting that electrophysiological responses to language-related a... more This study presents evidence suggesting that electrophysiological responses to language-related auditory stimuli recorded at 46 weeks postconceptional age (PCA) are associated with language development, particularly in infants with periventricular leukomalacia (PVL). In order to investigate this hypothesis, electrophysiological responses to a set of auditory stimuli consisting of series of syllables and tones were recorded from a population of infants with PVL at 46 weeks PCA. A communicative development inventory (i.e., parent report) was applied to this population during a follow-up study performed at 14 months of age. The results of this later test were analyzed with a statistical clustering procedure, which resulted in two well-defined groups identified as the high-score (HS) and low-score (LS) groups. The eventinduced power of the EEG data recorded at 46 weeks PCA was analyzed using a dimensionality reduction approach, resulting in a new set of descriptive variables. The LS and HS groups formed well-separated clusters in the space spanned by these descriptive variables, which can therefore be used to predict whether a new subject will belong to either of these groups. A predictive classification rate of 80% was obtained by using a linear classifier that was trained with a leave-one-out cross-validation technique.
Neuroscience Letters, 2012
This study had two goals: (a) to describe the results of the Wechsler Adult Intelligence Scale-II... more This study had two goals: (a) to describe the results of the Wechsler Adult Intelligence Scale-III (WAIS-III) and of the quantitative analyses of electroencephalograms (EEG) in elderly adults (60-84 y.o.) that are both healthy and active, and (b) to explore the relationship between the WAIS-III results and EEG in this group. A correlation analysis was made between WAIS-III scores and the Z-transformed absolute power (AP) and relative power (RP) values of delta, theta, alpha, and beta frequency bands. Lower values of delta and theta and higher values of alpha AP and RP were significantly related to better scores in the WAIS-III subtest scores. There were few significant relationships between WAIS-III scores and beta activity. These results suggest that the WAIS-III could be used as a brain function indicator in this age group, paying special attention to the subtests that correlated most significantly with EEG values.
Brain and Language, 2011
This study presents evidence suggesting that electrophysiological responses to language-related a... more This study presents evidence suggesting that electrophysiological responses to language-related auditory stimuli recorded at 46 weeks postconceptional age (PCA) are associated with language development, particularly in infants with periventricular leukomalacia (PVL). In order to investigate this hypothesis, electrophysiological responses to a set of auditory stimuli consisting of series of syllables and tones were recorded from a population of infants with PVL at 46 weeks PCA. A communicative development inventory (i.e., parent report) was applied to this population during a follow-up study performed at 14 months of age. The results of this later test were analyzed with a statistical clustering procedure, which resulted in two well-defined groups identified as the high-score (HS) and low-score (LS) groups. The eventinduced power of the EEG data recorded at 46 weeks PCA was analyzed using a dimensionality reduction approach, resulting in a new set of descriptive variables. The LS and HS groups formed well-separated clusters in the space spanned by these descriptive variables, which can therefore be used to predict whether a new subject will belong to either of these groups. A predictive classification rate of 80% was obtained by using a linear classifier that was trained with a leave-one-out cross-validation technique.
Journal of Neuroscience Methods, 2013
A method for extracting features that differentiate two populations in a neurophysiological exper... more A method for extracting features that differentiate two populations in a neurophysiological experiment is presented. These features are efficiently computed and amenable of clear interpretation. The features provide maximal separation of the subjects and may be used for classification of new subjects. Examples using simulated and real data are presented.