Daniel Guillermo García-Murillo

Bio: Daniel Guillermo García-Murillo is an academic researcher from National University of Colombia. The author has contributed to research in topics: Motor imagery & Feature selection. The author has an hindex of 2, co-authored 5 publications receiving 7 citations.

Single-Trial Kernel-Based Functional Connectivity for Enhanced Feature Extraction in Motor-Related Tasks

Abstract: Motor learning is associated with functional brain plasticity, involving specific functional connectivity changes in the neural networks. However, the degree of learning new motor skills varies among individuals, which is mainly due to the between-subject variability in brain structure and function captured by electroencephalographic (EEG) recordings. Here, we propose a kernel-based functional connectivity measure to deal with inter/intra-subject variability in motor-related tasks. To this end, from spatio-temporal-frequency patterns, we extract the functional connectivity between EEG channels through their Gaussian kernel cross-spectral distribution. Further, we optimize the spectral combination weights within a sparse-based l2-norm feature selection framework matching the motor-related labels that perform the dimensionality reduction of the extracted connectivity features. From the validation results in three databases with motor imagery and motor execution tasks, we conclude that the single-trial Gaussian functional connectivity measure provides very competitive classifier performance values, being less affected by feature extraction parameters, like the sliding time window, and avoiding the use of prior linear spatial filtering. We also provide interpretability for the clustered functional connectivity patterns and hypothesize that the proposed kernel-based metric is promising for evaluating motor skills.

Individual Detection of Citrus and Avocado Trees Using Extended Maxima Transform Summation on Digital Surface Models

Abstract: Individual tree detection (ITD) locates plants from images to estimate monitoring parameters, helping the management of forestry and agriculture systems. As a low-cost solution to help farm monitoring, digital surface models are increasingly involved together with mathematical morphology techniques within the framework of ITD tasks. However, morphology-based approaches are prone to omission and commission errors due to the shape and size of structuring elements. To reduce the error rate in ITD tasks, we introduce a morphological transform that is based on the local maxima segmentation (Cumulative Summation of Extended Maxima transform (SEMAX)) with the aim to enhance the seed selection by extracting information collected from different heights. Validation is performed on data collected from the plantations of citrus and avocado using different measures of precision. The results obtained by the SEMAX approach show that the devised ITD algorithm provides enough accuracy, and achieves the lowest false-negative rate than other compared state-of-art approaches do.

EEG Source Imaging Based on Dynamic Sparse Coding as ADHD Biomarker

Abstract: The study of the psychiatric disorder denominated Attention-deficit hyperactivity disorder (ADHD) demands the assessment of specific behavior, measured and evaluated through biomarkers like the neuroimaging that is applied due to the assumed association between with changes in the structure and function of the ADHD brain Because of the provided time resolution, Electroencephalographic (EEG) signals and derived versions have recently gained increased attention for studying event-related potentials (ERPs) Moreover, relate to the ADHD diagnosis, techniques of EEG/ERP source imaging (ESI) are effective to locate brain areas related to attention task and analyze spatiotemporal patterns of the P300 wave Therefore, with the aim to accurately determine the spatial location and temporal patterns involved in attention task, there is a need for implementing an adequate ERP marker able to incorporate the spatial and temporal prior information to the ESI solution In this paper, the influence of the source reconstruction is evaluated on visual and auditory evoked potentials through an ESI solution, namely, Dynamic Sparse Coding that is based on physiological motivated spatio-temporal constraints over the source representation As a result, the DSC-based approach improves the characterization of the spatio-temporal dynamics of the attentional evoked potentials processes, including reduced amplitudes in the P300 components of the ERPs in the ADHD group

Sparse-Based Feature Selection for Discriminating Between Crops and Weeds Using Field Images

Abstract: Control of weed growing in yields is a critical task for reducing crop losses. Recently, image-based systems attempt to discriminate between crops and weeds from a set of features. Although some features have a physiological meaning, most of them are redundant or noisy. Therefore, selecting relevant features must result in interpretable and accurate results while reducing the computational complexity of the system. In this work, we introduce a sparse-based feature selection approach using the Lasso operator that eliminates noisy features aiming to improve the classification of crops. We evaluate our proposal on the Crop/Weed Field Image Dataset, for which we tune the parameters by maximizing the accuracy and minimizing feature dimension. Achieved performance results evidence that our proposed approach improves discrimination in comparison with other feature selection approaches, with the benefit of providing interpretability in weed/crop discrimination tasks.

Relevance of Filter Bank Common Spatial Patterns Using Multiple Kernel Learning in Motor Imagery

Abstract: Brain-Computer Interfaces directly communicate the human brain and machines through the analysis of sensorimotor activity, relying on the Motor Imagery paradigm of cognitive neuroscience. Conventional BCI systems use electroencephalographic signals due to its high temporal resolution, portability, and easiness to implement, for which the filter-banked analysis works as the characterization baseline. Due to such analysis yields to highly dimensional representation spaces leading to overtrained systems, we propose to combine the multiple spectral bands into a single representation space through the maximization of the centered kernel alignment criterion. As a result, the similarity between the measured EEG data and the available label sets is maximized, with the additional benefit of enhancing the spectral interpretation of the subject performance. The proposed \(\upkappa \)-FB is evaluated in the dataset IIa of the BCI competition IV for a binary classification task. Attained accuracy proves that \(\upkappa \)-FB outperforms other filter-banked representations without compromising the system confidence.

Complex Pearson Correlation Coefficient for EEG Connectivity Analysis

Abstract: In the background of all human thinking—acting and reacting are sets of connections between different neurons or groups of neurons. We studied and evaluated these connections using electroencephalography (EEG) brain signals. In this paper, we propose the use of the complex Pearson correlation coefficient (CPCC), which provides information on connectivity with and without consideration of the volume conduction effect. Although the Pearson correlation coefficient is a widely accepted measure of the statistical relationships between random variables and the relationships between signals, it is not being used for EEG data analysis. Its meaning for EEG is not straightforward and rarely well understood. In this work, we compare it to the most commonly used undirected connectivity analysis methods, which are phase locking value (PLV) and weighted phase lag index (wPLI). First, the relationship between the measures is shown analytically. Then, it is illustrated by a practical comparison using synthetic and real EEG data. The relationships between the observed connectivity measures are described in terms of the correlation values between them, which are, for the absolute values of CPCC and PLV, not lower that 0.97, and for the imaginary component of CPCC and wPLI—not lower than 0.92, for all observed frequency bands. Results show that the CPCC includes information of both other measures balanced in a single complex-numbered index.

Comparison of Canopy Shape and Vegetation Indices of Citrus Trees Derived from UAV Multispectral Images for Characterization of Citrus Greening Disease

Abstract: Citrus greening is a severe disease significantly affecting citrus production in the United States because the disease is not curable with currently available technologies. For this reason, monitoring citrus disease in orchards is critical to eradicate and replace infected trees before the spread of the disease. In this study, the canopy shape and vegetation indices of infected and healthy orange trees were compared to better understand their significant characteristics using unmanned aerial vehicle (UAV)-based multispectral images. Individual citrus trees were identified using thresholding and morphological filtering. The UAV-based phenotypes of each tree, such as tree height, crown diameter, and canopy volume, were calculated and evaluated with the corresponding ground measurements. The vegetation indices of infected and healthy trees were also compared to investigate their spectral differences. The results showed that correlation coefficients of tree height and crown diameter between the UAV-based and ground measurements were 0.7 and 0.8, respectively. The UAV-based canopy volume was also highly correlated with the ground measurements (R2 > 0.9). Four vegetation indices—normalized difference vegetation index (NDVI), normalized difference RedEdge index (NDRE), modified soil adjusted vegetation index (MSAVI), and chlorophyll index (CI)—were significantly higher in healthy trees than diseased trees. The RedEdge-related vegetation indices showed more capability for citrus disease monitoring. Additionally, the experimental results showed that the UAV-based flush ratio and canopy volume can be valuable indicators to differentiate trees with citrus greening disease.

High-Accuracy Classification of Attention Deficit Hyperactivity Disorder With l 2,1 -Norm Linear Discriminant Analysis and Binary Hypothesis Testing

Abstract: Attention Deficit Hyperactivity Disorder (ADHD) is a high incidence of neurobehavioral disease in school-age children. Its neurobiological diagnosis (or classification) is meaningful for clinicians to give proper treatment for ADHD patients. The existing ADHD classification methods suffer from two problems, i.e., insufficient data and noise disturbance. In this paper, a high-accuracy classification method is proposed by using brain Functional Connectivity (FC) as ADHD features, where an ${l_{2,1}}$ -norm Linear Discriminant Analysis (LDA) model and a binary hypothesis testing framework are effectively employed. In detail, we introduce a binary hypothesis testing framework to cope with insufficient data of ADHD database. The FCs of test data (without seeing its label) are used for training and thus affect the subspace learning of training data under binary hypotheses. On other hand, the ${l_{2,1}}$ -norm LDA model generates a subspace to represent ADHD features, aiming to overcome noise disturbance. By robustly learning ADHD features, the subspace energy difference between binary hypotheses becomes more discriminative. Thereby, the true hypothesis can be rightly estimated with its larger subspace energy, which provides reliable evidence to predict the label of test data. By the test on ADHD-200 database, it shows that our method outperforms other state-of-the-art methods with the significant average accuracy of 97.6%. Moreover, the corresponding result analysis with ADHD symptom score and the explanation of discriminative FCs between ADHD and healthy control groups are given, which further verifies the validity of our classification method.

Assessment of Tree Detection Methods in Multispectral Aerial Images

Abstract: Detecting individual trees and quantifying their biomass is crucial for carbon accounting procedures at the stand, landscape, and national levels. A significant challenge for many organizations is the amount of effort necessary to document carbon storage levels, especially in terms of human labor. To advance towards the goal of efficiently assessing the carbon content of forest, we evaluate methods to detect trees from high-resolution images taken from unoccupied aerial systems (UAS). In the process, we introduce the Digital Elevated Vegetation Model (DEVM), a representation that combines multispectral images, digital surface models, and digital terrain models. We show that the DEVM facilitates the development of refined synthetic data to detect individual trees using deep learning-based approaches. We carried out experiments in two tree fields located in different countries. Simultaneously, we perform comparisons among an array of classical and deep learning-based methods highlighting the precision and reliability of the DEVM.

Temporal Variability-Based Functional Brain Lateralization Study in ADHD.

Abstract: Objective: The aim of this work is to explore the relationship between temporal variability and brain lateralization in ADHD. Method: The temporal variabilities of 116 brain regions based on resting-state functional magnetic resonance imaging (rs-fMRI) data were calculated for analysis. Results: Between-group comparison revealed that in comparison with the controls, ADHD participants showed significantly higher temporal variability in the left superior frontal gyrus (medial), left rectus gyrus, left inferior parietal lobule and angular gyrus, and lower temporal variability in the amygdala, left caudate and putamen. Besides, ADHD patients exhibited significantly increased leftward lateralization in the orbitofrontal cortex (inferior), and decreased rightward lateralization in the orbitofrontal cortex (medial) and rectus gyrus, compared with controls. Lateralization indices were also found to be related with clinical characteristics of ADHD patients. Conclusion: Our results may help us deeper in understanding the pathology of ADHD.