J
Jaime Fernando Delgado Saa
Researcher at Universidad del Norte, Colombia
Publications - 16
Citations - 246
Jaime Fernando Delgado Saa is an academic researcher from Universidad del Norte, Colombia. The author has contributed to research in topics: Graphical model & Conditional random field. The author has an hindex of 7, co-authored 14 publications receiving 175 citations. Previous affiliations of Jaime Fernando Delgado Saa include Sabancı University & University of Geneva.
Papers
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Journal ArticleDOI
A latent discriminative model-based approach for classification of imaginary motor tasks from EEG data.
TL;DR: This work considers the problem of classification of imaginary motor tasks from electroencephalography (EEG) data for brain-computer interfaces (BCIs) and proposes a new approach based on hidden conditional random fields (HCRFs), which yields better classification accuracy.
Journal ArticleDOI
Imagined speech can be decoded from low- and cross-frequency intracranial EEG features
Timothée Proix,Jaime Fernando Delgado Saa,Andy Christen,Stephanie Martin,Brian N. Pasley,Robert T. Knight,Xing Tian,David Poeppel,Werner Doyle,Orrin Devinsky,Luc H. Arnal,Pierre Mégevand,Anne-Lise Giraud +12 more
TL;DR: In this article , the authors extracted consistent and specific neural features usable for future brain computer interfaces, and assessed their performance to discriminate speech items in articulatory, phonetic, and vocalic representation spaces.
EEG Signal Classification Using Power Spectral Features and linear Discriminant Analysis: A Brain Computer Interface Application
TL;DR: The design of a set of algorithms capable to classify brain signals related to imaginary motor activities ( left and right hand imaginary) is shown and it is shown that the use of parametrical methods for Spectral Power Density estimation can improve the accuracy of the Brain Computer Interface.
Journal ArticleDOI
Discriminative Methods for Classification of Asynchronous Imaginary Motor Tasks From EEG Data
TL;DR: This work describes how the asynchronous BCI problem can be posed as a classification problem based on CRFs or LDCRFs, by defining appropriate random variables and their relationships, and describes the improvements provided by the proposed methods in terms of classification accuracy.
Proceedings ArticleDOI
Hidden conditional random fields for classification of imaginary motor tasks from EEG data
TL;DR: This paper proposes a new approach for classification of imaginary motor tasks based on hidden conditional random fields (HCRFs), a discriminative graphical models that are attractive for this problem because they involve learned statistical models matched to the classification problem.