Jaime Fernando Delgado Saa

• PhD
• PostDoc Position at University of Geneva

Brain–computer interfaces (BCIs) facilitate communication between the brain and external devices, providing an alternative solution for individuals with upper limb disabilities. The decoding of brain movement commands in BCIs relies on signal feature extraction and classification. Herein, the BNCI Horizon 2020 dataset is employed, which consists of...

Communication difficulties are one of the core criteria in diagnosing ASD, and are often characterized by speech reception difficulties, whose biological underpinnings are not yet identified. This deficit could denote atypical neuronal ensemble activity, as reflected by neural oscillations. Atypical cross-frequency oscillation coupling, in particul...

The purpose of this study is to analyze the contribution of the interactions between electrodes, measured either as correlation or as Jaccard distance, to the classification of two actions in a motor imagery paradigm, namely, left-hand movement and right-hand movement. The analysis is performed in two classifier models, namely, a static (linear dis...

Reconstructing intended speech from neural activity using brain-computer interfaces holds great promises for people with severe speech production deficits. While decoding overt speech has progressed, decoding imagined speech has met limited success, mainly because the associated neural signals are weak and variable compared to overt speech, hence d...

Finger flexion decoding and classification has gained attention to understand the relationship between finger movements and the brain activity. Initially focused on EEG signals, it was moved quickly to electrocorticography (ECoG) signals because of the advantages provided by the latter signals. The present paper proposes two based-CRF discriminativ...

Communication difficulties in autism spectrum disorder (ASD) involve a speech reception deficit, whose biological causes are not yet identified. This deficit could denote atypical neuronal ensemble activity, as reflected by neural oscillations. Atypical cross-frequency oscillation coupling in particular could disrupt the possibility to jointly trac...

Reconstructing intended speech from neural activity using brain-computer interfaces (BCIs) holds great promises for people with severe speech production deficits. While decoding overt speech has progressed, decoding imagined speech have met limited success, mainly because the associated neural signals are weak and variable hence difficult to decode...

Carry-over effects on brain states have been reported following emotional and cogni-tive events, persisting even during subsequent rest. Here, we investigated such effects by identifying recurring co-activation patterns (CAPs) in neural networks at rest with functional magnetic resonance imaging (fMRI). We compared carry-over effects on brain-wide...

In face-to-face communication, audio-visual (AV) stimuli can be fused, combined or perceived as mismatching. While the left superior temporal sulcus (STS) is presumably the locus of AV integration, the process leading to combination is unknown. Based on previous modelling work, we hypothesize that combination results from a complex dynamic originat...

The traditional approach in neuroscience relies on encoding models where brain responses are related to different stimuli in order to establish dependencies. In decoding tasks, on the contrary, brain responses are used to predict the stimuli, and traditionally, the signals are assumed stationary within trials, which is rarely the case for natural s...

In this paper, we present a novel approach to training classifiers in a speller based on P300 potentials. The method, based on bootstrapping, is a known strategy for generating new samples, but it is rarely used in neurosciences. The study first demonstrates how the performance of the classification task (detecting P300 and Non-P300 classes) could...

In face-to-face communication, audio-visual (AV) stimuli can be fused, combined or perceived as mismatching. While the left superior temporal sulcus (LSTS) is admittedly the locus of AV integration, the process leading to combination is unknown. Analysing behaviour and time-/source-resolved human MEG data, we show that fusion and combination both i...

Neuroimaging studies have shown carry-over effects on brain activity and connectivity following both emotional and cognitive events, persisting even during subsequent rest. Here, we investigate the functional dynamics of such effects by identifying recurring co-activation patterns (CAPs). Using the precuneus as seed region, we compare carrying-over...

The traditional approach in neuroscience relies on encoding models where brain responses to different stimuli are related to the latter to establish reproducible dependencies. To reduce neuronal and experimental noise, brain signals are usually averaged across trials to detect reliable and coherent brain activity. However, neural representations of...

In this work, a Brain Computer interface able to decode imagery motor task from EEG is presented. The method uses time-frequency representation of the brain signal recorded in different regions of the brain to extract important features. Principal Component Analysis and Sequential Forward Selection methods are compared in their ability to represent...

A brain-computer interface (BCI) is a system that aims for establishing a non-muscular communication path for subjects who had suffer from a neurodegenerative disease. Many BCI systems make use of the phenomena of event-related synchronization and de-synchronization of brain waves as a main feature for classification of different cognitive tasks. H...

A novel method based on coherent average of stimulus response and spectral decomposition by overaggressive models is proposed for the detection of SSVEP, decreasing the amount of stimulation time required for perfect classification in a two-class BCI system.

A P300-speller BCI system makes the assumption that the actual P300 signal is buried in noise, therefore the average over many repetitions of the experiment is used to increase the signal to noise ratio. Although different classification methods are used to learn the distribution of the data containing P300 and non-P300 potentials, traditionally th...

In this work, we propose a deep-learning-based algorithm for classification of visual stimuli from Electrocorticographic (ECoG) signals. In the experiment performed, a random sequence of grayscale images from houses and faces are presented to 7 subjects, and the electrical potentials from their ventral temporal cortical surface are recorded. The pr...

Objective. In this work we propose the use of conditional random fields with long-range dependencies for the classification of finger movements from electrocorticographic recordings. Approach. The proposed method uses long-range dependencies taking into consideration time-lags between the brain activity and the execution of the motor task. In addit...

Objective. In this work we propose a probabilistic graphical model framework that uses language priors at the level of words as a mechanism to increase the performance of P300-based spellers. Approach. This paper is concerned with brain-computer interfaces based on P300 spellers. Motivated by P300 spelling scenarios involving communication based on...

Motivated by P300 spelling scenarios involving communication based on a limited vo-cabulary, we propose a probabilistic graphical model-based framework and an associated classification algorithm that uses learned statistical prior models of language at the level of words. Exploiting such high-level contextual information helps reduce the error rate...

In this work, two methods based on statistical models that take into account the temporal changes in the Electroencephalographic (EEG) signal are proposed for asynchronous brain computer interfaces (BCI) based on imaginary motor tasks. Unlike the current approaches to asynchronous BCI systems that make use of windowed versions of the EEG data combi...

The development of devices to detect explosive substances in situ with characteristics such as easy portability, simple operation, and quick response is of high interest nowadays. Raman spectroscopy meets most of these requirements, allowing for the identification of volume and trace amounts of an unknown substance. In this paper we report the char...

We consider the problem of classification of imaginary motor tasks from electroencephalography (EEG) data for brain–computer interfaces (BCIs) and propose a new approach based on hidden conditional random fields (HCRFs). HCRFs are discriminative graphical models that are attractive for this problem because they (1) exploit the temporal structure of...

Este trabajo describe algoritmos para clasificación de señales EEG con aplicaciones a interfaces cerebro computadora. La extracción de características se realiza mediante estimación de la densidad espectral de potencia de la señal EEG empleando métodos paramétricos y no-paramétricos. La selección de las características más importantes de la señal...

Offline analysis pipelines have been developed and evaluated for the detection of covert attention from electroen-cephalography recordings, and the detection of overt attention in terms of eye movement based on electrooculographic measurements. Some additional analysis were done in order to prepare the pipelines for use in a real-time system. This...

Brain Computer interfaces are systems that allow the control of external devices using the information extracted from the brain signals. Such systems find applications in rehabil-itation, as an alternative communication channel and in multime-dia applications for entertainment and gaming. In this work, a new approach based on the Time-Frequency (TF...

Brain-computer interfaces (BCIs) are systems that allow the control of external devices using information extracted from brain signals. Such systems find application in rehabilitation of patients with limited or no muscular control. One mechanism used in BCIs is the imagination of motor activity, which produces variations on the power of the electr...

Este documento describe el diseño y construcción de un electroencefalógrafo de 32 canales construido para ser utilizado en el diseño de interfaces cerebro máquina en la universidad del norte. Se realiza una introducción al tema de las señales Electroencefalográficas describiendo sus características, lo que da lugar a la determinación de los parámet...

Biological signal processing offers an alternative to improve life quality in handicapped patients. In this sense is possible, to control devices as wheel chairs or computer systems. The signals that are usually used are EMG, EOG and EEG. When the lost of ability is severe the use of EMG signals is not possible because the person had lost, as in th...

Resumen Este artículo muestra el diseño del primer prototipo desarrollado en la universidad del norte que posibilita la aplicación de la técnica conocida como Estimulación Eléctrica Funcional, esto, con el fin de permitir la apertura y cierre de la mano en personas incapacitadas. Estas lesiones son debidas principalmente a accidentes cerebro-vascul...