DSpace Repository :: Browsing by Author "Villar, Ana Julia"

Browsing by Author "Villar, Ana Julia"

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Comparative study of robust methods for motor imagery classification based on CSP and LDA
(2017-10) Villar, Ana Julia
" Common spatial patterns analysis and linear discriminant analysis are popular algorithms for spatial filtering and classifying in motor imagery. These algorithms are very sensitive to noise and artifacts which affect the classification accuracy. To deal with this issue, it is proposed to replace the usual estimators of covariance and scale used in the algorithms for robust versions. The performance of the methods are evaluated and compared on EGG data from BCI competition data sets; results show that robust methods outperformed classical techniques for subjects with poor classification accuracy. "
EEG waveform analysis of P300 ERP with applications to brain computer interfaces
(2018-11) Ramele, Rodrigo; Villar, Ana Julia; Santos, Juan Miguel
"The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition."
EPOC Emotiv EEG Basics
(2022-10) Ramele, Rodrigo; Villar, Ana Julia; Santos, Juan Miguel
This document provides some basic guidance to start working with the EPOC Emotiv neuroheadset device and describes how to use it to perform basic Brain-Computer Interface (BCI) research. A brief tutorial on how to set up the device, from its electrophysiological point of view, as well as a description and practical code to perform some basic analysis, is explained. A basic experiment is introduced to detect one of the oldest and, indeed, quite still valuable electrophysiological correlate, visual occipital alpha waves, or Berger Rhythm. An additional experiment is expounded where the power spectrum of alpha waves is reduced when a subject is affected by background cognitive disturbances. This document also briefs about the extraction of information by using the EPOC Emotiv library and also with python Emokit package. This report presents a basic guide on how to use EEGLAB + MATLAB, as well as python stack to perform the neurophysiological analysis. Finally, a basic analysis on different feature extraction and classification methods is provided.
Histogram of gradient orientations of EEG signal plots for brain computer interfaces
(2018) Ramele, Rodrigo; Santos, Juan Miguel; Villar, Ana Julia
"Brain Computer Interface (BCI) or Brain Machine Interfaces (BMI), has proved the feasibility of a distinct non-biological communication channel to transmit information from the Central Nervous System (CNS) to a computer device. Promising success has been achieved with invasive BCI, though biocompatibilities issues and the complexity and risks of surgical procedures are the main drive to enhance current non-invasive technologies. Electroencephalography (EEG) is the most widespread method to gather information from the CNS in a non-invasive way. Clinical EEG has traditionally focused on temporal waveforms, but signal analysis methods which follow this path have been neglected in BCI research. This Thesis proposes a method and framework to analyze the waveform, the shape of the EEG signal, using the histogram of gradient orientations, a fruitful technique from Computer Vision which is used to characterize image local features. Inspiration comes from what traditionally electroencephalographers have been doing for almost a century: visually inspecting raw EEG signal plots."
Histogram of gradient orientations of signal plots applied to P300 detection
(2019-07) Ramele, Rodrigo; Villar, Ana Julia; Santos, Juan Miguel
"The analysis of Electroencephalographic (EEG) signals is of ulterior importance to aid in the diagnosis of mental disease and to increase our understanding of the brain. Traditionally, clinical EEG has been analyzed in terms of temporal waveforms, looking at rhythms in spontaneous activity, subjectively identifying troughs and peaks in Event-Related Potentials (ERP), or by studying graphoelements in pathological sleep stages. Additionally, the discipline of Brain Computer Interfaces (BCI) requires new methods to decode patterns from non-invasive EEG signals. This field is developing alternative communication pathways to transmit volitional information from the Central Nervous System. The technology could potentially enhance the quality of life of patients affected by neurodegenerative disorders and other mental illness. This work mimics what electroencephalographers have been doing clinically, visually inspecting, and categorizing phenomena within the EEG by the extraction of features from images of signal plots. These features are constructed based on the calculation of histograms of oriented gradients from pixels around the signal plot. It aims to provide a new objective framework to analyze, characterize and classify EEG signal waveforms. The feasibility of the method is outlined by detecting the P300, an ERP elicited by the oddball paradigm of rare events, and implementing an offline P300-based BCI Speller. The validity of the proposal is shown by offline processing a public dataset of Amyotrophic Lateral Sclerosis (ALS) patients and an own dataset of healthy subjects."
Outliers resistant methods for motor imagery classification
(2018-08) Villar, Ana Julia
"Common spatial patterns analysis (CSP) and linear discriminant analysis (LDA) are widely used techniques for spatial filtering and classifying in motor imagery (MI). However, CSP is very sensitive to noise and artifacts. A method to detect and eliminate anomalous electroencephalogram (EGG) signals before applying CSP is presented. An outlier score of the signal is obtained by calculating the similarities with the other signals of the sample through the Bounded Coordinate System (BCS). Besides, it is proposed to replace the usual estimators of mean, covariance and scale, used in the algorithms, by Olive and Hawkins estimators to get robust versions of BCS and CSP. The assumption done in LDA that the covariance of each of the classes in MI are identical may not be true; if it is not satisfied, it is better to use quadratic discrimination. Tests to verify this hypothesis and decide which discriminant function must be used are considered. The performances of the methods are evaluated and compared on EGG data from BCI competition datasets; results show that robust methods outperformed classical techniques, especially for subjects with poor classification accuracy."

Browsing by Author "Villar, Ana Julia"

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