Extracting optimal tempo-spatial features using local discriminant bases and common spatial patterns for brain computer interfacing

Brain computer interfaces (BCI) provide a new approach to human computer communication, where the control is realised via performing mental tasks such as motor imagery (MI). In this study, we investigate a novel method to automatically segment electroencephalographic (EEG) data within a trial and extract features accordingly in order to improve the performance of MI data classification techniques. A new local discriminant bases (LDB) algorithm using common spatial patterns (CSP) projection as transform function is proposed for automatic trial segmentation. CSP is also used for feature extraction following trial segmentation. This new technique also allows to obtain a more accurate picture of the most relevant temporal–spatial points in the EEG during the MI. The results are compared with other standard temporal segmentation techniques such as sliding window and LDB based on the local cosine transform (LCT)

A study on temporal segmentation strategies for extracting common spatial patterns for brain computer interfacing

Brain computer interfaces (BCI) create a new approach to human computer communication, allowing the user to control a system simply by performing mental tasks such as motor imagery. This paper proposes and analyses different strategies for time segmentation in extracting common spatial patterns of the brain signals associated to these tasks leading to an improvement of BCI performance

Intelligent artificial ants based feature extraction from wavelet packet coefficients for biomedical signal classification

In this paper, a new feature extraction method utilizing ant colony optimization in the selection of wavelet packet transform (WPT) best basis is presented and adopted in classifying biomedical signals. The new algorithm, termed Intelligent Artificial Ants (IAA), searches the wavelet packet tree for subsets of features that best interact together to produce high classification accuracies. While traversing the WPT tree, the IAA takes into account existing correlation between features thus avoiding information redundancy. The IAA method is a mixture of filter and wrapper approaches in feature subset selection. The pheromone that the ants lay down is updated by means of an estimation of the information contents of a single feature or feature subset. The significance of the subsets selected by the ants is measured using linear discriminant analysis (LDA) classifier. The IAA method is tested on one of the most important biosignal driven applications, which is the Brain Computer Interface (BCI) problem with 56 EEG channels. Practical results indicate the significance of the proposed method achieving a maximum accuracy of 83%. ©2008 IEEE

Detecció automàtica i robusta de Bursts en EEG de nounats amb HIE. Enfocament tensorial

[ANGLÈS] Hypoxic-Ischemic Encephalopathy (HIE) is an important cause of brain injury in the newborn, and can result in long-term devastating consequences. Burst-suppression pattern is one of several indicators of severe pathology in the EEG signal that may occur after brain damage caused by e.g. asphyxia around the time of birth. The goal of this thesis is to design a robust method to detect burst patterns automatically regardless of the physiologic and extra-physiologic artifacts that may occur at any time. At first, a pre-detector has been designed to obtain potential burst candidates from different patients. Then, a post-classification has been implemented, applying high dimensional feature extraction methods, to get the real burst patterns from these patients with a high sensitivity.[CASTELLÀ] La Hipoxia-Isquemia Encefálica (HIE) es una causa importante de lesión cerebral en los recién nacidos, pudiendo acarrear devastadoras consecuencias a largo plazo. El patrón Burst-Suppression es uno de los indicadores dados en patologías severas en señales EEG los cuales ocurren después de una lesión cerebral causada, por ejemplo, por una asfixia poco después del nacimiento. El objetivo de esta tésis es diseñar un método robusto que detecte automáticamente patrones Burst, prescindiendo de los artefactos fisiológicos y extra-fisiológicos que puedan aparecer en cualquier momento. Primeramente, se ha diseñado un pre-detector para obtener los candidatos potenciales a Burst provenientes de diferentes pacientes. Seguidamente, se ha implementado una post-clasificación, aplicando métodos de extracción de características para altas dimensiones, para obtener patrones reales de Burst con una alta sensitividad.[CATALÀ] La Hipòxia-Isquèmia Encefàlica (HIE) és una causa important de lesió cerebral en nounats, que poden comportar devastadores conseqüències a llarg termini. El patró Burst-Suppression és un dels indicadors donats en patologies severes en els senyals EEG els quals ocorren després d'una lesió cerebral causada, per exemple, per una asfixia poc després del naixement. L'objectiu d'aquesta tesis és dissenyar un mètode robust que detecti automàticament patrons Burst, prescindint dels artefactes fisiològics i extra-fisiològics que poden aparèixer en qualsevol moment. Primerament, s'ha dissenyat un pre-detector per obtenir els candidats potencials a Burst provinents de diferents pacients. Seguidament, s'ha implementat una post-classificació, aplicant mètodes d'extracció de característiques per a altes dimensions, per tal d'obtenir patrons reals de Burst amb una alta sensitivitat

Application of biosignal-driven intelligent systems for multifunction prosthesis control

University of Technology, Sydney. Faculty of Engineering and Information Technology.Prosthetic devices aim to provide an artificial alternative to missing limbs. The controller for such devices is usually driven by the biosignals generated by the human body, particularly Electromyogram (EMG) or Electroencephalogram (EEG) signals. Such a controller utilizes a pattern recognition approach to classify the EMG signal recorded from the human muscles or the EEG signal from the brain. The aim of this thesis is to improve the EMG and EEG pattern classification accuracy. Due to the fact that the success of pattern recognition based biosignal driven systems highly depends on the quality of extracted features, a number of novel, robust, hybrid and innovative methods are proposed to achieve better performance. These methods are developed to effectively tackle many of the limitations of existing systems, in particular feature representation and dimensionality reduction. A set of knowledge extraction methods that can accurately and rapidly identify the most important attributes for classifying the arm movements are formulated. This is accomplished through the following: 1. Developing a new feature extraction technique that can identify the most important features from the high-dimensional time-frequency representation of the multichannel EMG and EEG signals. For this task, an information content estimation method using fuzzy entropies and fuzzy mutual information is proposed to identify the optimal wravelet packet transform decomposition for classification. 2. Developing a powerful variable (feature or channel) selection paradigm to improve the performance of multi-channel EMG and EEG driven systems. This will eventually lead to the development of a combined channel and feature selection technique as one possible scheme for dimensionality reduction. Two novel feature selection methods are developed under this scheme utilizing the ant colony arid differential evolution optimization techniques. The differential evolution optimization technique is further modified in a novel attempt in employing a float optimizer for the combinatorial task of feature selection, proving powerful performance by both methods. 3. Developing two feature projection techniques that extract a small subset of highly informative discriminant features, thus acting as an alternative scheme for dimensionality reduction. The two methods represent novel variations to fuzzy discriminant analysis based projection techniques. In addition, an extension to the non-linear discriminant analysis is proposed based on a mixture of differential evolution and fuzzy discriminant analysis. The testing and verification process of the proposed methods on different EMG and EEG datasets provides very encouraging results

Implementing physiologically-based approaches to improve Brain-Computer Interfaces usability in post-stroke motor rehabilitation

Stroke is one of the leading causes of long-term motor disability and, as such, directly impacts on daily living activities. Identifying new strategies to recover motor function is a central goal of clinical research. In the last years the approach to the post-stroke function restore has moved from the physical rehabilitation to the evidence-based neurological rehabilitation. Brain-Computer Interface (BCI) technology offers the possibility to detect, monitor and eventually modulate brain activity. The potential of guiding altered brain activity back to a physiological condition through BCI and the assumption that this recovery of brain activity leads to the restoration of behaviour is the key element for the use of BCI systems for therapeutic purposes. To bridge the gap between research-oriented methodology in BCI design and the usability of a system in the clinical realm requires efforts towards BCI signal processing procedures that would optimize the balance between system accuracy and usability. The thesis focused on this issue and aimed to propose new algorithms and signal processing procedures that, by combining physiological and engineering approaches, would provide the basis for designing more usable BCI systems to support post-stroke motor recovery. Results showed that introduce new physiologically-driven approaches to the pre-processing of BCI data, methods to support professional end-users in the BCI control parameter selection according to evidence-based rehabilitation principles and algorithms for the parameter adaptation in time make the BCI technology more affordable, more efficient, and more usable and, therefore, transferable to the clinical realm

School-age effects of the newborn individualized developmental care and assessment program for preterm infants with intrauterine growth restriction: preliminary findings

Background: The experience in the newborn intensive care nursery results in premature infants’ neurobehavioral and neurophysiological dysfunction and poorer brain structure. Preterms with severe intrauterine growth restriction are doubly jeopardized given their compromised brains. The Newborn Individualized Developmental Care and Assessment Program improved outcome at early school-age for preterms with appropriate intrauterine growth. It also showed effectiveness to nine months for preterms with intrauterine growth restriction. The current study tested effectiveness into school-age for preterms with intrauterine growth restriction regarding executive function (EF), electrophysiology (EEG) and neurostructure (MRI). Methods: Twenty-three 9-year-old former growth-restricted preterms, randomized at birth to standard care (14 controls) or to the Newborn Individualized Developmental Care and Assessment Program (9 experimentals) were assessed with standardized measures of cognition, achievement, executive function, electroencephalography, and magnetic resonance imaging. The participating children were comparable to those lost to follow-up, and the controls to the experimentals, in terms of newborn background health and demographics. All outcome measures were corrected for mother’s intelligence. Analysis techniques included two-group analysis of variance and stepwise discriminate analysis for the outcome measures, Wilks’ lambda and jackknifed classification to ascertain two-group classification success per and across domains; canonical correlation analysis to explore relationships among neuropsychological, electrophysiological and neurostructural domains at school-age, and from the newborn period to school-age. Results: Controls and experimentals were comparable in age at testing, anthropometric and health parameters, and in cognitive and achievement scores. Experimentals scored better in executive function, spectral coherence, and cerebellar volumes. Furthermore, executive function, spectral coherence and brain structural measures discriminated controls from experimentals. Executive function correlated with coherence and brain structure measures, and with newborn-period neurobehavioral assessment. Conclusion: The intervention in the intensive care nursery improved executive function as well as spectral coherence between occipital and frontal as well as parietal regions. The experimentals’ cerebella were significantly larger than the controls’. These results, while preliminary, point to the possibility of long-term brain improvement even of intrauterine growth compromised preterms if individualized intervention begins with admission to the NICU and extends throughout transition home. Larger sample replications are required in order to confirm these results

Automated design of robust discriminant analysis classifier for foot pressure lesions using kinematic data

In the recent years, the use of motion tracking systems for acquisition of functional biomechanical gait data, has received increasing interest due to the richness and accuracy of the measured kinematic information. However, costs frequently restrict the number of subjects employed, and this makes the dimensionality of the collected data far higher than the available samples. This paper applies discriminant analysis algorithms to the classification of patients with different types of foot lesions, in order to establish an association between foot motion and lesion formation. With primary attention to small sample size situations, we compare different types of Bayesian classifiers and evaluate their performance with various dimensionality reduction techniques for feature extraction, as well as search methods for selection of raw kinematic variables. Finally, we propose a novel integrated method which fine-tunes the classifier parameters and selects the most relevant kinematic variables simultaneously. Performance comparisons are using robust resampling techniques such as Bootstrap$632+$and k-fold cross-validation. Results from experimentations with lesion subjects suffering from pathological plantar hyperkeratosis, show that the proposed method can lead to$sim 96$correct classification rates with less than 10% of the original features

Wheelchair control using EEG signal classification

Tato diplomová práce představuje koncept elektrického invalidního vozíku ovládaného lidskou myslí. Tento koncept je určen pro osoby, které elektrický invalidní vozík nemohou ovládat klasickými způsoby, jakým je například joystick. V práci jsou popsány čtyři hlavní komponenty konceptu: elektroencefalograf, brain-computer interface (rozhraní mozek-počítač), systém sdílené kontroly a samotný elektrický invalidní vozík. V textu je představena použitá metodologie a výsledky provedených experimentů. V závěru jsou nastíněna doporučení pro budoucí vývoj.This diploma thesis presents the concept of mind-controlled electric wheelchair designed for people who are not able to use other interfaces such as hand joystick. Four main components of concept are described: electroencephalography, brain-computer interface, shared control and the electric wheelchair. In the text used methodology is described and results of conducted experiments are presented. In conclusion suggestions for future development are outlined.

Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates

This is the author accepted manuscript. The final version is available from IOP Publishing via the DOI in this record.OBJECTIVE: The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. APPROACH: Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. MAIN RESULTS: The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. SIGNIFICANCE: The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.The work presented in this paper was supported by FP7 EU funded MICHELANGELO project, Grant Agreement #288241. URL: www.michelangelo-project.eu/