Wavelet Transform Based Classification of Invasive Brain Computer Interface Data

The input signals of brain computer interfaces may be either electroencephalogram recorded from scalp or electrocorticogram recorded with subdural electrodes. It is very important that the classifiers have the ability for discriminating signals which are recorded in different sessions to make brain computer interfaces practical in use. This paper proposes a method for classifying motor imagery electrocorticogram signals recorded in different sessions. Extracted feature vectors based on wavelet transform were classified by using k-nearest neighbor, support vector machine and linear discriminant analysis algorithms. The proposed method was successfully applied to Data Set I of BCI competition 2005, and achieved a classification accuracy of 94 % on the test data. The performance of the proposed method was confirmed in terms of sensitivity, specificity and Kappa and compared with that of other studies used the same data set. This paper is an extended version of our work that won the Best Paper Award at the 33rd International Conference on Telecommunications and Signal Processing

Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface

© 2005-2012 IEEE. Brain-computer interface technologies, such as steady-state visually evoked potential, P300, and motor imagery are methods of communication between the human brain and the external devices. Motor imagery-based brain-computer interfaces are popular because they avoid unnecessary external stimuli. Although feature extraction methods have been illustrated in several machine intelligent systems in motor imagery-based brain-computer interface studies, the performance remains unsatisfactory. There is increasing interest in the use of the fuzzy integrals, the Choquet and Sugeno integrals, that are appropriate for use in applications in which fusion of data must consider possible data interactions. To enhance the classification accuracy of brain-computer interfaces, we adopted fuzzy integrals, after employing the classification method of traditional brain-computer interfaces, to consider possible links between the data. Subsequently, we proposed a novel classification framework called the multimodal fuzzy fusion-based brain-computer interface system. Ten volunteers performed a motor imagery-based brain-computer interface experiment, and we acquired electroencephalography signals simultaneously. The multimodal fuzzy fusion-based brain-computer interface system enhanced performance compared with traditional brain-computer interface systems. Furthermore, when using the motor imagery-relevant electroencephalography frequency alpha and beta bands for the input features, the system achieved the highest accuracy, up to 78.81% and 78.45% with the Choquet and Sugeno integrals, respectively. Herein, we present a novel concept for enhancing brain-computer interface systems that adopts fuzzy integrals, especially in the fusion for classifying brain-computer interface commands

EEG-Based Movement Imagery Classification Using Machine Learning Techniques and Welch’s Power Spectral Density Estimation

This project implements an EEG-based movement imagery classification using Welch’s Power Spectral Density estimation which could be used in Brain Computer Interface systems.  This classification which is based on the extracted features fro

Epileptic Seizures Prediction Using Machine Learning Methods

Epileptic seizures occur due to disorder in brain functionality which can affect patient’s health. Prediction of epileptic seizures before the beginning of the onset is quite useful for preventing the seizure by medication. Machine learning techniques and computational methods are used for predicting epileptic seizures from Electroencephalograms (EEG) signals. However, preprocessing of EEG signals for noise removal and features extraction are two major issues that have an adverse effect on both anticipation time and true positive prediction rate. Therefore, we propose a model that provides reliable methods of both preprocessing and feature extraction. Our model predicts epileptic seizures’ sufficient time before the onset of seizure starts and provides a better true positive rate. We have applied empirical mode decomposition (EMD) for preprocessing and have extracted time and frequency domain features for training a prediction model. The proposed model detects the start of the preictal state, which is the state that starts few minutes before the onset of the seizure, with a higher true positive rate compared to traditional methods, 92.23%, and maximum anticipation time of 33 minutes and average prediction time of 23.6 minutes on scalp EEG CHB-MIT dataset of 22 subjects

Adaptive nonlinear multivariate brain connectivity analysis of motor imagery movements using graph theory

Recent studies on motor imagery (MI)-based brain computer interaction (BCI) reported that the interaction of spatially separated brain areas in forms of functional or effective connectivity leads to a better insight of brain neural patterns during MI movements and can provide useful features for BCIs. However, existing studies suffer from unrealistic assumptions or technical weaknesses for processing brain signals, such as stationarity, linearity and bivariate analysis framework. Besides, volume conduction effect as a critical challenge in this area and the role of subcortical regions in connectivity analysis have not been considered and studied well. In this thesis, the neurophysiological connectivity patterns of healthy human brain during different MI movements are deeply investigated. At first, an adaptive nonlinear multivariate statespace model known as dual extended Kalman filter is proposed for connectivity pattern estimation. Several frequency domain functional and effective connectivity estimators are developed for nonlinear non-stationary signals. Evaluation results show superior parameter tracking performance and hence more accurate connectivity analysis by the proposed model. Secondly, source-space time-varying nonlinear multivariate brain connectivity during feet, left hand, right hand and tongue MI movements is investigated in a broad frequency range by using the developed connectivity estimators. Results reveal the similarities and the differences between MI tasks in terms of involved regions, density of interactions, distribution of interactions, functional connections and information flows. Finally, organizational principles of brain networks of MI movements measured by all considered connectivity estimators are extensively explored by graph theoretical approach where the local and global graph structures are quantified by computing different graph indexes. Results report statistical significant differences between and within the MI tasks by using the graph indexes extracted from the networks formed particularly by normalized partial directed coherence. This delivers promising distinctive features of the MI tasks for non-invasive BCI applications

Probabilistic Graphical Models for ERP-Based Brain Computer Interfaces

An event related potential (ERP) is an electrical potential recorded from the nervous system of humans or other animals. An ERP is observed after the presentation of a stimulus. Some examples of the ERPs are P300, N400, among others. Although ERPs are used very often in neuroscience, its generation is not yet well understood and different theories have been proposed to explain the phenomena. ERPs could be generated due to changes in the alpha rhythm, an internal neural control that reset the ongoing oscillations in the brain, or separate and distinct additive neuronal phenomena. When different repetitions of the same stimuli are averaged, a coherence addition of the oscillations is obtained which explain the increase in amplitude in the signals. Two ERPs are mostly studied: N400 and P300. N400 signals arise when a subject tries to make semantic operations that support neural circuits for explicit memory. N400 potentials have been observed mostly in the rhinal cortex. P300 signals are related to attention and memory operations. When a new stimulus appears, a P300 ERP (named P3a) is generated in the frontal lobe. In contrast, when a subject perceives an expected stimulus, a P300 ERP (named P3b) is generated in the temporal – parietal areas. This implicates P3a and P3b are related, suggesting a circuit pathway between the frontal and temporal–parietal regions, whose existence has not been verified. Un potencial relacionado con un evento (ERP) es un potencial eléctrico registrado en el sistema nervioso de los seres humanos u otros animales. Un ERP se observa tras la presentación de un estímulo. Aunque los ERPs se utilizan muy a menudo en neurociencia, su generación aún no se entiende bien y se han propuesto diferentes teorías para explicar el fenómeno. Una interfaz cerebro-computador (BCI) es un sistema de comunicación en el que los mensajes o las órdenes que un sujeto envía al mundo exterior proceden de algunas señales cerebrales en lugar de los nervios y músculos periféricos. La BCI utiliza ritmos sensorimotores o señales ERP, por lo que se necesita un clasificador para distinguir entre los estímulos correctos y los incorrectos. En este trabajo, proponemos utilizar modelos probabilísticos gráficos para el modelado de la dinámica temporal y espacial de las señales cerebrales con aplicaciones a las BCIs. Los modelos gráficos han sido seleccionados por su flexibilidad y capacidad de incorporar información previa. Esta flexibilidad se ha utilizado anteriormente para modelar únicamente la dinámica temporal. Esperamos que el modelo refleje algunos aspectos del funcionamiento del cerebro relacionados con los ERPs, al incluir información espacial y temporal.DoctoradoDoctor en Ingeniería Eléctrica y Electrónic

Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review

Brain-Computer Interfaces (BCIs) are systems that establish a direct communication pathway between the users' brain activity and external effectors. They offer the potential to improve the quality of life of motor-impaired patients. Motor BCIs aim to permit severely motor-impaired users to regain limb mobility by controlling orthoses or prostheses. In particular, motor BCI systems benefit patients if the decoded actions reflect the users' intentions with an accuracy that enables them to efficiently interact with their environment. One of the main challenges of BCI systems is to adapt the BCI's signal translation blocks to the user to reach a high decoding accuracy. This paper will review the literature of data-driven and user-specific transducer design and identification approaches and it focuses on internally-paced motor BCIs. In particular, continuous kinematic biomimetic and mental-task decoders are reviewed. Furthermore, static and dynamic decoding approaches, linear and non-linear decoding, offline and real-time identification algorithms are considered. The current progress and challenges related to the design of clinical-compatible motor BCI transducers are additionally discussed

Desenvolvimento de uma interface cérebro computador baseada em ritmos sensório-motores para controle de dispositivos

O desenvolvimento de ferramentas voltadas para a área da tecnologia assistida tem crescido muito nos últimos anos devido ao avanço tecnológico e científico. Uma das áreas em destaque na Comunidade Científica nos últimos anos é a área denominada de Brain-Computer Interface ou simplesmente BCI, que basicamente utiliza sinais cerebrais para controlar ou gerenciar dispositivos. Neste trabalho é desenvolvido um sistema experimental BCI, síncrono e não invasivo, utilizando sinais cerebrais da região do córtex somatossensorial capturados com um EEG de 3 canais, com o objetivo de comandar um protótipo de cadeira de rodas motorizada sem a participação de nervos periféricos e músculos. São realizados 4 experimentos onde voluntários não treinados realizam tarefas motoras imaginárias de 2, 3 ou 4 movimentos, onde são avaliados diversos aspectos como, por exemplo, o método de seleção e extração de características, taxas de acerto na classificação, aplicação do método empregado em uma base de dados internacional conhecida para comparação de resultados, assim como a avaliação geral do sistema. Foram obtidas taxas de acerto médias de 74,9% para os 3 melhores voluntários do experimento com 2 movimentos, 60% para o experimento com 3 movimentos e 40,2% para o experimento de 4 movimentos. No experimento de interface com a cadeira de rodas foram obtidas taxas de acerto médias de 65,7 e 49,2% para 2 ou 3 direções, respectivamente. É importante ressaltar que essas taxas de acerto são similares às obtidas em outros trabalhos.The development of tools for assistive technology has grown tremendously in recent years due to technological and scientific advancement. One of the areas highlighted in the scientific community in recent years is called Brain-Computer Interface or simply BCI, which basically uses brain signals to control or manage devices. In this work is developed an experimental BCI system, synchronous and non-invasive, using brain signals from somatosensory cortex captured with a 3-channel EEG, in order to command a motorized wheelchair without the involvement of peripheral nerves and muscles. Four experiments are performed where untrained volunteers perform imaginary tasks (two, three or four imaginary movements), which are evaluated several aspects, such as the method of selection and feature extraction, classification accuracy rates, application of the method employed in an international database for comparison, as well as the general evaluation of the system. Were obtained hit rates (average) of 74.9% for the three best volunteers of the experiment with two movements, 60% for the experiment with three movements and 40.2% for the experiment with four movements. In the experiment with the wheelchair were obtained 65.7 and 49.2% hit rates (average) for 2 or 3 directions, respectively. It is noteworthy that these hit rates are compatible with other works

Looking through the crowded mask: investigating the effect of distractor number and position in object substitution masking

Object substitution masking (OSM) is a phenomenon wherein a surrounding mask (typically four dots) that onsets with a target but lingers after its offset significantly reduces target perceptibility. OSM was originally postulated to occur only when spatial attention was spread (Di Lollo et al., 2000). Specifically, it was claimed that OSM only occurred when the target was presented in the context of large set-size displays (Di Lollo et al., 2000). However, more recent research has raised questions over the relevance of set size in OSM. Two separate investigations (Argyropoulos et al., 2013; Filmer et al., 2014) found that strong masking by OSM could be produced even with a set size of one. It was argued that the “set size” effects in OSM were actually an artifact of constrained performance. That is, once performance was brought within a measurable range, OSM was reported to be independent of set size. Further research however has suggested that perhaps this rejection of the role of set size in OSM was premature. Pilling (2013) found that increased set size did in fact lead to greater OSM magnitude. Therefore it seems that an explanation of constrained performance cannot fully account for the experimental findings. This thesis begins by investigating the disparity between these results by further exploring the role of set size in OSM. The first chapter provides an overview of some of the constraints for perceptual awareness by examining experimental phenomena that prevent visual awareness. The experimental phenomena of visual masking and specifically OSM are focused on with particular focus given to the role of attention in OSM. Chapter 2 is the first experimental chapter. This chapter investigates the role of set size in OSM using five experiments. Chapter 3 explores if visual crowding can be used as an alternative explanation for the set size effects in OSM with five experiments. Chapter 4 attempts to investigate the neural underpinnings of OSM, and the interaction between OSM and crowding using an EEG method. This thesis proposes, based on its findings, that the nominal set size effect in OSM is actually an effect of crowding, a factor which tends to co-vary with set size in most studies. Further experiments in this thesis showed that the interaction between crowding and OSM was one in which OSM affected crowding rather than the converse process. That is, with the use of OSM, the window at which flankers crowd the target becomes extended. These findings show parallels with the previously reported phenomenon of “supercrowding” which has been reported with classical masking. Given this, these results challenge claims regarding the position of OSM and crowding in the object processing hierarchy (e.g. Breitmeyer, 2014). This thesis contributes to the ongoing investigation of OSM, provides implications for its existing theories and for accounts of object processing more generally as well as highlighting future directions for research in this field