Epileptic seizure detection from EEG signals using logistic model trees

Reliable analysis of electroencephalogram (EEG) signals is crucial that could lead the way to correct diagnostic and therapeutic methods for the treatment of patients with neurological abnormalities, especially epilepsy. This paper presents a novel analysis system for detecting epileptic seizure from EEG signals, which uses statistical features based on optimum allocation technique (OAT) with logistic model trees (LMT). The analysis involves applying the OAT to select representative EEG signals that reflect the entire database. Then, some statistical features are extracted from these EEG signals and the obtained feature set is fed into the LMT classification model to detect epileptic seizure. To test the consistency of the proposed method, all experiments are carried out on a benchmark EEG dataset and repeated twenty times with the same parameters in the detection process, and the average values of the performance parameters are reported. The results show very high detection performances for each class, and also confirm the consistency of the proposed method in the repeating process. The proposed method outperforms some state-of-the-art methods of epileptic EEG signal detection using the same EEG dataset

A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery

Common spatial pattern (CSP) is a popular feature extraction method for electroencephalogram (EEG) motor imagery (MI). This study modifies the conventional CSP algorithm to improve the multi-class MI classification accuracy and ensure the computation process is efficient. The EEG MI data is gathered from the Brain-Computer Interface (BCI) Competition IV. At first, a bandpass filter and a time-frequency analysis are performed for each experiment trial. Then, the optimal EEG signals for every experiment trials are selected based on the signal energy for CSP feature extraction. In the end, the extracted features are classified by three classifiers, linear discriminant analysis (LDA), na\"ive Bayes (NVB), and support vector machine (SVM), in parallel for classification accuracy comparison. The experiment results show the proposed algorithm average computation time is 37.22% less than the FBCSP (1st winner in the BCI Competition IV) and 4.98% longer than the conventional CSP method. For the classification rate, the proposed algorithm kappa value achieved 2nd highest compared with the top 3 winners in BCI Competition IV.Comment: Accepted by 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society, 202

Epileptic seizure detection from EEG signals using logistic model trees

Reliable analysis of electroencephalogram (EEG) signals is crucial that could lead the way to correct diagnostic and therapeutic methods for the treatment of patients with neurological abnormalities, especially epilepsy. This paper presents a novel analysis system for detecting epileptic seizure from EEG signals, which uses statistical features based on optimum allocation technique (OAT) with logistic model trees (LMT). The analysis involves applying the OAT to select representative EEG signals that reflect the entire database. Then, some statistical features are extracted from these EEG signals and the obtained feature set is fed into the LMT classification model to detect epileptic seizure. To test the consistency of the proposed method, all experiments are carried out on a benchmark EEG dataset and repeated twenty times with the same parameters in the detection process, and the average values of the performance parameters are reported. The results show very high detection performances for each class, and also confirm the consistency of the proposed method in the repeating process. The proposed method outperforms some state-of-the-art methods of epileptic EEG signal detection using the same EEG dataset

BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients

Purpose: Brain–computer interface (BCI)-controlled assistive robotic systems have been developed with increasing success with the aim to rehabilitation of patients after brain injury to increase independence and quality of life. While such systems may use surgically implanted invasive sensors, non-invasive alternatives can be better suited due to the ease of use, reduced cost, improvements in accuracy and reliability with the advancement of the technology and practicality of use. The consumer-grade BCI devices are often capable of integrating multiple types of signals, including Electroencephalogram (EEG) and Electromyogram (EMG) signals. Materials and Methods: This paper summarizes the development of a portable and cost-efficient BCI-controlled assistive technology using a non-invasive BCI headset “OpenBCI” and an open source robotic arm, U-Arm, to accomplish tasks related to rehabilitation, such as access to resources, adaptability or home use. The resulting system used a combination of EEG and EMG sensor readings to control the arm. To avoid risks of injury while the device is being used in clinical settings, appropriate measures were incorporated into the software control of the arm. A short survey was used following the system usability scale (SUS), to measure the usability of the technology to be trialed in clinical settings. Results: From the experimental results, it was found that EMG is a very reliable method for assistive technology control, provided that the user specific EMG calibration is done. With the EEG, even though the results were promising, due to insufficient detection of the signal, the controller was not adequate to be used within a neurorehabilitation environment. The survey indicated that the usability of the system is not a barrier for moving the system into clinical trials. Implication on rehabilitation For the rehabilitation of patients suffering from neurological disabilities (particularly those suffering from varying degrees of paralysis), it is necessary to develop technology that bypasses the limitations of their condition. For example, if a patient is unable to walk due to the unresponsiveness in their motor neurons, technology can be developed that used an alternate input to move an exoskeleton, which enables the patient to walk again with the assistance of the exoskeleton. This research focuses on neuro-rehabilitation within the framework of the NHS at the Kent and Canterbury Hospital in UK. The hospital currently does not have any system in place for self-driven rehabilitation and instead relies on traditional rehabilitation methods through assistance from physicians and exercise regimens to maintain muscle movement. This paper summarises the development of a portable and cost-efficient BCI controlled assistive technology using a non-invasive BCI headset “OpenBCI” and an open source robotic arm, U-Arm, to accomplish tasks related to rehabilitation, such as access to resources, adaptability or home use. The resulting system used a combination of EEG and EMG sensor readings to control the arm, which could perform a number of different tasks such as picking/placing objects or assist users in eating

Diverse Feature Blend Based on Filter-Bank Common Spatial Pattern and Brain Functional Connectivity for Multiple Motor Imagery Detection

Motor imagery (MI) based brain-computer interface (BCI) is a research hotspot and has attracted lots of attention. Within this research topic, multiple MI classification is a challenge due to the difficulties caused by time-varying spatial features across different individuals. To deal with this challenge, we tried to fuse brain functional connectivity (BFC) and one-versus-the-rest filter-bank common spatial pattern (OVR-FBCSP) to improve the robustness of classification. The BFC features were extracted by phase locking value (PLV), representing the brain inter-regional interactions relevant to the MI, whilst the OVR-FBCSP is used to extract the spatial-frequency features related to the MI. These diverse features were then fed into a multi-kernel relevance vector machine (MK-RVM). The dataset with three motor imagery tasks (left hand MI, right hand MI, and feet MI) was used to assess the proposed method. Experimental results not only showed that the cascade structure of diverse feature fusion and MK-RVM achieved satisfactory classification performance (average accuracy: 83.81%, average kappa: 0.76), but also demonstrated that BFC plays a supplementary role in the MI classification. Moreover, the proposed method has a potential to be integrated into multiple MI online detection owing to the advantage of strong time-efficiency of RVM

EEG Signal Processing in Motor Imagery Brain Computer Interfaces with Improved Covariance Estimators

Desde hace unos años hasta la actualidad, el desarrollo en el campo de los interfaces cerebro ordenador ha ido aumentando. Este aumento viene motivado por una serie de factores distintos. A medida que aumenta el conocimiento acerca del cerebro humano y como funciona (del que aún se conoce relativamente poco), van surgiendo nuevos avances en los sistemas BCI que, a su vez, sirven de motivación para que se investigue más acerca de este órgano. Además, los sistemas BCI abren una puerta para que cualquier persona pueda interactuar con su entorno independientemente de la discapacidad física que pueda tener, simplemente haciendo uso de sus pensamientos. Recientemente, la industria tecnológica ha comenzado a mostrar su interés por estos sistemas, motivados tanto por los avances con respecto a lo que conocemos del cerebro y como funciona, como por el uso constante que hacemos de la tecnología en la actuali- dad, ya sea a través de nuestros smartphones, tablets u ordenadores, entre otros muchos dispositivos. Esto motiva que compañías como Facebook inviertan en el desarrollo de sistemas BCI para que tanto personas sin discapacidad como aquellas que, si las tienen, puedan comunicarse con los móviles usando solo el cerebro. El trabajo desarrollado en esta tesis se centra en los sistemas BCI basados en movimien- tos imaginarios. Esto significa que el usuario piensa en movimientos motores que son interpretados por un ordenador como comandos. Las señales cerebrales necesarias para traducir posteriormente a comandos se obtienen mediante un equipo de EEG que se coloca sobre el cuero cabelludo y que mide la actividad electromagnética producida por el cere- bro. Trabajar con estas señales resulta complejo ya que son no estacionarias y, además, suelen estar muy contaminadas por ruido o artefactos. Hemos abordado esta temática desde el punto de vista del procesado estadístico de la señal y mediante algoritmos de aprendizaje máquina. Para ello se ha descompuesto el sistema BCI en tres bloques: preprocesado de la señal, extracción de características y clasificación. Tras revisar el estado del arte de estos bloques, se ha resumido y adjun- tado un conjunto de publicaciones que hemos realizado durante los últimos años, y en las cuales podemos encontrar las diferentes aportaciones que, desde nuestro punto de vista, mejoran cada uno de los bloques anteriormente mencionados. De manera muy resumida, para el bloque de preprocesado proponemos un método mediante el cual conseguimos nor- malizar las fuentes de las señales de EEG. Al igualar las fuentes efectivas conseguimos mejorar la estima de las matrices de covarianza. Con respecto al bloque de extracción de características, hemos conseguido extender el algoritmo CSP a casos no supervisados. Por último, en el bloque de clasificación también hemos conseguido realizar una sepa- ración de clases de manera no supervisada y, por otro lado, hemos observado una mejora cuando se regulariza el algoritmo LDA mediante un método específico para Gaussianas.The research and development in the field of Brain Computer Interfaces (BCI) has been growing during the last years, motivated by several factors. As the knowledge about how the human brain is and works (of which we still know very little) grows, new advances in BCI systems are emerging that, in turn, serve as motivation to do more re- search about this organ. In addition, BCI systems open a door for anyone to interact with their environment regardless of the physical disabilities they may have, by simply using their thoughts. Recently, the technology industry has begun to show its interest in these systems, mo- tivated both by the advances about what we know of the brain and how it works, and by the constant use we make of technology nowadays, whether it is by using our smart- phones, tablets or computers, among many other devices. This motivates companies like Facebook to invest in the development of BCI systems so that people (with or without disabilities) can communicate with their devices using only their brain. The work developed in this thesis focuses on BCI systems based on motor imagery movements. This means that the user thinks of certain motor movements that are in- terpreted by a computer as commands. The brain signals that we need to translate to commands are obtained by an EEG device that is placed on the scalp and measures the electromagnetic activity produced by the brain. Working with these signals is complex since they are non-stationary and, in addition, they are usually heavily contaminated by noise or artifacts. We have approached this subject from the point of view of statistical signal processing and through machine learning algorithms. For this, the BCI system has been split into three blocks: preprocessing, feature extraction and classification. After reviewing the state of the art of these blocks, a set of publications that we have made in recent years has been summarized and attached. In these publications we can find the different contribu- tions that, from our point of view, improve each one of the blocks previously mentioned. As a brief summary, for the preprocessing block we propose a method that lets us nor- malize the sources of the EEG signals. By equalizing the effective sources, we are able to improve the estimation of the covariance matrices. For the feature extraction block, we have managed to extend the CSP algorithm for unsupervised cases. Finally, in the classification block we have also managed to perform a separation of classes in an blind way and we have also observed an improvement when the LDA algorithm is regularized by a specific method for Gaussian distributions

CLASSIFIERS BASED ON A NEW APPROACH TO ESTIMATE THE FISHER SUBSPACE AND THEIR APPLICATIONS

In this thesis we propose a novel classifier, and its extensions, based on a novel estimation of the Fisher Subspace. The proposed classifiers have been developed to deal with high dimensional and highly unbalanced datasets whose cardinality is low. The efficacy of the proposed techniques has been proved by the results achieved on real and synthetic datasets, and by the comparison with state of the art predictors