Classification of multichannel EEG signal by linear discriminant analysis

Motor imagery (MI) related Electroencephalogram (EEG) signal classification is one of the main challenge in designing a brain computer interface (BCI) system. Linear Discriminant Analysis (LDA) has a very low computational requirement which makes it suitable for online BCI system. This paper proposes an advanced and simple classification technique for MI related BCI system. Initially the signal is extracted for different features. The LDA classifier has been used to propose technique to design an MI based BCI. For contrastive comparison other classification techniques have been evaluated by classification accuracy and Cohen's kappa

Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification

Objective. The main goal of this work is to develop a model for multi-sensor signals such as MEG or EEG signals, that accounts for the inter-trial variability, suitable for corresponding binary classification problems. An important constraint is that the model be simple enough to handle small size and unbalanced datasets, as often encountered in BCI type experiments. Approach. The method involves linear mixed effects statistical model, wavelet transform and spatial filtering, and aims at the characterization of localized discriminant features in multi-sensor signals. After discrete wavelet transform and spatial filtering, a projection onto the relevant wavelet and spatial channels subspaces is used for dimension reduction. The projected signals are then decomposed as the sum of a signal of interest (i.e. discriminant) and background noise, using a very simple Gaussian linear mixed model. Main results. Thanks to the simplicity of the model, the corresponding parameter estimation problem is simplified. Robust estimates of class-covariance matrices are obtained from small sample sizes and an effective Bayes plug-in classifier is derived. The approach is applied to the detection of error potentials in multichannel EEG data, in a very unbalanced situation (detection of rare events). Classification results prove the relevance of the proposed approach in such a context. Significance. The combination of linear mixed model, wavelet transform and spatial filtering for EEG classification is, to the best of our knowledge, an original approach, which is proven to be effective. This paper improves on earlier results on similar problems, and the three main ingredients all play an important role

EEG signal analysis via a cleaning procedure based on multivariate empirical mode decomposition

IJCCI 2012Artifacts are present in most of the electroencephalography (EEG) recordings, making it difficult to interpret or analyze the data. In this paper a cleaning procedure based on a multivariate extension of empirical mode decomposition is used to improve the quality of the data. This is achieved by applying the cleaning method to raw EEG data. Then, a synchrony measure is applied on the raw and the clean data in order to compare the improvement of the classification rate. Two classifiers are used, linear discriminant analysis and neural networks. For both cases, the classification rate is improved about 20%

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

Epileptic seizure detection and prediction based on EEG signal

Epilepsy is a kind of chronic brain disfunction, manifesting as recurrent seizures which is caused by sudden and excessive discharge of neurons. Electroencephalogram (EEG) recordings is regarded as the golden standard for clinical diagnosis of epilepsy disease. The diagnosis of epilepsy disease by professional doctors clinically is time-consuming. With the help artificial intelligence algorithms, the task of automatic epileptic seizure detection and prediction is called a research hotspot. The thesis mainly contributes to propose a solution to overfitting problem of EEG signal in deep learning and a method of multiple channels fusion for EEG features. The result of proposed method achieves outstanding performance in seizure detection task and seizure prediction task. In seizure detection task, this paper mainly explores the effect of the deep learning in small data size. This thesis designs a hybrid model of CNN and SVM for epilepsy detection compared with end-to-end classification by deep learning. Another technique for overfitting is new EEG signal generation based on decomposition and recombination of EEG in time-frequency domain. It achieved a classification accuracy of 98.8%, a specificity of 98.9% and a sensitivity of 98.4% on the classic Bonn EEG data. In seizure prediction task, this paper proposes a feature fusion method for multi-channel EEG signals. We extract a three-order tensor feature in temporal, spectral and spatial domain. UMLDA is a tensor-to-vector projection method, which ensures minimal redundancy between feature dimensions. An excellent experimental result was finally obtained, including an average accuracy of 95%, 94% F1-measure and 90% Kappa index

Using EEG and NIRS for brain-computer interface and cognitive performance measures: a pilot study

This study addresses two important problem statements, namely, selection of training datasets for online Brain-Computer Interface (BCI) classifier training and determination of participant concentration levels during an experiment. The work also attempted a pilot study to integrate electroencephalograms (EEGs) and Near Infra Red Spectroscopy (NIRS) for possible applications such as the BCI and for measuring cognitive levels. Two experiments are presented, the first being a mathematical task interleaved with rest states using NIRS only. In the next, integration of the EEG-NIRS with reference to P300-based BCI systems as well as the experimental conditions designed to elicit the concentration levels (denoted as ON and OFF states here) during the paradigm, are presented. The first experiment indicates that NIRS can be used to differentiate a concentrated (i.e., mental activity) level from the rest. However, the second experiment reveals statistically significant results using the EEG only. We present details about the equipment used, the participants as well as the signal processing and machine learning techniques implemented to analyse the EEG and NIRS data. After discussing the results, we conclude by describing the research scope as well as the possible pitfalls in this work from a NIRS viewpoint, which presents an opportunity for future research exploration for BCI and cognitive performance measures

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

Near Real-Time Data Labeling Using a Depth Sensor for EMG Based Prosthetic Arms

Recognizing sEMG (Surface Electromyography) signals belonging to a particular action (e.g., lateral arm raise) automatically is a challenging task as EMG signals themselves have a lot of variation even for the same action due to several factors. To overcome this issue, there should be a proper separation which indicates similar patterns repetitively for a particular action in raw signals. A repetitive pattern is not always matched because the same action can be carried out with different time duration. Thus, a depth sensor (Kinect) was used for pattern identification where three joint angles were recording continuously which is clearly separable for a particular action while recording sEMG signals. To Segment out a repetitive pattern in angle data, MDTW (Moving Dynamic Time Warping) approach is introduced. This technique is allowed to retrieve suspected motion of interest from raw signals. MDTW based on DTW algorithm, but it will be moving through the whole dataset in a pre-defined manner which is capable of picking up almost all the suspected segments inside a given dataset an optimal way. Elevated bicep curl and lateral arm raise movements are taken as motions of interest to show how the proposed technique can be employed to achieve auto identification and labelling. The full implementation is available at https://github.com/GPrathap/OpenBCIPytho