Discriminative methods for classification of asynchronous imaginary motor tasks from EEG data

In this work, two methods based on statistical models that take into account the temporal changes in the electroencephalographic (EEG) signal are proposed for asynchronous brain-computer interfaces (BCI) based on imaginary motor tasks. Unlike the current approaches to asynchronous BCI systems that make use of windowed versions of the EEG data combined with static classifiers, the methods proposed here are based on discriminative models that allow sequential labeling of data. In particular, the two methods we propose for asynchronous BCI are based on conditional random fields (CRFs) and latent dynamic CRFs (LDCRFs), respectively. We describe how the asynchronous BCI problem can be posed as a classification problem based on CRFs or LDCRFs, by defining appropriate random variables and their relationships. CRF allows modeling the extrinsic dynamics of data, making it possible to model the transitions between classes, which in this context correspond to distinct tasks in an asynchronous BCI system. On the other hand, LDCRF goes beyond this approach by incorporating latent variables that permit modeling the intrinsic structure for each class and at the same time allows modeling extrinsic dynamics. We apply our proposed methods on the publicly available BCI competition III dataset V as well as a data set recorded in our laboratory. Results obtained are compared to the top algorithm in the BCI competition as well as to methods based on hierarchical hidden Markov models (HHMMs), hierarchical hidden CRF (HHCRF), neural networks based on particle swarm optimization (IPSONN) and to a recently proposed approach based on neural networks and fuzzy theory, the S-dFasArt. Our experimental analysis demonstrates the improvements provided by our proposed methods in terms of classification accuracy

A LightGBM-Based EEG Analysis Method for Driver Mental States Classification

Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)

Advanced computational intelligence strategies for mental task classification using electroencephalography signals

University of Technology, Sydney. Faculty of Engineering and Information Technology.Brain-computer interface (BCI) has been known as a cutting-edge technology in the current research. It is able to measure the brain activity directly instead of using the natural peripheral nerves and muscles and translates the user’s intent brain activity into useful control signals. There is still a need for a technology for severely disabled individuals who suffer from locked-in syndromes, such as amyotrophic lateral sclerosis (ALS), cervical spinal cord injury (SCI) or tetraplegia and brain stem stroke. A brain-computer interface (BCI) could be used here as an alternative solution for control and communication. The main aim of this research is to develop a BCI system to assist mobility as hand-free technology for people with severe disability, with improved accuracy, which provides effective classification accuracy for wheelchair control. Electroencephalography (EEG) is the chosen BCI technology because it is non-invasive, portable and inexpensive. Currently, BCI using EEG can be divided into two strategies; selective attention and spontaneous mental signal. For the selective attention strategy, BCI relies on external stimuli which might be uncomfortable for severely disabled individuals who need to focus on external stimuli and the environment simultaneously. This is not the case for BCIs which rely on spontaneous mental signals initiated by the users themselves. BCI that uses sensorimotor rhythm (SMR) is one of the examples of the spontaneous mental strategy. There have been many reports in research using SMR-based BCI; however, there are still some people who are unable to use this. As a result, in this thesis, mental task-based EEG is used as an alternative. This thesis presents the embedded EEG system for mental task classification. A prototype wireless embedded EEG system for mental task BCI classification is developed. The prototype includes a wireless EEG as head gear and an embedded system with a wireless receiver. The developed wireless EEG provides a good common mode rejection ratio (CMRR) performance and a compact size with a low current consumption coin cell battery for power. Mental tasks data are collected using the prototype system from six healthy participants which include arithmetic, figure rotation, letter composing and counting task with additional eyes closed task. The developed prototype BCI system is able to detect the dominant alpha wave between 8-13Hz during eyes closed. Using the FFT as the features extractor and artificial neural network (ANN) as the classifier, the developed prototype EEG system provides high accuracy for the eyes closed and eyes open tasks. The classification of the three mental task combinations achieve an overall accuracy of around 70%. Also, an optimized BCI system for mental task classification using the Hilbert-Huang transform (HHT) feature extractor and the genetic algorithm optimization of the artificial neural network (GA-ANN) classifier is presented. Non motor imagery mental tasks are employed, including: arithmetic, letter composing, Rubik’s cube rolling, visual counting, ringtone, spatial navigation and eyes closed task. When more mental tasks are used, users are able to choose the most effective of tasks suitable for their circumstance. The result of classification for the three user chosen mental tasks achieves accuracy between 76% and 85% using eight EEG channels with GA-ANN (classifier) and FFT (feature extractor). In a two EEG channels classification using FFT as the features extractor, the accuracy is reduced between 65% and 79%. However, the HHT features extractor provides improved accuracy between 70% and 84%. Further, an advanced BCI system using the ANN with fuzzy particle swarm optimization using cross-mutated operation (FPSOCM-ANN) for mental task classification is presented. This experiment involves five able-bodied subjects and also five patients with tetraplegia as the target group of the BCI system. The three relevant mental tasks used for the BCI concentrates on mental letter composing, mental arithmetic and mental Rubik’s cube rolling forward. Although the patients group has lower classification accuracy, this is improved by increasing the time-window of data with the best at 7s. The results classification for 7s time-window show the best classifier is using the FPSOCM-ANN (84.4% using FPSOCM-ANN, 77.4% using GA-ANN, 77.0% using SVM, 72.1% using LDA, and 71.0% using linear perceptron). For practical use of a BCI, the two channels EEG is also presented using this advanced BCI classification method (FPSOCM-ANN). For overall, O1 and C4 are the best two channels at 80.5% of accuracy, followed by the second best at P3 and O2 at 76.4% of accuracy, and the third best at C3 and O2 channels at 75.4% of accuracy

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported

EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications.

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research