Research on the relation of EEG signal chaos characteristics with high-level intelligence activity of human brain

Using phase space reconstruct technique from one-dimensional and multi-dimensional time series and the quantitative criterion rule of system chaos, and combining the neural network; analyses, computations and sort are conducted on electroencephalogram (EEG) signals of five kinds of human consciousness activities (relaxation, mental arithmetic of multiplication, mental composition of a letter, visualizing a 3-dimensional object being revolved about an axis, and visualizing numbers being written or erased on a blackboard). Through comparative studies on the determinacy, the phase graph, the power spectra, the approximate entropy, the correlation dimension and the Lyapunov exponent of EEG signals of 5 kinds of consciousness activities, the following conclusions are shown: (1) The statistic results of the deterministic computation indicate that chaos characteristic may lie in human consciousness activities, and central tendency measure (CTM) is consistent with phase graph, so it can be used as a division way of EEG attractor. (2) The analyses of power spectra show that ideology of single subject is almost identical but the frequency channels of different consciousness activities have slight difference. (3) The approximate entropy between different subjects exist discrepancy. Under the same conditions, the larger the approximate entropy of subject is, the better the subject's innovation is. (4) The results of the correlation dimension and the Lyapunov exponent indicate that activities of human brain exist in attractors with fractional dimensions. (5) Nonlinear quantitative criterion rule, which unites the neural network, can classify different kinds of consciousness activities well. In this paper, the results of classification indicate that the consciousness activity of arithmetic has better differentiation degree than that of abstract

Toward a model-based predictive controller design in brain-computer interfaces

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8-23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications.Grants K25NS061001 (MK) and K02MH01493 (SJS) from the National Institute of Neurological Disorders And Stroke (NINDS) and the National Institute of Mental Health (NIMH), the Portuguese Foundation for Science and Technology (FCT) Grant SFRH/BD/21529/2005 (NSD), the Pennsylvania Department of Community and Economic Development Keystone Innovation Zone Program Fund (SJS), and the Pennsylvania Department of Health using Tobacco Settlement Fund (SJS)

A common spatial pattern approach for classification of mental counting and motor execution EEG

© Springer Nature Switzerland AG 2018. A Brain Computer Interface (BCI) as a medium of communication is convenient for people with severe motor disabilities. Although there are a number of different BCIs, the focus of this paper is on Electroencephalography (EEG) as a means of human computer interaction. Motor imagery and mental arithmetic are the most popular techniques used to modulate brain waves that can be used to control devices. We show that it is possible to define different mental states using real fist rotation and imagined reverse counting. While people have already investigated left fist rotation and right fist rotation for dual state BCI, we intend to define a new state using mental reverse counting. We use Common Spatial Pattern (CSP) approach for feature extraction to distinguish between these states. CSP has been prominently used in the context of motor imagery task, we define its applicability for the distinction between motor execution and mental counting. CSP features are evaluated using classifiers like GMM, SVM, and GMM-UBM. GMM-UBM using data filtered through the beta band (13–30 Hz) gives the best performance

Electroencephalogram based brain-computer interface: An introduction

Electroencephalogram (EEG) signals are useful for diagnosing various mental conditions such as epilepsy, memory impairments and sleep disorders. Brain-Computer Interface (BCI) is a revolutionary new area using EEG that is most use-ful for the severely disabled individuals for hands-off device control and commu-nication as they create a direct interface from the brain to the external environ-ment, therefore circumventing the use of peripheral muscles and limbs. However, being non-invasive, BCI designs are not necessarily limited to this user group and other applications for gaming, music, biometrics etc have been developed more recently. This chapter will give an introduction to EEG based BCI and existing methodologies; specifically those based on transient and steady state evoked po-tentials, mental tasks and motor imagery will be described. Two real-life scenarios of EEG based BCI applications in biometrics and device control will also be brief-ly explored. Finally, current challenges and future trends of this technology will be summarized

Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques

This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose. The EEG dataset employed for the validation of the proposed method consisted of two classes: (1) the EEG signals recorded during the complex cognitive task—Raven’s advance progressive metric test and (2) the EEG signals recorded in rest condition—eyes open. The performance of four different classifiers was evaluated with four performance measures, i.e., accuracy, sensitivity, specificity and precision values. The accuracy was achieved above 98 % by the support vector machine, multi-layer perceptron and the K-nearest neighbor classifiers with approximation (A4) and detailed coefficients (D4), which represent the frequency range of 0.53–3.06 and 3.06–6.12 Hz, respectively. The findings of this study demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate.</p