Analysis and classification of speech imagery EEG for BCI

Electroencephalogram (EEG) is generally used in brain–computer interface (BCI), including motor imagery, mental task, steady-state evoked potentials (SSEPs) and P300. In order to complement existing motor-based control paradigms, this paper proposed a novel imagery mode: speech imagery. Chinese characters are monosyllabic and one Chinese character can express one meaning. Thus, eight Chinese subjects were required to read two Chinese characters in mind in this experiment. There were different shapes, pronunciations and meanings between two Chinese characters. Feature vectors of EEG signals were extracted by common spatial patterns (CSP), and then these vectors were classified by support vector machine (SVM). The accuracy between two characters was not superior. However, it was still effective to distinguish whether subjects were reading one character in mind, and the accuracies were between 73.65% and 95.76%. The results were better than vowel speech imagery, and they were suitable for asynchronous BCI. BCI systems will be also extended from motor imagery to combine motor imagery and speech imagery in the future.     

Gabor based analysis prior formulation for EEG signal reconstruction

This paper deals with the problem of tele-monitoring EEG signals. In EEG tele-monitoring system, the integral step is to compress the signals in computationally efficient manner so that they can be transmitted over a limited bandwidth. In such a situation a Compressed Sensing (CS) framework for compressing and recovering the signals is the most viable approach. Previously the well known synthesis prior formulation is used for reconstruction. For the first time in this work, we show that the lesser known analysis prior formulation is a more appropriate way to frame the reconstruction problem. We show that our method yields better results than the previous synthesis prior formulation.     

A digital signal processing system for EEG frequency analysis

This paper describes the digital signal processing work of a research project for studying children's cognitive processes by analyzing EEG signals during school-related tasks. The EEG being analyzed involves two homologous channels (left and right parietal area), and is recorded on magnetic tapes. The objective of the analysis is to determine if, by examining the alpha band of the ongoing EEG, different school tasks and correct vs incorrect responses can be detected. Analysis of alpha-band calls for the determination of signal power in the 7-12 Hz frequency band (adjusted for the age of the subjects) for each channel as well as correlation between the channels. A digital signal processing scheme implemented on an Apple II microcomputer was developed for such an analysis. The results obtained are discussed.     

Improving the separability of multiple EEG features for a BCI by neural-time-series-prediction-preprocessing

A recently proposed method for EEG preprocessing is extended and analyzed in this work via a range of different tests in combination with various other BCI components. Neural-time-series-prediction-processing (NTSPP) is a predictive approach to EEG preprocessing where prediction models (PMs) are trained to perform one-step-ahead prediction of the EEG times-series which reflect motor imagery induced alterations in neuronal activity. Due to the specialization of distinct PMs, the predicted signals (Ys) and error signals (Es) are distinctly different from the original (Os) signals. The PMs map the Os signals to a higher dimension which, in the majority of cases, produces features that are more separable than those produced by the Os signals. Four feature extraction procedures, ranging in complexity and in terms of the information which is extracted i.e., time domain, frequency domain and time–frequency (t–f) domain, are used to determine the separability enhancements which are verified by comparative statistical tests and brain–computer interface (BCI) tests on six subjects. It is shown that, in the majority of the tests, features extracted from the NTSPP signals are more separable than those extracted from the Os signals, in terms of increased Euclidean distance between class means, reduced inter-class correlations and intra-class variance, and higher classification accuracy (CA), information transfer (IT) rate and mutual information (MI).     

Attractor analysis of asynchronous Boolean models of signal transduction networks

Prior work on the dynamics of Boolean networks, including analysis of the state space attractors and the basin of attraction of each attractor, has mainly focused on synchronous update of the nodes’ states. Although the simplicity of synchronous updating makes it very attractive, it fails to take into account the variety of time scales associated with different types of biological processes. Several different asynchronous update methods have been proposed to overcome this limitation, but there have not been any systematic comparisons of the dynamic behaviors displayed by the same system under different update methods. Here we fill this gap by combining theoretical analysis such as solution of scalar equations and Markov chain techniques, as well as numerical simulations to carry out a thorough comparative study on the dynamic behavior of a previously proposed Boolean model of a signal transduction network in plants. Prior evidence suggests that this network admits oscillations, but it is not known whether these oscillations are sustained. We perform an attractor analysis of this system using synchronous and three different asynchronous updating schemes both in the case of the unperturbed (wild-type) and perturbed (node-disrupted) systems. This analysis reveals that while the wild-type system possesses an update-independent fixed point, any oscillations eventually disappear unless strict constraints regarding the timing of certain processes and the initial state of the system are satisfied. Interestingly, in the case of disruption of a particular node all models lead to an extended attractor. Overall, our work provides a roadmap on how Boolean network modeling can be used as a predictive tool to uncover the dynamic patterns of a biological system under various internal and environmental perturbations.     

On the Quantification of SSVEP Frequency Responses in Human EEG in Realistic BCI Conditions

This article concerns one of the most important problems of brain-computer interfaces (BCI) based on Steady State Visual Evoked Potentials (SSVEP), that is the selection of the a-priori most suitable frequencies for stimulation. Previous works related to this problem were done either with measuring systems that have little in common with actual BCI systems (e.g., single flashing LED) or were presented on a small number of subjects, or the tested frequency range did not cover a broad spectrum. Their results indicate a strong SSVEP response around 10 Hz, in the range 13–25 Hz, and at high frequencies in the band of 40–60 Hz. In the case of BCI interfaces, stimulation with frequencies from various ranges are used. The frequencies are often adapted for each user separately. The selection of these frequencies, however, was not yet justified in quantitative group-level study with proper statistical account for inter-subject variability. The aim of this study is to determine the SSVEP response curve, that is, the magnitude of the evoked signal as a function of frequency. The SSVEP response was induced in conditions as close as possible to the actual BCI system, using a wide range of frequencies (5–30 Hz, in step of 1 Hz). The data were obtained for 10 subjects. SSVEP curves for individual subjects and the population curve was determined. Statistical analysis were conducted both on the level of individual subjects and for the group. The main result of the study is the identification of the optimal range of frequencies, which is 12–18 Hz, for the registration of SSVEP phenomena. The applied criterion of optimality was: to find the largest contiguous range of frequencies yielding the strong and constant-level SSVEP response.     

A model for dual channel segmentation of the EEG signal

Dual channel segmentation of the EEG signal has been developed. The purpose was to divide the signals into segments, according to information common for the two channels. The criterion for segmentation was based on the changes in the cross-spectrum of the two signals. It has been shown theoretically, as well as by simulation studies and by analysis of real EEG data that this method is sensitive to changes common for both channels, whereas segmentation does not occur as a result of changes in each channel separately.     

Continuous recycling: a mechanism for modulatory signal transduction

Modulatory signal transduction commonly requires efficient "on demand" assembly of specific multicomponent cellular machines that convert signals to cellular actions. This article suggests that for these signaling machines to detect and respond to fluctuations in signal strength, they must be continuously disassembled in an energy-dependent process that probably involves molecular chaperones.     

Single tap identification for fast BCI control

One of the major aims of BCI research is devoted to achieving faster and more efficient control of external devices. The identification of individual tap events in a motor imagery BCI is therefore a desirable goal. EEG is recorded from subjects performing and imagining finger taps with their left and right hands. A Differential Evolution based feature selection wrapper is used in order to identify optimal features in the spatial and frequency domains for tap identification. Channel-frequency band combinations are found which allow differentiation of tap vs. no-tap control conditions for executed and imagined taps. Left vs. right hand taps may also be differentiated with features found in this manner. A sliding time window is then used to accurately identify individual taps in the executed tap and imagined tap conditions. Highly statistically significant classification accuracies are achieved with time windows of 0.5 s and more allowing taps to be identified on a single trial basis.     

Metabolic control analysis. An application of signal flow graphs.

In order to study particle phagocytosis and glycogenolysis simultaneously, this study was designed to develop a direct-read-out method to monitor Kupffer-cell function continuously, based on the uptake of colloidal carbon by the isolated perfused rat liver. Livers were perfused for 20 min with Krebs-Henseleit buffer saturated with O2/CO2 (19:1). Colloidal carbon (1-2 mg/ml) was added to the buffer, and absorbance of carbon was monitored continuously at 623 nm in the effluent perfusate. Since colloidal-carbon uptake was proportional to A623, rates of uptake were determined from the influent minus effluent concentration difference, the flow rate and the liver wet weight. Rates of colloidal-carbon uptake were 50-200 mg/h per g and were proportional to the concentration of carbon infused. Data from light-microscopy and cell-separation studies demonstrated that carbon was taken up exclusively by non-parenchymal cells and predominantly by Kupffer cells. Further, the amount of colloidal carbon detected histologically in non-parenchymal cells increased as the concentration of colloidal carbon in the perfusate was elevated. When Kupffer cells were activated or inhibited by treatment with endotoxin or methyl palmitate, carbon uptake was increased or decreased respectively. Taken together, these results indicate that Kupffer-cell function can be monitored continuously in a living organ. This new method was utilized to compare the time course of phagocytosis of carbon by Kupffer cells and carbohydrate output by parenchymal cells. Carbohydrate output increased rapidly by 69 +/- 9 mumol per g within 2-4 min after addition of carbon and returned to basal values within 12-16 min. However, carbon uptake by the liver did not reach maximal rates until about 15 min. Infusion of a cyclo-oxygenase inhibitor, aspirin (10 mM), caused a progressive decrease in carbohydrate output and blocked the stimulation by carbon completely. Aspirin neither altered rates of carbon uptake nor prevented stimulation of carbohydrate release by addition of N2-saturated buffer. The data from these experiments are consistent with the hypothesis that output of mediators by Kupffer cells, presumably prostaglandin D2 and E2, occurs transiently as Kupffer cells begin to phagocytose foreign particles in the intact organ, a process which continues at high rates for hours.     

EMD-based EEG signal enhancement for auditory evoked potential recovery under high stimulus-rate paradigm

Short inter-stimulus interval (ISI) is one inherent characteristic of the high stimulus-rate (HSR) paradigms for studying auditory evoked potentials (AEPs). At short ISIs, the AEPs to adjacent stimuli overlap. To resolve the AEP to a specific stimulus requires an inverse process of overlapping. Inverse filtering (also called as deconvolution) has been commonly employed to achieve this goal. However, the resulted signal may be severely distorted as inverse filtering can substantially amplify such undesired components as noises and artifacts in the raw EEG recordings. In practice, even if care be taken to obtain quality EEGs, noises and artifacts are unavoidable. It is thus critical to remove or at least supress these undesired components for studies using HSR paradigms. In this paper, we propose a systematic approach to EEG signal enhancement based on empirical mode decomposition (EMD) and threshold filtering/rejection. Using synthetic and real data, we test the effectiveness of our approach. Results for both types of data consistently demonstrate that our methods can significantly improve the quality of recovered AEPs, according to visual inspection and SNRs estimated using two metrics.     

Blind noise reduction for multisensory signals using ICA and subspace filtering, with application to EEG analysis

 In many applications of signal processing, especially in communications and biomedicine, preprocessing is necessary to remove noise from data recorded by multiple sensors. Typically, each sensor or electrode measures the noisy mixture of original source signals. In this paper a noise reduction technique using independent component analysis (ICA) and subspace filtering is presented. In this approach we apply subspace filtering not to the observed raw data but to a demixed version of these data obtained by ICA. Finite impulse response filters are employed whose vectors are parameters estimated based on signal subspace extraction. ICA allows us to filter independent components. After the noise is removed we reconstruct the enhanced independent components to obtain clean original signals; i.e., we project the data to sensor level. Simulations as well as real application results for EEG-signal noise elimination are included to show the validity and effectiveness of the proposed approach. Received: 6 November 2000 / Accepted in revised form: 12 November 2001     

Efficient interaction for automated chromosome analysis using asynchronous parallel processes.

It is likely that any practical automated chromosome analysis system will be interactive. To prevent long pauses in the stream of operator interactions, it is necessary, if using standard computer hardware, to configure for asynchronous and parallel operation. A system is presented which uses several computer processors, which can support one or more operators, and which divides processing into interactive and noninteractive sections, smoothes the rate of presentation of interactions, and keeps both the operator and the computer fully employed.     

EasyLCMS: An asynchronous web application for the automated quantification of LC-MS data

ABSTRACT: BACKGROUND: Downstream applications in metabolomics, as well as mathematical modelling, require data in a quantitative format, which may also necessitate the automated and simultaneous quantification of numerous metabolites. Although numerous applications have been previously developed for metabolomics data handling, automated calibration and calculation of the concentrations in terms of mumol have not been carried out. Moreover, most of the metabolomics applications are designed for GC-MS, and would not be suitable for LC-MS, since in LC, the deviation in the retention time is not linear, which is not taken into account in these applications. Moreover, only a few are web-based applications, which could improve stand-alone software in terms of compatibility, sharing capabilities and hardware requirements, even though a strong bandwidth is required. Furthermore, none of these incorporate asynchronous communication to allow real-time interaction with pre-processed results. FINDINGS: Here, we present EasyLCMS (http://www.easylcms.es/), a new application for automated quantification which was validated using more than 1000 concentration comparisons in real samples with manual operation. The results showed that only 1% of the quantifications presented a relative error higher than 15%. Using clustering analysis, the metabolites with the highest relative error distributions were identified and studied to solve recurrent mistakes. CONCLUSIONS: EasyLCMS is a new web application designed to quantify numerous metabolites, simultaneously integrating LC distortions and asynchronous web technology to present a visual interface with dynamic interaction which allows checking and correction of LC-MS raw data pre-processing results. Moreover, quantified data obtained with EasyLCMS are fully compatible with numerous downstream applications, as well as for mathematical modelling in the systems biology field.     

A bayesian model for exploiting application constraints to enable unsupervised training of a P300-based BCI

This work introduces a novel classifier for a P300-based speller, which, contrary to common methods, can be trained entirely unsupervisedly using an Expectation Maximization approach, eliminating the need for costly dataset collection or tedious calibration sessions. We use publicly available datasets for validation of our method and show that our unsupervised classifier performs competitively with supervised state-of-the-art spellers. Finally, we demonstrate the added value of our method in different experimental settings which reflect realistic usage situations of increasing difficulty and which would be difficult or impossible to tackle with existing supervised or adaptive methods.     

Deformable models for segmentation of CLSM tissue images and its application in FISH signal analysis.

In this paper we present an application of deformable models for the segmentation of volumetric tissue images. The three-dimensional images are obtained using confocal microscope. The segmented images have been used for the quantitative analysis of the Fluorescence In Situ Hybridization (FISH) signals. An ellipsoidal surface initialized around the cell of interest acts as a deformable model. The deformable model surface voxels are subjected to various internal and external forces derived from underlying image features as well as externally imposed constraints. The deformable model converges to the optimum cell shape when the vector sum of all the forces acting on the model is zero. The result of segmentation is used to confirm the cell membership of the FISH signals and to reject all the signals that lie outside the cell nuclei. Three-dimensional region isolation and labeling technique is used to label and count the FISH signals per cell nucleus. A simple study on the effect of different segmentation methods over a quantitative analysis of FISH signals is also presented.     

Detection of quadratic phase coupling between EEG signal components by nonparamatric and parametric methods of bispectral analysis]

Recently the assumption of the independence of individual frequency components in a signal has been rejected, for example, for the EEG during defined physiological states such as sleep or sedation [9, 10]. Thus, the use of higher-order spectral analysis capable of detecting interrelations between individual signal components has proved useful. The aim of the present study was to investigate the quality of various non-parametric and parametric estimation algorithms using simulated as well as true physiological data. We employed standard algorithms available for the MATLAB. The results clearly show that parametric bispectral estimation is superior to non-parametric estimation in terms of the quality of peak localisation and the discrimination from other peaks.     

Rhythmic Oscillations in Quantitative EEG Measured During a Continuous Performance Task

The objective of the present investigation was to determine if cyclic variations in human performance recorded during a 30 min continuous performance task would parallel cyclic variations in right-hemisphere beta-wave activity. A fast fourier transformation was performed on the quantitative electroencephalogram (qEEG) and the performance record of each participant (N = 62), producing an individual periodogram for each outcome measure. An average periodogram was then produced for both qEEG and performance by combining (averaging) the amplitudes associated with each periodicity in the 62 original periodograms. Periodicities ranging from 1.00 to 2.00 min and from 4.70 to 5.70 min with amplitudes greater than would be expected due to chance were retained (Smith et al. 2003). The results of the present investigation validate the existence of cyclic variations in human performance that have been identified previously (Smith et al. 2003) and extend those findings by implicating right-hemisphere mediated arousal in the process (Arruda et al. 1996, 1999, 2007). Significant cyclic variations in left-hemisphere beta-wave activity were not observed. Taken together, the findings of the present investigation support a model of sustained attention that predicts cyclic changes in human performance that are the result of cyclic changes in right-hemisphere arousal.