Gaochao Cui

Bayesian CP factorization of incomplete tensor for EEG signal application

CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powerful and useful data analysis technique. This method can achieve the purpose of tensor completion through explicitly capturing the multilinear latent factors. Recently, a CP factorization based on a hierarchical probabilistic model has been proposed which is used fully Bayesian theory by incorporating a sparsity-inducing prior over multiple latent factors and the appropriate hyper-priors over all hyper-parameters. In this way, the rank of tensor can be determined automatically instead of traditional manual assignment. This method has been applied into image inpainting and facial image synthesis effectively. However, there is no research on the application in EEG signal processing of this method. Moreover, the EEG data loss often occurs during experiment recording period. In this paper, we used this newer data analysis method for processing EEG data set from P300 experiment including data completion under different levels of data missing and classification analysis on the recovered data. The experiment result shows that this method has a good processing performance on incomplete EEG signal.

Real-time mobile phone dialing system based on SSVEP

Brain computer interface (BCI) systems based on the steady state visual evoked potential (SSVEP) provide higher information transfer rates and require shorter training time than BCI systems using other brain signals. It has been widely used in brain science, rehabilitation engineering, biomedical engineering and intelligent information processing. In this paper, we present a real-time mobile phone dialing system based on SSVEP, and it is more portable than other dialing system because the flashing dial interface is set on a small tablet. With this online BCI system, we can take advantage of this system based on SSVEP to identify the specific frequency on behalf of a number using canonical correlation analysis (CCA) method and dialed out successfully without using any physical movements such as finger tapping. This phone dialing system will be promising to help disable patients to improve the quality of lives.

Hybrid-BCI: Classification of auditory and visual related potentials

The brain computer interface (BCI) is a technology that utilizes neurophysiological signals recorded from brain to control external machines or computers, and has become widespread in the last decade due to technical and mechanical developments. A P300-based BCI, often called P300 speller, is one of the most successful paradigm, which has shown advantages in terms of high accuracy and short training time. However, the existing P300-based BCI employs single type of external stimuli, such as visual stimuli, which limits their application domains. In this paper, we propose a hybrid-BCI system based on multiple modality of P300 evoked by simultaneous auditory and visual stimuli. The experimental results show the significant difference in ERPs between visual stimuli and multiple types of stimuli. The classification results demonstrate the effectiveness of our new BCI paradigm, which outperforms the visual P300 in terms of higher accuracy.

EEG energy analysis for evaluating consciousness level using dynamic MEMD

Analysis of electroencephalography (EEG) energy is a useful technique in the brain signal processing. In this paper, we present a novel data analysis method based on a dynamic multivariate empirical mode decomposition (D-MEMD) algorithm to analyze EEG energy of three different conscious states such as normal awake, comatose and brain death. By using D-MEMD, we can not only denoise the original EEG data but also calculate the EEG energy of subjects in a dynamic time series. Moreover, from the result, we distinguish three consciousness levels. The results of healthy subject in normal awake, comatose patient and brain death will be shown. The analyzed results illustrate the effectiveness and performance of the proposed method in calculation of EEG energy for evaluating consciousness level.

A Graph Theory Analysis on Distinguishing EEG-Based Brain Death and Coma

Electroencephalogram (EEG) is always used to diagnosis the patients consciousness clinically because it is safe and easy to be record from patients. The aim of this paper is to analysis the relations between each channel in order to find out the brain network of brain death and coma patients particularity. In this paper, we use 10 adult patients’ EEG data to calculate the partial directed coherence (PDC) and build the average brain network for the two groups’ data after t-test based on the PDC results. Results showed that, these two clinical data are at most difference in the network parameters of degree, centrality and cluster coefficient as the threshold of PDC is set of 0.3. The time-varying connectivity could lead to better understanding of non-symmetric relations between different EEG channels and application in prediction of patients in brain death or coma state.

Video denoising using low rank tensor decomposition

Reducing noise in a video sequence is of vital important in many real-world applications. One popular method is block matching collaborative filtering. However, the main drawback of this method is that noise standard deviation for the whole video sequence is known in advance. In this paper, we present a tensor based denoising framework that considers 3D patches instead of 2D patches. By collecting the similar 3D patches non-locally, we employ the low-rank tensor decomposition for collaborative filtering. Since we specify the non-informative prior over the noise precision parameter, the noise variance can be inferred automatically from observed video data. Therefore, our method is more practical, which does not require knowing the noise variance. The experimental on video denoising demonstrates the effectiveness of our proposed method.

Dynamic MEMD Associated with Approximate Entropy in Patients’ Consciousness Evaluation

Electroencephalography (EEG) based preliminary examination has been widely used in diagnosis of brain diseases. Based on previous studies, clinical brain death determination also can be actualized by analyzing EEG signal of patients. Dynamic Multivariate empirical mode decomposition (D-MEMD) and approximate entropy (ApEn) are two kinds of methods to analyze brain activity status of the patients in different perspectives for brain death determination. In our previous studies, D-MEMD and ApEn methods were always used severally and it cannot analyzing the patients’ brain activity entirety. In this paper, we present a combine analysis method based on D-MEMD and ApEn methods to determine patients’ brain activity level. Moreover, We will analysis three different status EEG data of subjects in normal awake, comatose patients and brain death. The analyzed results illustrate the effectiveness and reliability of the proposed methods.

BCI-Based Mobile Phone Using SSVEP Techniques

The brain–computer interface (BCI) system aims at creating new direct information interaction and communication channels between the human brain and computer systems without depending on the brain’s normal output channels of peripheral nerves and muscles. The BCI research has drawn the attention of scientists in brain science, rehabilitation engineering, biomedical engineering, and intelligent information processing. In this paper, we will investigate several BCI techniques based on the Steady-State Visual Evoked Potential (SSVEP), and applying to a special mobile phone system that is used by someone disable to use the mobile communication. The simulation results illustrate efficient and good performance for the proposed method.

Analysis of Brain-Death EEG Data Using 2T-EMD Algorithm

Electroencephalography (EEG) energy analysisbased on empirical mode decomposition (EMD) and Multivariateempirical mode decomposition (MEMD) methodsfor evaluating consciousness level have been proposed inmany previous studies, and the effectiveness of these analysismethods had been proved by the experiment results. In thispaper, we proposed an analysis method for quasi brain deathEEG data based on new algorithm of Turning Tangent EMD(2T-EMD). Firstly, an artificial data was employed in theexperiments in order to inspect the feasibility of the newEEG data analysis method based on 2T-EMD. Then we used2T-EMD data analysis method to analyze the real patientsEEG data that was recorded in the hospital. Finally, theexperiment results shown that 2T-EMD has advantage overother analysis methods on the computation accuracy andspeed under some certain conditions.

Patients' consciousness analysis using dynamic approximate entropy and MEMD method

Electroencephalography (EEG) based preliminary examination has been proposed in the clinical brain death determination. Multivariate empirical mode decomposition(MEMD) and approximate entropy(ApEn) are often used in the EEG signal analysis process. MEMD is an extended approach of empirical mode decomposition(EMD), in which it overcomes the problem of the decomposed number and frequency, and enables to extract brain activity features from multi-channel EEG simultaneously. ApEn as a complexity based method appears to have potential for the application to physiological and clinical time series data. In our previous studies, MEMD method and ApEn measure were always used severally, if MEMD and ApEn are used to analysis the same EEG signal simultaneously, the result of experiment will be more accurate. In this paper, we present MEMD method and ApEn measure based blind test without knowing about the clinical symptoms of patients beforehand. Features obtained from two typical cases indicate one patient being in coma and another in quasi-brain-death state.