A two-dimensional approach for lossless EEG compression

In this paper, we study various lossless compression techniques for electroencephalograph (EEG) signals. We discuss a computationally simple pre-processing technique, where EEG signal is arranged in the form of a matrix (2-D) before compression. We discuss a two-stage coder to compress the EEG matrix, with a lossy coding layer (SPIHT) and residual coding layer (arithmetic coding). This coder is optimally tuned to utilize the source memory and the i.i.d. nature of the residual. We also investigate and compare EEG compression with other schemes such as JPEG2000 image compression standard, predictive coding based shorten, and simple entropy coding. The compression algorithms are tested with University of Bonn database and Physiobank Motor/Mental Imagery database. 2-D based compression schemes yielded higher lossless compression compared to the standard vector-based compression, predictive and entropy coding schemes. The use of pre-processing technique resulted in 6% improvement, and the two-stage coder yielded a further improvement of 3% in compression performance.     

急性轻中度缺氧EEG信号的复杂度研究

建立了一个急性高空缺氧实验模型,记录了四种不同高度条件下从缺氧前(正常呼吸)到缺氧后30分钟时的EEG,分析了其复杂度。发现缺氧引起复杂度明显变化,随时间和高度增加,一定程度缺氧可使EEG复杂度低于正常。表明EEG复杂度对脑缺氧较为敏感,可用于缺氧程度进行评估,有望成为临床诊断的一个指标。     

Eigenspace Time Frequency Based Features for Accurate Seizure Detection from EEG Data

Background

Epilepsy is a neurological disorder that affects over 2% of the world population. Epilepsy patients suffer from recurring seizures that can be very harmful. The unpredictability of seizures is a major concern for medical practitioners because uncontrollable seizures can lead to sudden death and morbidity. A system that could warn patients and doctors alike about the impending seizure event would dramatically enhance the quality of life for patients.

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.     

复杂性热性惊厥脑电图特征与癫痫发生的相关性

目的：探讨复杂性热性惊厥脑电图特征与癫痫发生的相关性。方法：2012年3 月到2014 年5 月选择在我院诊治为复杂性热 性惊厥的呼吸道感染患儿86 例作为观察组,同期选择在我院诊治的非热性惊厥的呼吸道感染患儿86 例作为对照组,两组都进 行脑电图监测与认知功能判定,对癫痫发生情况进行判定与分析。结果：观察组的言语智商、行为智商与总智商评分都明显低于 对照组(P<0.05)。观察组的癫痫发生率为9.3 %,脑电图异常率为8.1 %;而对照组的癫痫发生率为1.2 %,脑电图异常率为2.3 %, 对比差异都有统计学意义(P<0.05)。在观察组患儿中,Spearman 相关性分析显示脑电图异常与癫痫发生存在明显正向相关性(r=0. 349,P<0.05)。结论：复杂性热性惊厥伴随有脑电图异常,与癫痫发生存在明显正向相关性,损害患儿的认知功能。     

Monitoring depth of anesthesia using combination of EEG measure and hemodynamic variables

Monitoring depth of anesthesia (DOA) via vital signs is a major ongoing challenge for anesthetists. A number of electroencephalogram (EEG)-based monitors such as the Bispectral (BIS) index have been proposed. However, anesthesia is related to central and autonomic nervous system functions whereas the EEG signal originates only from the central nervous system. This paper proposes an automated DOA detection system which consists of three steps. Initially, we introduce multiscale modified permutation entropy index which is robust in the characterization of the burst suppression pattern and combine multiscale information. This index quantifies the amount of complexity in EEG data and is computationally efficient, conceptually simple and artifact resistant. Then, autonomic nervous system activity is quantified with heart rate and mean arterial pressure which are easily acquired using routine monitoring machine. Finally, the extracted features are used as input to a linear discriminate analyzer (LDA). The method is validated with data obtained from 25 patients during the cardiac surgery requiring cardiopulmonary bypass. The experimental results indicate that an overall accuracy of 89.4 % can be obtained using combination of EEG measure and hemodynamic variables, together with LDA to classify the vital sign into awake, light, surgical and deep anesthetised states. The results demonstrate that the proposed method can estimate DOA more effectively than the commercial BIS index with a stronger artifact-resistance.     

基于关联维数和Lyapunov指数高频脑电非线性动力学研究

目的:探讨利用非线性动力学理论特征值来区分老年人和青年人脑电的差异。方法:采用非线性动力学的关联维数和lyapunov指数对老年组和青年组的安静闭眼、安静睁眼和N-back字母记忆事件等的高频脑电进行特征提取,对特征值进行统计分析,分析两组特征值之间的差别。结果:安静闭眼和安静睁眼事件下,老年组多数导联的关联维数和lyapunov指数的特征值与青年组的特征值存在显著差异(P均0.05);N-back字母记忆事件下,老人多数导联的关联维数特征值大于青年的特征值,但老年组只有14导联的lyapunov指数特征值大于青年组的特征值(P均0.05)。不管有无思维活动,人脑在矢状线上的五个导联Fz、FCz、Cz、CPz、Pz的关联维数和lyapunov指数的特征值都处在峰值处。结论:老年人相比青年人脑功能弱、能量发放少;在思维活动情况下,老年需要更多地发放能量来完成相同的思维活动。安静闭眼和睁眼以及N-back字母记忆事件等三个事件的关联维数和lyapunov指数特征值可用来区分老年组和青年组脑电的差异。     

Local discriminative spatial patterns for movement-related potentials-based EEG classification

A novel discriminant method, termed local discriminative spatial patterns (LDSP), is proposed for movement-related potentials (MRPs)-based single-trial electroencephalogram (EEG) classification. Different from conventional discriminative spatial patterns (DSP), LDSP explicitly considers local structure of EEG trials in the construction of scatter matrices in the Fisher-like criterion. The underlying manifold structure of two-dimensional spatio-temporal EEG signals contains more discriminative information. LDSP is an extension to DSP in the sense that DSP can be formulated as a special case of LDSP. By constructing an adjacency matrix, LDSP is calculated as a generalized eigenvalue problem, and so is computationally straightforward. Experiments on MRPs-based single-trial EEG classification show the effectiveness of the proposed LDSP method.     

Analysis of User Control Attainment in SMR-based Brain Computer Interfaces

Context

Sensorimotor rhythms (SMR) have been the neuronal phenomena of choice in non-invasive EEG-based endogenous brain computer interfaces (BCIs) for more than two decades and SMR-based BCIs have achieved the highest degree of freedom control so far. Nevertheless, they are subject to long periods of training prior to attaining a satisfactory level of control requiring users to learn to modulate their rhythms. The goal of this work is to analyse this problem, discuss the causes of the slow rise in performance and provide recommendations on alternative solutions to quicken control attainment.