A wireless brainwave-driven system for daily-life analyses and applications

Smartphones have become more popular in our lives. We will no longer need to use our hands to control phones to do such things as take pictures, switch music, or make phone calls in the future; we will use our brains: all that can be controlled with the use of brainwaves instead. In this study, we implement a novel system that contains the most commonly used functions of a smartphone, including camera use and music play, with an app that uses brainwave controls. In addition, we also provide an essential daily-use function which can remind us to concentrate when we drive, study, or do something important. Under the proposed system, when the wireless brainwave instrument is worn, brainwave signals transfer to the smartphone via Bluetooth automatically and execute the aforementioned functions. Experimental results indicate that the present system is effective and suitable for such applications in our lives. In the future, some more related applications will be developed with brainwave control for practical daily-life uses.     

基于功能连接信息熵的精神分裂症EEG分类研究

为了提高精神分裂症的有效诊断;利用网络功能连接信息熵的方法对51例精神分裂症患者和56例年龄匹配的正常人的脑电信号（Electroencephalogram;EEG）进行了分类。通过采用分频技术、相位同步分析方法、信息熵方法、支持向量机（Support Vector Machine;SVM）分类方法;大幅提高了分类准确率（98.13%）;实现了对精神分裂症的有效诊断。该分类方法主要涉及两阶段:利用分频技术和相位同步分析方法;获得各频段的脑电信号在各个时间点的功能连接矩阵;基于整个时间域上的功能连接计算各频段的信息熵;并将其分别作为功能脑网络的分类特征训练SVM分类器;进而对两组被试分类。分类结果表明;该方法大幅提高了精神分裂症检测的准确率。     

脑电信号中眼电伪迹的自动去除算法

为实现眼电伪迹的自动去除,提高算法的有效性和稳健性,提出一种眼电伪迹自动去除算法。采用样本熵和一种通用的伪迹判决方法对眼电伪迹进行自动识别,通过脑电信号的重构实现眼电伪迹的去除。实验结果表明,对于不同长度的真实脑电信号,该算法均能准确地去除眼电伪迹,较好地保留其他的脑电信号成分,且可以完全自动地去除眼电伪迹,适用于实时场合。     

一种新型智能化脑电图机的设计

本文介绍一种基于单片微机控制的新型脑电图机的设计方案，给出了其性能指标，设计构思及软硬件配置，并着重讨论了其前置放大器的设计与去极化电压的新方法。     

Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines

The electroencephalogram (EEG) has proven a valuable tool in the study and detection of epilepsy. This paper investigates for the first time the use of Permutation Entropy (PE) as a feature for automated epileptic seizure detection. A Support Vector Machine (SVM) is used to classify segments of normal and epileptic EEG based on PE values. The proposed system utilizes the fact that the EEG during epileptic seizures is characterized by lower PE than normal EEG. It is shown that average sensitivity of 94.38% and average specificity of 93.23% is obtained by using PE as a feature to characterize epileptic and seizure-free EEG, while 100% sensitivity and specificity were also obtained in single-trial classifications.     

Entropies for detection of epilepsy in EEG

The electroencephalogram (EEG) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about the state of the brain. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. The aim of this work is to compare the different entropy estimators when applied to EEG data from normal and epileptic subjects. The results obtained indicate that entropy estimators can distinguish normal and epileptic EEG data with more than 95% confidence (using t-test). The classification ability of the entropy measures is tested using ANFIS classifier. The results are promising and a classification accuracy of about 90% is achieved.     

基于局部模式的癫痫脑电信号自动分类方法

为有效地检测脑电图(EEG)中的癫痫信号,设计一维局部三值模式(1D-LTP)算子提取信号特征,并结合主成分分析(PCA)和极限学习机(ELM)对特征进行分类。通过1D-LTP算子计算信号点的顶层模式和底层模式下的特征变换码以准确滤除干扰信号,并对变换码直方图PCA降维后采用ELM进行分类,以10折交叉验证评估分类性能。实验结果表明,该方法能有效识别在癫痫发作期的EEG信号,其准确率可达99.79%。     

Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition

Epilepsy is one of the most common neurological disorders characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is an invaluable measurement for the purpose of assessing brain activities, containing information relating to the different physiological states of the brain. It is a very effective tool for understanding the complex dynamical behavior of the brain. This paper presents the application of empirical mode decomposition (EMD) for analysis of EEG signals. The EMD decomposes a EEG signal into a finite set of bandlimited signals termed intrinsic mode functions (IMFs). The Hilbert transformation of IMFs provides analytic signal representation of IMFs. The area measured from the trace of the analytic IMFs, which have circular form in the complex plane, has been used as a feature in order to discriminate normal EEG signals from the epileptic seizure EEG signals. It has been shown that the area measure of the IMFs has given good discrimination performance. Simulation results illustrate the effectiveness of the proposed method.     

Learning polynomial networks for classification of clinical electroencephalograms

We describe a polynomial network technique developed for learning to classify clinical electroencephalograms (EEGs) presented by noisy features. Using an evolutionary strategy implemented within group method of data handling, we learn classification models which are comprehensively described by sets of short-term polynomials. The polynomial models were learnt to classify the EEGs recorded from Alzheimer and healthy patients and recognize the EEG artifacts. Comparing the performances of our technique and some machine learning methods we conclude that our technique can learn well-suited polynomial models which experts can find easy-to-understand.     

Automatic classification of sleep stages based on the time-frequency image of EEG signals

In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner–Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obtain the time-frequency image (TFI). The segmentation of TFI has been performed based on the frequency-bands of the rhythms of EEG signals. The features derived from the histogram of segmented TFI have been used as an input feature set to multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of sleep stages from EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals.