面向分段函数的定性仿真算法PQSIM及其在脑电图研究中的应用

应用最广泛的QSIM算法针对的是连续函数,无法处理不同状态满足不同约束的问题。PQSIM算法作了相应改进,增加两个新的约束：判断约束IF和赋值约束EQ,限定约束作用的状态集合,从而可以有效地对分段函数系统进行定性仿真。该文以其在脑电图定性产生模型中的应用实例验证算法的正确性,同时也展示出PQSIM算法为脑电图研究开辟了新路。通过时间复杂性计算证明PQSIM算法是一个高效的定性仿真算法。     

基于LabWindows/CVI与Matlab混编的在线BCI系统

基于LabWindows/CVI和Matlab设计一个BCI在线控制系统,对8Hz～30Hz的运动想象脑电信号提取时域均值、中值偏差估计、瞬时能量均值、AR模型参数等特征,应用增量式支持向量机进行分类,实现人脑对虚拟汽车直接控制。系统采用了多线程技术,保证各项工作的同时进行,在CVI中完成脑电数据采集、Matlab调用和控制指令的发送,在Matlab中进行脑电模式识别,两个程序共同完成对虚拟汽车的运动控制。经过实际测试证明,该系统具有操作简单方便、界面友好、可扩展性强、效率和可靠性高等优点,进一步推动了BCI的应用。     

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.     

Toward a psychophysiology of performance: Sport psychology principles dealing with anxiety.

In this applied case study during an off-season period, a unique and innovative internal imagery/video/electroencephalogram (EEG) biofeedback protocol was used to train visual attention and increase self-confidence of a collegiate baseball player struggling to recover from a serious eye injury. Results from the ensuing competitive season revealed that self-confidence increased and hitting and fielding performance improved dramatically. In the subsequent competitive season, without psychological skill training, hitting and fielding performance declined to preintervention levels. Although EEG biofeedback has been slow to gain acceptance among applied practitioners, used in conjunction with video and internal imagery, it appears to have potential as a tool for training visual attention in athletes within a variety of externally paced sports, such as baseball, softball, and racquet sports. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Identification of Driving Intention Based on EEG Signals

The driver''s intention is recognized by electroencephalogram(EEG) signals under different driving conditions to provide theoretical and practical support for the applications of automated driving. An EEG signal acquisition system is established by designing a driving simulation experiment, in which data of the driver''s EEG signals before turning left, turning right, and going straight, are collected in a specified time window. The collected EEG signals are analyzed and processed by wavelet packet transform to extract characteristic parameters. A driving intention recognition model, based on neural network, is established, and particle swarm optimization (PSO) is adopted to optimize the model parameters. The extracted characteristic parameters are inputted into the recognition model to identify driving intention before turning left, turning right, and going straight. Matlab is used to simulate and verify the established model to obtain the results of the model.The maximum recognition rate of driving intention is 92.9%. Results show that the driver''s EEG signal can be used to analyze the law of EEG signals. Furthermore, the PSO-based neural network model can be adapted to recognize driving intention.     

Unsupervised adaptation of electroencephalogram signal processing based on fuzzy C‐means algorithm

This paper studies an unsupervised approach for online adaptation of electroencephalogram (EEG) based brain–computer interface (BCI). The approach is based on the fuzzy C‐means (FCM) algorithm. It can be used to improve the adaptability of BCIs to the change in brain states by online updating the linear discriminant analysis classifier. In order to evaluate the performance of the proposed approach, we applied it to a set of simulation data and compared with other unsupervised adaptation algorithms. The results show that the FCM‐based algorithm can achieve a desirable capability in adapting to changes and discovering class information from unlabeled data. The algorithm has also been tested by the real EEG data recorded in experiments in our laboratory and the data from other sources (set IIb of the BCI Competition IV). The results of real data are consistent with that of simulation data. Copyright © 2011 John Wiley & Sons, Ltd.     

Cross-task emotion recognition using eeg measures: first step towards practical application

Electroencephalographic (EEG)-based emotion recognition has received increasing attention in the field of human-computer interaction (HCI) recently, there however remains a number of challenges in building a generalized emotion recognition model, one of which includes the difficulty of an EEG-based emotion classifier trained on a specific task to handle other tasks. Little attention has been paid to this issue. The current study is to determine the feasibility of coping with this challenge using feature selection. 12 healthy volunteers were emotionally elicited when conducting picture induced and video induced tasks. Firstly, support vector machine (SVM) classifier was examined under within-task conditions (trained and tested on the same task) and cross-task conditions (trained on one task and tested on another task) for picture induced and video induced tasks. The within-task classification performed fairly well (classification accuracy: 51.6% for picture task and 94.4% for video task). Cross-task classification, however, deteriorated to low levels (around 44%). Trained and tested with the most robust feature subset selected by SVM-recursive feature elimination (RFE), the performance of cross-task classifier was significantly improved to above 68%. These results suggest that cross-task emotion recognition is feasible with proper methods and bring EEG-based emotion recognition models closer to being able to discriminate emotion states for any tasks.     

An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism

Alcoholism affects the structure and functioning of brain. Electroencephalogram (EEG) signals can depict the state of brain. The EEG signals are ensemble of various neuronal activity recorded from different scalp regions having different characteristics and very low magnitude in microvolts. These factors make human interpretation difficult and time consuming to analyze these signals. Moreover, these highly varying EEG signals are susceptible to inter/intra variability errors. So, a Computer-Aided Diagnosis (CAD) can be used to identify the alcoholic and normal subjects accurately. However, these EEG signals exhibit nonlinear and non-stationary properties. Therefore, it needs much effort in deciphering the diagnostic evidence from them using linear time and frequency-domain methods. The nonlinear parameters together with time-frequency/scale domain methods can help to detect tiny changes in these signals. The correntropy is nonlinear indicator which characterizes the dynamic behavior of EEG signals in time-scale domain. In this paper, we present a new way for diagnosis of alcoholism using Tunable-Q Wavelet Transform (TQWT) based features derived from EEG signals. The feature extraction is performed using TQWT based decomposition and extracted Centered Correntropy (CC) from the forth decomposed detail sub-band. The Principal Component Analysis (PCA) is used for feature reduction followed by Least Squares-Support Vector Machine (LS-SVM) for classifying normal and alcoholic EEG signals. In order to make sure reliable classification performance, 10-fold cross-validation scheme is adopted. Our proposed system is able to diagnose the alcoholic and normal EEG signals, with an average accuracy of 97.02%, sensitivity of 96.53%, specificity of 97.50% and Matthews correlation coefficient of 0.9494 for Q-factor (Q) varying between 3 and 8 using Radial Basis Function (RBF) kernel function. Also, we have established a novel Alcoholism Risk Index (ARI) using three clinically significant features to discriminate the given classes by means of a single number. This system can be used for automated diagnosis and monitoring of alcoholic subjects to evaluate the effect of treatment.     

Biogeography based hybrid scheme for automatic detection of epileptic seizures from EEG signatures

Biogeography Based Optimization (BBO) algorithm is one of the nature-inspired optimization methods, based on the study of geographical distribution of species on earth. The present research work is based upon decomposition of human electroencephalograms (EEG) signal by Wavelet Packet Transform, computation of a complete feature set using statistical and non-statistical properties followed by optimal selection of features by BBO; the optimality criterion being classification rate. The stopping criterion for BBO is set to 100% correct classification rate. The proposed algorithm is novel in terms of TWSVM computing the Habitat Suitability Index (HSI) values for BBO, perfect classification rate, low computation time, and feature selection mechanism. The proposed scheme outperforms several previous results reported in literature in that it gives a feature subset which gives 100% classification accuracy for different classification instances.     

Adaptive filtering of EEG/ERP through Bounded Range Artificial Bee Colony (BR-ABC) algorithm

In this paper, the Artificial Bee Colony (ABC) algorithm is applied to construct Adaptive Noise Canceller (ANC) for electroencephalogram (EEG)/Event Related Potential (ERP) filtering with modified range selection, described as Bounded Range ABC (BR-ABC). ERP generated due to hand movement is filtered through Adaptive Noise Canceller (ANC) from the EEG signals. ANCs are also implemented with Least Mean Square (LMS) and Recursive Least Square (RLS) algorithm. Performance of the algorithms is evaluated in terms of Signal-to-Noise Ratio (SNR) in dB, correlation between resultant and template ERP, and mean value difference. Testing of their noise attenuation capability is done on contaminated ERP with white noise at different SNR levels. A comparative study of the performance of conventional gradient based methods like LMS, RLS, and ABC algorithm is also made which reveals that ABC algorithm gives better performance in highly noisy environment.