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.     

Complex networks and deep learning for EEG signal analysis

Electroencephalogram (EEG) signals acquired from brain can provide an effective representation of the human’s physiological and pathological states. Up to now, much work has been conducted to study and analyze the EEG signals, aiming at spying the current states or the evolution characteristics of the complex brain system. Considering the complex interactions between different structural and functional brain regions, brain network has received a lot of attention and has made great progress in brain mechanism research. In addition, characterized by autonomous, multi-layer and diversified feature extraction, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, including brain state research. Both of them show strong ability in EEG signal analysis, but the combination of these two theories to solve the difficult classification problems based on EEG signals is still in its infancy. We here review the application of these two theories in EEG signal research, mainly involving brain–computer interface, neurological disorders and cognitive analysis. Furthermore, we also develop a framework combining recurrence plots and convolutional neural network to achieve fatigue driving recognition. The results demonstrate that complex networks and deep learning can effectively implement functional complementarity for better feature extraction and classification, especially in EEG signal analysis.     

Decision Tree in Working Memory Task Effectively Characterizes EEG Signals in Healthy Aging Adults

BackgroundThe changes in electroencephalogram (EEG) signals that reflect the changes in physiological structure, cognitive functions, and activities have been observed in healthy aging adults. It is unknown that when the brain aging initiates and whether these age-related alterations can be associated with incipient neurodegenerative diseases in healthy elderly individuals.Materials and methodsWe employed feature extraction and classification methods to classify and compare the EEG signals of middle-aged and elderly age groups. This study included 20 healthy middle-aged and 20 healthy elderly subjects. The EEG signals were recorded during a resting state (eyes-open and eyes-closed) and during a working memory (WM) task using eight electrodes. The minimum redundancy maximum relevance technique was employed in the selection of the optimal feature. Four classification methods, including decision tree, support vector machine, Naïve Bayes, and K-nearest neighbor, were used to distinguish the elderly age group from the middle-aged group based on their EEG signals.ResultsIn the resting state, a good correlation was observed among absolute power delta and theta bands and aging, whereas between beta absolute power and aging, a WM task correlation was observed. The results also indicated that the mean frequency and absolute power might be useful for the prediction and classification of EEG signals in aging individuals. Furthermore, the use of the decision tree method in a WM task state distinguished the elderly group from the middle-aged group with an accuracy of 87.5%.ConclusionsWorking memory could play a vital role in the optimization of classification of EEG signals in aging and discrimination of age-related issues associated with neurodegeneration.     

A Comparative Approach for MI-Based EEG Signals Classification Using Energy,Power and Entropy

ObjectiveThe initial principal task of a Brain-Computer Interfacing (BCI) research is to extract the best feature set from a raw EEG (Electroencephalogram) signal so that it can be used for the classification of two or multiple different events. The main goal of the paper is to develop a comparative analysis among different feature extraction techniques and classification algorithms.Materials and methodsIn this present investigation, four different methodologies have been adopted to classify the recorded MI (motor imagery) EEG signal, and their comparative study has been reported. Haar Wavelet Energy (HWE), Band Power, Cross-correlation, and Spectral Entropy (SE) based Cross-correlation feature extraction techniques have been considered to obtain the necessary features set from the raw EEG signals. Four different machine learning algorithms, viz. LDA (Linear Discriminant Analysis), QDA (Quadratic Discriminant Analysis), Naïve Bayes, and Decision Tree, have been used to classify the features.ResultsThe best average classification accuracies are 92.50%, 93.12%, 72.26%, and 98.71% using the four methods. Further, these results have been compared with some recent existing methods.ConclusionThe comparative results indicate a significant accuracy level performance improvement of the proposed methods with respect to the existing one. Hence, this presented work can guide to select the best feature extraction method and the classifier algorithm for MI-based EEG signals.     

Correlation dimension based lossless compression of EEG signals

Transmission of long duration EEG signals without loss of information is essential for telemedicine based applications. In this work, a lossless compression scheme for EEG signals based on neural network predictors using the concept of correlation dimension (CD) is proposed. EEG signals which are considered as irregular time series of chaotic processes can be characterized by the non-linear dynamic parameter CD which is a measure of the correlation among the EEG samples. The EEG samples are first divided into segments of 1 s duration and for each segment, the value of CD is calculated. Blocks of EEG samples are then constructed such that each block contains segments with closer CD values. By arranging the EEG samples in this fashion, the accuracy of the predictor is improved as it makes use of highly correlated samples. As a result, the magnitude of the prediction error decreases leading to less number of bits for transmission. Experiments are conducted using EEG signals recorded under different physiological conditions. Different neural network predictors as well as classical predictors are considered. Experimental results show that the proposed CD based preprocessing scheme improves the compression performance of the predictors significantly.     

对脑电相关维数计算中有关参数的探讨

应用Ｇｒａｓｓｂｅｒｇｅｒ－Ｐｒｏｃａｃｉａ ａｌｇｏｒｉｔｈｍ算法,通过点列长度、时间延迟、嵌入维数等参数的变化,对人的８导脑电时间序列的相关维数值的影响进行了研究。结果表明不仅参数的选用是否合理对计算值影响很大,而且脑电信号不像是一个稳定的混沌吸引子,因此拟用相关维数作用一项客观指标来准确地表示大脑的不同功能状态,几乎是不可能的。     

Electric Field Encephalography as a Tool for Functional Brain Research: A Modeling Study

We introduce the notion of Electric Field Encephalography (EFEG) based on measuring electric fields of the brain and demonstrate, using computer modeling, that given the appropriate electric field sensors this technique may have significant advantages over the current EEG technique. Unlike EEG, EFEG can be used to measure brain activity in a contactless and reference-free manner at significant distances from the head surface. Principal component analysis using simulated cortical sources demonstrated that electric field sensors positioned 3 cm away from the scalp and characterized by the same signal-to-noise ratio as EEG sensors provided the same number of uncorrelated signals as scalp EEG. When positioned on the scalp, EFEG sensors provided 2–3 times more uncorrelated signals. This significant increase in the number of uncorrelated signals can be used for more accurate assessment of brain states for non-invasive brain-computer interfaces and neurofeedback applications. It also may lead to major improvements in source localization precision. Source localization simulations for the spherical and Boundary Element Method (BEM) head models demonstrated that the localization errors are reduced two-fold when using electric fields instead of electric potentials. We have identified several techniques that could be adapted for the measurement of the electric field vector required for EFEG and anticipate that this study will stimulate new experimental approaches to utilize this new tool for functional brain research.     

基于多特征融合的多分类运动想象脑电信号识别研究

针对目前多分类运动想象脑电识别存在特征提取单一、分类准确率低等问题,提出一种多特征融合的四分类运动想象脑电识别方法来提高识别率。对预处理后的脑电信号分别使用希尔伯特-黄变换、一对多共空间模式、近似熵、模糊熵、样本熵提取结合时频—空域—非线性动力学的初始特征向量,用主成分分析降维,最后使用粒子群优化支持向量机分类。该算法通过对国际标准数据集BCI2005 Data set IIIa中的k3b受试者数据经MATLAB仿真处理后获得93.30%的识别率,均高于单一特征和其它组合特征下的识别率。分别对四名实验者实验采集运动想象脑电数据,使用本研究提出的方法处理获得了72.96%的平均识别率。结果表明多特征融合的特征提取方法能更好的表征运动想象脑电信号,使用粒子群支持向量机可取得较高的识别准确率,为人脑的认知活动提供了一种新的识别方法。     

Continuous EEG signal analysis for asynchronous BCI application

In this study, we propose a two-stage recognition system for continuous analysis of electroencephalogram (EEG) signals. An independent component analysis (ICA) and correlation coefficient are used to automatically eliminate the electrooculography (EOG) artifacts. Based on the continuous wavelet transform (CWT) and Student's two-sample t-statistics, active segment selection then detects the location of active segment in the time-frequency domain. Next, multiresolution fractal feature vectors (MFFVs) are extracted with the proposed modified fractal dimension from wavelet data. Finally, the support vector machine (SVM) is adopted for the robust classification of MFFVs. The EEG signals are continuously analyzed in 1-s segments, and every 0.5 second moves forward to simulate asynchronous BCI works in the two-stage recognition architecture. The segment is first recognized as lifted or not in the first stage, and then is classified as left or right finger lifting at stage two if the segment is recognized as lifting in the first stage. Several statistical analyses are used to evaluate the performance of the proposed system. The results indicate that it is a promising system in the applications of asynchronous BCI work.     

Classifying Response Correctness across Different Task Sets: A Machine Learning Approach

Erroneous behavior usually elicits a distinct pattern in neural waveforms. In particular, inspection of the concurrent recorded electroencephalograms (EEG) typically reveals a negative potential at fronto-central electrodes shortly following a response error (Ne or ERN) as well as an error-awareness-related positivity (Pe). Seemingly, the brain signal contains information about the occurrence of an error. Assuming a general error evaluation system, the question arises whether this information can be utilized in order to classify behavioral performance within or even across different cognitive tasks. In the present study, a machine learning approach was employed to investigate the outlined issue. Ne as well as Pe were extracted from the single-trial EEG signals of participants conducting a flanker and a mental rotation task and subjected to a machine learning classification scheme (via a support vector machine, SVM). Overall, individual performance in the flanker task was classified more accurately, with accuracy rates of above 85%. Most importantly, it was even feasible to classify responses across both tasks. In particular, an SVM trained on the flanker task could identify erroneous behavior with almost 70% accuracy in the EEG data recorded during the rotation task, and vice versa. Summed up, we replicate that the response-related EEG signal can be used to identify erroneous behavior within a particular task. Going beyond this, it was possible to classify response types across functionally different tasks. Therefore, the outlined methodological approach appears promising with respect to future applications.     

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).     

Research on the relation of EEG signal chaos characteristics with high-level intelligence activity of human brain

Using phase space reconstruct technique from one-dimensional and multi-dimensional time series and the quantitative criterion rule of system chaos, and combining the neural network; analyses, computations and sort are conducted on electroencephalogram (EEG) signals of five kinds of human consciousness activities (relaxation, mental arithmetic of multiplication, mental composition of a letter, visualizing a 3-dimensional object being revolved about an axis, and visualizing numbers being written or erased on a blackboard). Through comparative studies on the determinacy, the phase graph, the power spectra, the approximate entropy, the correlation dimension and the Lyapunov exponent of EEG signals of 5 kinds of consciousness activities, the following conclusions are shown: (1) The statistic results of the deterministic computation indicate that chaos characteristic may lie in human consciousness activities, and central tendency measure (CTM) is consistent with phase graph, so it can be used as a division way of EEG attractor. (2) The analyses of power spectra show that ideology of single subject is almost identical but the frequency channels of different consciousness activities have slight difference. (3) The approximate entropy between different subjects exist discrepancy. Under the same conditions, the larger the approximate entropy of subject is, the better the subject's innovation is. (4) The results of the correlation dimension and the Lyapunov exponent indicate that activities of human brain exist in attractors with fractional dimensions. (5) Nonlinear quantitative criterion rule, which unites the neural network, can classify different kinds of consciousness activities well. In this paper, the results of classification indicate that the consciousness activity of arithmetic has better differentiation degree than that of abstract.     

Movement-related potentials during the performance of a motor task I: The effect of learning and force

 Movement-related potentials (MRPs) recorded from the brain may be affected by several factors. These include the how well the subject knows the task and the load against which he performs it. The objective of this study is to determine how dominant these two factors are in influencing the shape and power of MRPs. MRPs were recorded during performance of a simple motor task that required learning of a force. A stochastic algorithm was used in order to partition a set of MRPs that are embedded in the surrounding electroencephalographic (EEG) activity into distinct classes according to the power of the underlying MRPs. Our results show that the most influential factor in the partition was the load against which the subject performed the task. Furthermore, it was found that learning has a smaller, though not insignificant, influence on the power of the MRPs. Received: 27 December 2000 / Accepted in revised form: 26 April 2001     

Improving N1 classification by grouping EEG trials with phases of pre-stimulus EEG oscillations

A reactive brain-computer interface using electroencephalography (EEG) relies on the classification of evoked ERP responses. As the trial-to-trial variation is evitable in EEG signals, it is a challenge to capture the consistent classification features distribution. Clustering EEG trials with similar features and utilizing a specific classifier adjusted to each cluster can improve EEG classification. In this paper, instead of measuring the similarity of ERP features, the brain states during image stimuli presentation that evoked N1 responses were used to group EEG trials. The correlation between momentary phases of pre-stimulus EEG oscillations and N1 amplitudes was analyzed. The results demonstrated that the phases of time–frequency points about 5.3 Hz and 0.3 s before the stimulus onset have significant effect on the ERP classification accuracy. Our findings revealed that N1 components in ERP fluctuated with momentary phases of EEG. We also further studied the influence of pre-stimulus momentary phases on classification of N1 features. Results showed that linear classifiers demonstrated outstanding classification performance when training and testing trials have close momentary phases. Therefore, this gave us a new direction to improve EEG classification by grouping EEG trials with similar pre-stimulus phases and using each to train unit classifiers respectively.     

Fractal dimension of electroencephalographic time series and underlying brain processes

 Fractal dimension has been proposed as a useful measure for the characterization of electrophysiological time series. This paper investigates what the pointwise dimension of electroencephalographic (EEG) time series can reveal about underlying neuronal generators. The following theoretical assumptions concerning brain function were made (i) within the cortex, strongly coupled neural assemblies exist which oscillate at certain frequencies when they are active, (ii) several such assemblies can oscillate at a time, and (iii) activity flow between assemblies is minimal. If these assumptions are made, cortical activity can be considered as the weighted sum of a finite number of oscillations (plus noise). It is shown that the correlation dimension of finite time series generated by multiple oscillators increases monotonically with the number of oscillators. Furthermore, it is shown that a reliable estimate of the pointwise dimension of the raw EEG signal can be calculated from a time series as short as a few seconds. These results indicate that (i) The pointwise dimension of the EEG allows conclusions regarding the number of independently oscillating networks in the cortex, and (ii) a reliable estimate of the pointwise dimension of the EEG is possible on the basis of short raw signals. Received: 1 September 1994/Accepted in revised form: 16 May 1995     

Feature extraction and recognition of epileptiform activity in EEG by combining PCA with ApEn

This paper proposes a new method for feature extraction and recognition of epileptiform activity in EEG signals. The method improves feature extraction speed of epileptiform activity without reducing recognition rate. Firstly, Principal component analysis (PCA) is applied to the original EEG for dimension reduction and to the decorrelation of epileptic EEG and normal EEG. Then discrete wavelet transform (DWT) combined with approximate entropy (ApEn) is performed on epileptic EEG and normal EEG, respectively. At last, Neyman–Pearson criteria are applied to classify epileptic EEG and normal ones. The main procedure is that the principle component of EEG after PCA is decomposed into several sub-band signals using DWT, and ApEn algorithm is applied to the sub-band signals at different wavelet scales. Distinct difference is found between the ApEn values of epileptic and normal EEG. The method allows recognition of epileptiform activities and discriminates them from the normal EEG. The algorithm performs well at epileptiform activity recognition in the clinic EEG data and offers a flexible tool that is intended to be generalized to the simultaneous recognition of many waveforms in EEG.     

LASSO based stimulus frequency recognition model for SSVEP BCIs

Steady-state visual evoked potential (SSVEP) has been increasingly used for the study of brain–computer interface (BCI). How to recognize SSVEP with shorter time and lower error rate is one of the key points to develop a more efficient SSVEP-based BCI. To achieve this goal, we make use of the sparsity constraint of the least absolute shrinkage and selection operator (LASSO) for the extraction of more discriminative features of SSVEP, and then we propose a LASSO model using the linear regression between electroencephalogram (EEG) recordings and the standard square-wave signals of different frequencies to recognize SSVEP without the training stage. In this study, we verified the proposed LASSO model offline with the EEG data of nine healthy subjects in contrast to canonical correlation analysis (CCA). In the experiment, when a shorter time window was used, we found that the LASSO model yielded better performance in extracting robust and detectable features of SSVEP, and the information transfer rate obtained by the LASSO model was significantly higher than that of the CCA. Our proposed method can assist to reduce the recording time without sacrificing the classification accuracy and is promising for a high-speed SSVEP-based BCI.     

LEDPatNet19: Automated Emotion Recognition Model based on Nonlinear LED Pattern Feature Extraction Function using EEG Signals

Electroencephalography (EEG) signals collected from human brains have generally been used to diagnose diseases. Moreover, EEG signals can be used in several areas such as emotion recognition, driving fatigue detection. This work presents a new emotion recognition model by using EEG signals. The primary aim of this model is to present a highly accurate emotion recognition framework by using both a hand-crafted feature generation and a deep classifier. The presented framework uses a multilevel fused feature generation network. This network has three primary phases, which are tunable Q-factor wavelet transform (TQWT), statistical feature generation, and nonlinear textural feature generation phases. TQWT is applied to the EEG data for decomposing signals into different sub-bands and create a multilevel feature generation network. In the nonlinear feature generation, an S-box of the LED block cipher is utilized to create a pattern, which is named as Led-Pattern. Moreover, statistical feature extraction is processed using the widely used statistical moments. The proposed LED pattern and statistical feature extraction functions are applied to 18 TQWT sub-bands and an original EEG signal. Therefore, the proposed hand-crafted learning model is named LEDPatNet19. To select the most informative features, ReliefF and iterative Chi2 (RFIChi2) feature selector is deployed. The proposed model has been developed on the two EEG emotion datasets, which are GAMEEMO and DREAMER datasets. Our proposed hand-crafted learning network achieved 94.58%, 92.86%, and 94.44% classification accuracies for arousal, dominance, and valance cases of the DREAMER dataset. Furthermore, the best classification accuracy of the proposed model for the GAMEEMO dataset is equal to 99.29%. These results clearly illustrate the success of the proposed LEDPatNet19.     

Classification of Motor Imagery EEG Based on Time-Domain and Frequency-Domain Dual-Stream Convolutional Neural Network

Background and objectiveAn important task of the brain-computer interface (BCI) of motor imagery is to extract effective time-domain features, frequency-domain features or time-frequency domain features from the raw electroencephalogram (EEG) signals for classification of motor imagery. However, choosing an appropriate method to combine time domain and frequency domain features to improve the performance of motor imagery recognition is still a research hotspot.MethodsIn order to fully extract and utilize the time-domain and frequency-domain features of EEG in classification tasks, this paper proposed a novel dual-stream convolutional neural network (DCNN), which can use time domain signal and frequency domain signal as the inputs, and the extracted time-domain features and frequency-domain features are fused by linear weighting for classification training. Furthermore, the weight can be learned by the DCNN automatically.ResultsThe experiments based on BCI competition II dataset III and BCI competition IV dataset 2a showed that the model proposed by this study has better performance than other conventional methods. The model used time-frequency signal as the inputs had better performance than the model only used time-domain signals or frequency-domain signals. The accuracy of classification was improved for each subject compared with the models only used one signals as the inputs.ConclusionsFurther analysis shown that the fusion weight of different subject is specifically, adjusting the weight coefficient automatically is helpful to improve the classification accuracy.