EEG Pattern Recognition Research Articles

Exploring EEG Emotion Recognition through Complex Networks: Insights from the Visibility Graph of Ordinal Patterns

The construction of complex networks from electroencephalography (EEG) proves to be an effective method for representing emotion patterns in affection computing as it offers rich spatiotemporal EEG features associated with brain emotions. In this paper, we propose a novel method for constructing complex networks from EEG signals for emotion recognition, which begins with phase space reconstruction to obtain ordinal patterns and subsequently forms a graph network representation from the sequence of ordinal patterns based on the visibility graph method, named ComNet-PSR-VG. For the proposed ComNet-PSR-VG, the initial step involves mapping EEG signals into a series of ordinal partitions using phase space reconstruction, generating a sequence of ordinal patterns. These ordinal patterns are then quantified to form a symbolized new sequence. Subsequently, the resulting symbolized sequence of ordinal patterns is transformed into a graph network using the visibility graph method. Two types of network node measures, average node degree (AND) and node degree entropy (NDE), are extracted from the graph networks as the inputs of machine learning for EEG emotion recognition. To evaluate the effectiveness of the proposed construction method of complex networks based on the visibility graph of ordinal patterns, comparative experiments are conducted using two types of simulated signals (random and Lorenz signals). Subsequently, EEG emotion recognition is performed on the SEED EEG emotion dataset. The experimental results show that, with AND as the feature, our proposed method is 4.88% higher than the existing visibility graph method and 12.23% higher than the phase space reconstruction method. These findings indicate that our proposed novel method for constructing complex networks from EEG signals not only achieves effective emotional EEG pattern recognition but also exhibits the potential for extension to other EEG pattern learning tasks, suggesting broad adaptability and application potential for our method.

Epileptic EEG patterns recognition through machine learning techniques and relevant time-frequency features.

The present study is designed to explore the process of epileptic patterns' automatic detection, specifically, epileptic spikes and high-frequency oscillations (HFOs), via a selection of machine learning (ML) techniques. The primary motivation for conducting such a research lies mainly in the need to investigate the long-term electroencephalography (EEG) recordings' visual examination process, often considered as a time-consuming and potentially error-prone procedure, requiring a great deal of mental focus and highly experimented neurologists. On attempting to resolve such a challenge, a number of state-of-the-art ML algorithms have been evaluated and compare in terms of performance, to pinpoint the most effective algorithm fit for accurately extracting epileptic EEG patterns. Based on intracranial as well as simulated EEG data, the attained findings turn out to reveal that the randomforest (RF) method proved to be the most consistently effective approach, significantly outperforming the entirety of examined methods in terms of EEG recordings epileptic-pattern identification. Indeed, the RF classifier appeared to record an average balanced classification rate (BCR) of 92.38 % in regard to spikes recognition process, and 78.77 % in terms of HFOs detection. Compared to other approaches, our results provide valuable insights into the RF classifier's effectiveness as a powerful ML technique, fit for detecting EEG signals born epileptic bursts. As a potential future work, we envisage to further validate and sustain our major reached findings through incorporating a larger EEG dataset. We also aim to explore the generative adversarial networks (GANs) application so as to generate synthetic EEG signals or combine signal generation techniques with deep learning approaches. Through this new vein of thought, we actually preconize to enhance and boost the automated detection methods' performance even more, thereby, noticeably enhancing the epileptic EEG pattern recognition area.

Comparing between Different Sets of Preprocessing, Classifiers, and Channels Selection Techniques to Optimise Motor Imagery Pattern Classification System from EEG Pattern Recognition

The ability to control external devices through thought is increasingly becoming a reality. Human beings can use the electrical signals of their brain to interact or change the surrounding environment and more. The development of this technology called brain-computer interface (BCI) will increasingly allow people with motor disabilities to communicate or use assistive devices to walk, manipulate objects and communicate. Using data from the PhysioNet database, this study implemented a pattern classification system for use in a BCI on 109 healthy volunteers during real movement activities and motor imagery recorded by 64-channels electroencephalography (EEG) system. Different classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Trees (TREE) were applied on different combinations of EEG channels. Starting from two channels (C3, C4 and CP3 and CP4) positioned on the contralateral and ipsilateral sensorimotor cortex, the Region of Interest (RoI) centred on C3/Cp3 and C4/Cp4 and, finally, a data-driven automatic channels selection was tested to explore the best channel combination able to increase the classification accuracy. The results showed that the proposed automatic channels selection was able to significantly improve the performance of each classifier achieving 98% of accuracy for classification of real and imagined hand movement (sensitivity = 97%, specificity = 99%, AUC = 0.99) by SVM. While the accuracy of the classification between the imagery of hand and foot movements was 91% (sensitivity = 87%, specificity = 86%, AUC = 0.93) also with SVM. In the proposed approach, the data-driven automatic channels selection outperforms classical a priori channel selection models such as C3/C4, Cp3/Cp4, or RoIs around those channels with the utmost accuracy to help remove the boundaries of human communication and improve the quality of life of people with disabilities.

Sparse representation-based classification with two-dimensional dictionary optimization for motor imagery EEG pattern recognition

BackgroundSparse representation-based classification (SRC) has more advantages in motor imagery EEG pattern recognition, and the quality of dictionary construction directly determines the performance of SRC. In this paper, we proposed a two-dimensional dictionary optimization (TDDO) method to directly improve the performance of SRC. New MethodFirstly, an initial dictionary was constructed with multi-band features extracted by filter band common spatial pattern (FBCSP). Then Lasso regression is used to select significant features in each atom synchronously in the horizontal direction, and the KNN-based method is used to clean up noise atoms in the vertical direction. Finally, an SRC method by training samples linearly representing test samples was implemented in classification. ResultsThe results show the necessity and rationality of TDDO-SRC method. The highest average classification accuracy of 86.5% and 92.4% is obtained on two public datasets. Comparison with Existing Method(s)The proposed method has more superior classification accuracy compared to traditional methods and existing winners’ methods. ConclusionsThe quality of dictionary construction has a great impact on the robustness of SRC. And compared with the original SRC, the classification accuracy of the optimized TDDO-SRC is greatly improved.

Electrographic pattern recognition: A simple tool to predict clinical outcome in children with lissencephaly

PurposeTo describe and correlate the clinical, radiological and EEG findings in children with lissencephaly. MethodRetrospective record analysis of children with lissencephaly presenting to tertiary health centre in Northern India was performed. Radiological classification and severity scoring were done. EEG findings were categorized into three patterns and its association with clinical severity was studied. ResultsTwenty-eight children (males = 17) with lissencephaly were enrolled. Median age at diagnosis was 6.5months (range 3days-3years). Global developmental delay (median social quotient (SQ) = 25 (range15−68) was seen in all; motor deficits in 23 (82 %); epilepsy in 21 (75 %); behavioural problems in 18 (64 %); ophthalmic problems in 17 (61 %); microcephaly in 13 (46 %); feeding difficulty in 12 (43 %). Radiologically, classical Type I lissencephaly was seen in 18(64 %), cobblestone variant (Type II) in 5 (18 %) and microlissencephaly in 5 (18 %). Grade 4 (diffuse pachygyria) radiologic severity was most common (severity grade 1–6); no cases with severity score 5 or 6 were seen. The clinical profile did not correspond with radiological severity grading. EEG pattern recognition revealed pattern I in 14 (50 %); pattern II in 6 (21 %); pattern III in 8 (29 %). Children with pattern III EEG had drug resistant epilepsy and severe developmental delay. No relationship between EEG patterns and radiological severity grading was evident. ConclusionEEG is better predictor of clinical status and outcome rather than radiological severity grading. EEG pattern III is associated with severe developmental delay and drug resistant epilepsy.

Spatial-Frequency Feature Learning and Classification of Motor Imagery EEG Based on Deep Convolution Neural Network.

EEG pattern recognition is an important part of motor imagery- (MI-) based brain computer interface (BCI) system. Traditional EEG pattern recognition algorithm usually includes two steps, namely, feature extraction and feature classification. In feature extraction, common spatial pattern (CSP) is one of the most frequently used algorithms. However, in order to extract the optimal CSP features, prior knowledge and complex parameter adjustment are often required. Convolutional neural network (CNN) is one of the most popular deep learning models at present. Within CNN, feature learning and pattern classification are carried out simultaneously during the procedure of iterative updating of network parameters; thus, it can remove the complicated manual feature engineering. In this paper, we propose a novel deep learning methodology which can be used for spatial-frequency feature learning and classification of motor imagery EEG. Specifically, a multilayer CNN model is designed according to the spatial-frequency characteristics of MI EEG signals. An experimental study is carried out on two MI EEG datasets (BCI competition III dataset IVa and a self-collected right index finger MI dataset) to validate the effectiveness of our algorithm in comparison with several closely related competing methods. Superior classification performance indicates that our proposed method is a promising pattern recognition algorithm for MI-based BCI system.

Comparison of EEG Pattern Recognition of Motor Imagery for Finger Movement Classification

Comparison of EEG Pattern Recognition of Motor Imagery for Finger Movement Classification

Comparison of EEG Pattern Recognition of Motor Imagery for Finger Movement Classification

Comparison of EEG Pattern Recognition of Motor Imagery for Finger Movement Classification

Neural-signal electroencephalogram (EEG) methods to improve human-building interaction under different indoor air quality

In this study, neural-signal electroencephalogram (EEG) methods to improve human-building interaction under different indoor air quality conditions were investigated. Experiment was conducted to study correlations between EEG frequency bands and subjective perception as well as task performance. Machine learning-based EEG pattern recognition methods as feedback mechanisms were also investigated. Results showed that EEG theta band (4–8 Hz) correlated with subjective perceptions, and EEG alpha band (8–13 Hz) correlated with task performance. These EEG indices could be utilized as more objective metrics in addition to questionnaire and task-based metrics. For the machine learning-based EEG pattern recognition methods, the linear discriminant analysis (LDA) and support vector machine (SVM) classifiers can classify mental states under different indoor air quality conditions with high accuracy. In general, the EEG theta and alpha bands as more objective indices and the machine learning-based EEG pattern recognition methods as real-time feedback mechanisms have good potential to improve the human-building interaction.

A Novel Neural Network Approach to Creating a Brain–Computer Interface Based on the EEG Patterns of Voluntary Muscle Movements

A novel neural network approach to the real-time detection and classification of temporospatial EEG patterns accompanying performance of motor imagery (imaginary movements) based on a local approximation method using radial basis functions and an original algorithm for interpreting the time sequence of the responses of the neural network is proposed. This was used as the basis for creating and testing an asynchronous neural interface whose basic element was a classifier including a committee of five neural networks detecting EEG patterns accompanying four types of motor imagery. We present a comparative assessment of the EEG pattern recognition effectiveness for motor imagery using the method developed here and classical neural network methods, particularly radial basis networks, a multilayer perceptron, and the support vectors method. Experimental studies demonstrated a user learning effect with increases in recognition accuracy and EEG pattern classification, as well as speed of control.

Quantitative measures of EEG for prediction of outcome in cardiac arrest subjects treated with hypothermia: a literature review.

Cardiac arrest (CA) is the leading cause of death and disability in the United States. Early and accurate prediction of CA outcomecan help clinicians and families to make a better-informed decision for the patient's healthcare. Studies have shown that electroencephalography (EEG) may assist in early prognosis of CA outcome. However, visual EEG interpretation is subjective, labor-intensive, and requires interpretation by a medical expert, i.e., neurophysiologists. These limiting factors may hinder the applicability of such testing as the prognostic method in clinical settings. Automatic EEG pattern recognition using quantitative measures can make the EEG analysis more objective and less time consuming. It also allows to detect and display hidden patterns that may be useful for the prognosis over longer time periods of monitoring. Given these potential benefits, there have been an increasing interest over the last few years in the development and employment of EEG quantitative measures to predict CA outcome. This paper extensively reviews the definition and efficacy of various measures that have been employed for the prediction of outcome in CA subjects undergoing hypothermia (a neuroprotection method that has become a standard of care to improve the functional recovery of CA patients after resuscitation). The review details the State-of-the-Art and provides some perspectives on what seems to be promising for the early and accurate prognostication of CA outcome using the quantitative measures of EEG.

Human-building interaction under various indoor temperatures through neural-signal electroencephalogram (EEG) methods

In this study, potential of neural-signal electroencephalogram (EEG)-based methods for enhancing human-building interaction under various indoor temperatures were explored. Correlations between EEG and subjective perceptions/tasks performance were experimentally investigated. Machine learning-based EEG pattern recognition was further studied. Results showed that the EEG frontal asymmetrical activity related well to the subjective questionnaire and objective tasks performance, which can be used as a more objective metric to corroborate traditional subjective questionnaire-based methods and task-based methods. Machine learning-based EEG pattern recognition with linear discriminant analysis (LDA) classifiers can well classify the different mental states under different thermal conditions. Utilization of the EEG frontal asymmetrical activities and the machine learning-based EEG pattern recognition method as a feedback mechanism of occupants, which can be implemented on a routine basis, has a great potential to enhance the human-building interaction in a more objective and holistic way.

Index finger motor imagery EEG pattern recognition in BCI applications using dictionary cleaned sparse representation-based classification for healthy people.

Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interface (BCI) has shown its effectiveness for the control of rehabilitation devices designed for large body parts of the patients with neurologic impairments. In order to validate the feasibility of using EEG to decode the MI of a single index finger and constructing a BCI-enhanced finger rehabilitation system, we collected EEG data during right hand index finger MI and rest state for five healthy subjects and proposed a pattern recognition approach for classifying these two mental states. First, Fisher's linear discriminant criteria and power spectral density analysis were used to analyze the event-related desynchronization patterns. Second, both band power and approximate entropy were extracted as features. Third, aiming to eliminate the abnormal samples in the dictionary and improve the classification performance of the conventional sparse representation-based classification (SRC) method, we proposed a novel dictionary cleaned sparse representation-based classification (DCSRC) method for final classification. The experimental results show that the proposed DCSRC method gives better classification accuracies than SRC and an average classification accuracy of 81.32% is obtained for five subjects. Thus, it is demonstrated that single right hand index finger MI can be decoded from the sensorimotor rhythms, and the feature patterns of index finger MI and rest state can be well recognized for robotic exoskeleton initiation.

A spatial-frequency-temporal optimized feature sparse representation-based classification method for motor imagery EEG pattern recognition.

Effective feature extraction and classification methods are of great importance for motor imagery (MI)-based brain-computer interface (BCI) systems. The common spatial pattern (CSP) algorithm is a widely used feature extraction method for MI-based BCIs. In this work, we propose a novel spatial-frequency-temporal optimized feature sparse representation-based classification method. Optimal channels are selected based on relative entropy criteria. Significant CSP features on frequency-temporal domains are selected automatically to generate a column vector for sparse representation-based classification (SRC). We analyzed the performance of the new method on two public EEG datasets, namely BCI competition III dataset IVa which has five subjects and BCI competition IV dataset IIb which has nine subjects. Compared to the performance offered by the existing SRC method, the proposed method achieves average classification accuracy improvements of 21.568 and 14.38% for BCI competition III dataset IVa and BCI competition IV dataset IIb, respectively. Furthermore, our approach also shows better classification performance when compared to other competing methods for both datasets.

Designing an Interval Type-2 Fuzzy Logic System for Handling Uncertainty Effects in Brain–Computer Interface Classification of Motor Imagery Induced EEG Patterns

One of the urgent challenges in the automated analysis and interpretation of electrical brain activity is the effective handling of uncertainties associated with the complexity and variability of brain dynamics, reflected in the nonstationary nature of brain signals such as electroencephalogram (EEG). This poses a severe problem for existing approaches to the classification task within brain–computer interface (BCI) systems. Recently emerged type-2 fuzzy logic (T2FL) methodology has shown a remarkable potential in dealing with uncertain information given limited insight into the nature of the data generating mechanism. The objective of this work is thus to examine the applicability of T2FL approach to the problem of EEG pattern recognition. In particular, the focus is two-fold: i) the design methodology for the interval T2FL system (IT2FLS) that can robustly deal with inter-session as well as within-session manifestations of nonstationary spectral EEG correlates of motor imagery (MI), and ii) the comprehensive examination of the proposed fuzzy classifier in both off-line and on-line EEG classification case studies. The on-line evaluation of the IT2FLS-controlled real-time neurofeedback over multiple recording sessions holds special importance for EEG-based BCI technology. In addition, a retrospective comparative analysis accounting for other popular BCI classifiers such as linear discriminant analysis (LDA), kernel Fisher discriminant (KFD) and support vector machines (SVMs) as well as a conventional type-1 FLS (T1FLS), simulated off-line on the recorded EEGs, has demonstrated the enhanced potential of the proposed IT2FLS approach to robustly handle uncertainty effects in BCI classification.

Guest editorial for special issue on human-centered intelligent robots: issues and challenges

The twelve papers in this special issue aim to address a broad spectrum of topics ranging from human-centered intelligent robots acting as a servant, secretary, or companion to intelligent robotic functions. Included are innovative ideas and concepts, new discoveries, and novel applications and business models relevant to the field of human-centered intelligent robots. Topics covered include: modeling, intelligent control, EEG signal processing, pattern recognition and impedance control in unstructured environments are studied for human-centered intelligent robots.

Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN.

Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easier. This study deals with feature extraction and mental task pattern recognition on 2-D cursor control from EEG as offline analysis approach. The hemispherical power density changes are computed and compared on alpha-beta frequency bands with only mental imagination of cursor movements. First of all, power spectral density (PSD) features of EEG signals are extracted and high dimensional data reduced by principle component analysis (PCA) and independent component analysis (ICA) which are statistical algorithms. In the last stage, all features are classified with two types of support vector machine (SVM) which are linear and least squares (LS-SVM) and three different artificial neural network (ANN) structures which are learning vector quantization (LVQ), multilayer neural network (MLNN) and probabilistic neural network (PNN) and mental task patterns are successfully identified via k-fold cross validation technique.

Multi-channel EEG signal feature extraction and pattern recognition on horizontal mental imagination task of 1-D cursor movement for brain computer interface.

Brain computer interfaces (BCIs), based on multi-channel electroencephalogram (EEG) signal processing convert brain signal activities to machine control commands. It provides new communication way with a computer by extracting electroencephalographic activity. This paper, deals with feature extraction and classification of horizontal mental task pattern on 1-D cursor movement from EEG signals. The hemispherical power changes are computed and compared on alpha & beta frequencies and horizontal cursor control extracted with only mental imagination of cursor movements. In the first stage, features are extracted with the well-known average signal power or power difference (alpha and beta) method. Principal component analysis is used for reducing feature dimensions. All features are classified and the mental task patterns are recognized by three neural network classifiers which learning vector quantization, multilayer neural network and probabilistic neural network due to obtaining acceptable good results and using successfully in pattern recognition via k-fold cross validation technique.

Electronic system with memristive synapses for pattern recognition.

Memristive synapses, the most promising passive devices for synaptic interconnections in artificial neural networks, are the driving force behind recent research on hardware neural networks. Despite significant efforts to utilize memristive synapses, progress to date has only shown the possibility of building a neural network system that can classify simple image patterns. In this article, we report a high-density cross-point memristive synapse array with improved synaptic characteristics. The proposed PCMO-based memristive synapse exhibits the necessary gradual and symmetrical conductance changes, and has been successfully adapted to a neural network system. The system learns, and later recognizes, the human thought pattern corresponding to three vowels, i.e. /a /, /i /, and /u/, using electroencephalography signals generated while a subject imagines speaking vowels. Our successful demonstration of a neural network system for EEG pattern recognition is likely to intrigue many researchers and stimulate a new research direction.

EEG Pattern Recognition based on Frequency Analysis during Imaging and Gazing Tasks

EEG Pattern Recognition based on Frequency Analysis during Imaging and Gazing Tasks