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[Display omitted] In today's world, a large number of people suffer from motor impairment-related challenges. Rehabilitation is the main method used to overcome these difficulties. The goal of the paper is to develop a deep learning-based electroencephalogram (EEG) sensor-controlled assistive device for the rehabilitation of elbow and finger movements. We have introduced an innovative finger and elbow movement rehabilitation method using an EEG sensor. The EEG sensor's recorded EEG signals, attention values, and meditation values have been used for this purpose. This rehabilitation technique helps a person perform basic finger movement rehabilitation motions, such as finger extension and flexion. Also, basic elbow movement rehabilitation exercises, i.e., elbow extension and elbow flexion, can be performed by using this rehabilitation technique. In this research, an EEG sensor records the prefrontal lobe's EEG signals, attention value, and meditation value of a person while the person performs motor imagery. A deep learning-based CNN-TLSTM (Convolution Neural Network-tanh Long Short-Term Memory) model with attention mechanism has been designed for decoding the EEG sensor recorded data. The trained deep learning model decides the course of action of the rehabilitation device. The designed model achieves an accuracy of 99.6%. A working prototype model of the rehabilitation device has been developed, and the overall success rate of the model is found to be 98.66%. The novelty of the paper lies in i) designing an attention-based CNN-TLSTM model for motor imagery classification and ii) developing a low-cost EEG sensor-driven rehabilitation device for finger and elbow movement rehabilitation. [ABSTRACT FROM AUTHOR]

Epilepsy is one of the most paramount neurological diseases, affecting about 1% of the world's population. Seizure detection and classification are difficult tasks and are ongoing challenges in biomedical signal processing to enhance medical diagnosis. This paper presents and highlights the unique frequency and amplitude information found within multiple seizure types, including their morphologies, to aid the development of future seizure classification algorithms. Whilst many published works in the literature have reported on seizure detection using electroencephalogram (EEG), there has yet to be an exhaustive review detailing multi-seizure type classification using EEG. Therefore, this paper also includes a detailed review of multi-seizure type classification performance based on the Temple University Hospital Seizure Corpus (TUSZ) dataset for focal and generalised classification, and multi-seizure type classification. Deep learning techniques have a higher overall average performance for focal and generalised classification compared to machine learning techniques, whereas hybrid deep learning approaches have the highest overall average performance for multi-seizure type classification. Finally, this paper also highlights the limitations of the TUSZ dataset and suggests some future work, including the curation of a standardised training and testing dataset from the TUSZ that would allow a proper comparison of classification methods and spur advancement in the field. [ABSTRACT FROM AUTHOR]

Odor sensory evaluation is broadly applied in food, clothing, cosmetics, and other fields. Traditional artificial sensory evaluation has poor repeatability, and the machine olfaction represented by the electronic nose (E-nose) is difficult to reflect human feelings. Olfactory electroencephalogram (EEG) contains features associated with human olfactory preference, which has unique advantages in odor sensory evaluation. However, the difficulty of cross-subject olfactory EEG recognition dramatically limits its application. In this paper, a human–machine collaborative multimodal learning method is proposed for cross-subject olfactory preference recognition. Firstly, the olfactory EEG and E-nose multimodal data acquisition and preprocessing paradigms are established. Secondly, a complementary multimodal data mining strategy is proposed to effectively mine the individual and common features representing subjects' olfactory preferences from multimodal data. Finally, the cross-subject olfactory preference recognition is achieved in 24 subjects by fusing the extracted common and individual features, and the recognition effect is superior to the state-of-the-art recognition methods. Furthermore, the advantages of the proposed method in cross-subject olfactory preference recognition indicate its potential for practical odor evaluation applications. [ABSTRACT FROM AUTHOR]

Decoding brain activity from non-invasive motor imagery electroencephalograph (MI-EEG) has garnered significant attentions for brain-computer interface (BCI) and brain disorders. Notably, owing to the remarkable advances in feature representation, extracting and selecting discriminative features in EEG decoding has gained widespread popularity in recent years. However, many EEG studies suffer from limited sample sizes with low signal-to-noise ratio, making it difficult to effectively represent complementary features in a single view. To address this fundamental limitation, this paper proposes a novel method to S elective extract the M ulti- V iew T ime- F requency decomposed S patial (S-MVTFS) feature matrix, which employs spatial features from Euclidean and Riemannian spaces based on time–frequency decompositions. It selectively extracts discriminative features on manifold embedded space and classifies feature matrix through sparse support matrix machine. The proposed method has been systematically benchmarked on three BCI competition MI-EEG datasets, and its classification performance surpasses several state-of-the-art methods. Notably, the S-MVTFS method achieved average classification improvements of 0.45%, 2.28%, and 2.04% on BCI-III dataset 4a, BCI-IV dataset 1, and BCI-IV dataset 2a, respectively. Moreover, it effectively captures meaningful temporal varying and spatially coupled features with parameter insensitivity. Our method therefore provides a novel MI-EEG tailored feature representation for decoding brain activity. [ABSTRACT FROM AUTHOR]

Highlights • A method of detecting the top 20% of viewer attention to video segments is proposed. • This is the first study of detecting viewer attention during video viewing. • All subject-independent models unbiased to specific genres are evaluated. • The all-14-channel, single-channel, and selected multi-channel models are included. • The interval band ratio features are the most suitable for all the types of models. Abstract To manage voluminous viewed videos, which US adults watch at a rate of more than five hours per day on average, an automatic method of detecting highly attended video segments during video viewing is required to access them for fine-grained sharing and rewatching. Most electroencephalography (EEG)-based studies of user state analysis have addressed the recognition of attention-related states in a specific task condition, such as drowsiness during driving, attention during learning, and mental fatigue during task execution. In contrast to attention in a specific task condition, both inattention and normal attention are meaningless to viewers in terms of managing viewed videos, while detecting high attention paid to video segments would make a valuable contribution to an automatic management system of viewed videos based on viewer attention. To the best of our knowledge, this is the first EEG-based study of detecting viewer attention paid to video segments. This study describes how to collect video-induced EEG and attention data for video segments from viewers without bias to specific genres and how to construct a subject-independent detection model for the top 20% of viewer attention. The attention detection model using the proposed interval EEG features from 14 channels achieved the best average F 1 score of 39.79% with an average accuracy of 52.96%. Additionally, this paper proposes a channel-based feature selection method that considers both the performances of single-channel models and their physical locations for investigating the group of channels relevant to attention detection. The attention detection models using the interval EEG features from all four or some of the channels located in the fronto-central, parietal, temporal, and occipital lobes of the left hemisphere achieved the best F 1 score of 39.60% with an average accuracy of 48.70%. It is shown that these models achieve better performance than models using the features from all four or some of their symmetric channels in the right hemisphere and models using the features from six channels located in the anterior-frontal and frontal lobes of the left and right hemispheres. This paper shows the feasibility of subject-independent and genre-independent attention detection models using a wireless EEG headset with optimized channels; these models can be applied to an intelligent video management system based on viewer attention in real-world scenarios. [ABSTRACT FROM AUTHOR]

Driver's states in successive time slices are not independent, especially, fatigue is one of a cognitive state that is developing over time. Meanwhile, driver fatigue is also influenced by some corresponding contextual information at a certain time. In such case, classifying driving state at each time slice separately from it in before and after time slices obviously has less meaning. Therefore, a dynamic fatigue detection model based on Hidden Markov Model (HMM) is proposed in this paper. Driver fatigue can be estimated by this model in a probabilistic way using various physiological and contextual information. Electroencephalogram (EEG), Electromyogram (EMG), and respiration signals were simultaneously recorded by wearable sensors and sent to computer by Bluetooth during the real driving. From these physiological information, fatigue likelihood can be achieved using kernel distribution estimate at different time sections. Contextual information offered by specific environmental factors were used as prior of fatigue. As time proceeds, the posterior of fatigue can be gotten dynamically by this HMM-based fatigue recognition method. Based on the results of the method in this paper, it shows that it provides an effective way in detecting driver fatigue. [ABSTRACT FROM AUTHOR]

Sleep stage scoring is a challenging task. Most of existing sleep stage classification approaches rely on analysing electroencephalography (EEG) signals in time or frequency domain. A novel technique for EEG sleep stages classification is proposed in this paper. The statistical features and the similarities of complex networks are used to classify single channel EEG signals into six sleep stages. Firstly, each EEG segment of 30 s is divided into 75 sub-segments, and then different statistical features are extracted from each sub-segment. In this paper, feature extraction is important to reduce dimensionality of EEG data and the processing time in classification stage. Secondly, each vector of the extracted features, which represents one EEG segment, is transferred into a complex network. Thirdly, the similarity properties of the complex networks are extracted and classified into one of the six sleep stages using a k-means classifier. For further investigation, in the statistical features extraction phase two statistical features sets are tested and ranked based on the performance of the complex networks. To investigate the classification ability of complex networks combined with k-means, the extracted statistical features were also forwarded to a k-means and a support vector machine (SVM) for comparison. We also compare the proposed method with other existing methods in the literature. The experimental results show that the proposed method attains better classification results and a reasonable execution time compared with the SVM, k-means and the other existing methods. The research results in this paper indicate that the proposed method can assist neurologists and sleep specialists in diagnosing and monitoring sleep disorders. [ABSTRACT FROM AUTHOR]

Transfer learning for motor imagery-based brain–computer interfaces (MI-BCIs) struggles with inter-subject variability, hindering its generalization to new users. This paper proposes an advanced implicit transfer learning framework, META-EEG, designed to overcome the challenge arising from inter-subject variability. By incorporating gradient-based meta-learning with an intermittent freezing strategy, META-EEG ensures efficient feature representation learning, providing a robust zero-calibration solution. A comparative analysis reveals that META-EEG significantly outperforms all the baseline methods and competing methods on three different public datasets. Moreover, we demonstrate the efficiency of the proposed model through a neurophysiological and feature-representational analysis. With its robustness and superior performance on challenging datasets, META-EEG provides an effective solution for calibration-free MI-EEG classification, facilitating broader usability. • We propose a meta-learning-based zero-calibration EEG feature learning framework. • We construct meta-tasks robust to unseen subjects in meta-training. • We design intermittent freezing to learn class-relevant EEG features efficiently. • It shows effectiveness in motor imagery EEG classification for new stroke patients. • It provides an effective solution for zero-calibration BCI with broader usability. [ABSTRACT FROM AUTHOR]

Due to the influence of volume conduction, information redundancy exists between electrodes in electroencephalogram (EEG) signals, resulting in low spatial resolution. Furthermore, the current method of extracting EEG spatial features in Euclidean space cannot accurately depict the complex relationships among multiple electrodes, leading to poor decoding performance of EEG signals. A novel decoding framework for motor imagery EEG signals, known as the Spatial Filter Temporal Graph Convolutional Network (SF-TGCN), is proposed in this study. The spatial resolution of motor imagery EEG signals is enhanced by constructing the spatial filtering module using the Laplacian graph operator. Deep temporal features of the EEG signals are extracted using multiple layers of temporal convolutional modules, and the graph convolutional network module is employed to learn the topology of electrode connections, facilitating the decoding of motor imagery EEG signals. Superior performance is demonstrated by the proposed decoding model, achieving an average accuracy of 80.82% on the BCI IV 2a public dataset and 87.98% on a private dataset, as compared to other state-of-the-art decoding approaches. • We proposed a novel decoding framework for motor imagery EEG signals. • Our spatial filtering module enhances the spatial resolution of EEG signals. • We utilize graph convolutional modules to better learn the spatial features of EEG signals. • Based on the decoding model proposed in this paper, we design an online BCI intelligent wheelchair control system. [ABSTRACT FROM AUTHOR]

To identify epilepsy, Electroencephalography (EEG) is an important and common tool used to study the electrical activity of the human brain. The machine learning-based classifier is utilized to detect the seizure by manually extracting the features from the EEG signals in previous works. Though, these effective benefits have been proved already with automatic feature extraction, they are unable to achieve the classification of multiple classes. Meanwhile, real-time epileptic seizure detection is unable to keep the capacity because the identifiable EGG is too long. Hence, this paper is decided to develop an enhanced deep learning architecture with EEG signal for performing automatic epileptic seizure detection. The EEG signal is collected from the standard datasets. The Fourier-Bessel Series Expansion-Based Empirical Wavelet Transform (FBSE-EWT) method is used to decompose the signal from the gathered data. In feature extraction, the decomposed signals are used and while extracting the signal features, the autoencoder is involved in that process. To reduce the computational overload, the relief-F feature ranking method is used for choosing the important signal features. The Long Short Term Memory (LSTM) and Multi-Scale Atrous-based Deep Convolutional Neural Networks (MSA-DCNN) are used that is named as Hybrid Deep Scheme (HDS) to detect epileptic seizures with the top-ranked features for epileptic seizure classification. The Black Widow Optimization (BWO) and the Spider Monkey Optimization (SMO) are combined to generate Adaptive Spider Monkey Black Widow Optimization (ASMBWO) that is employed to perform the parameter tuning into a classification technique. Using different measures, the experimental analysis is done between the conventional epileptic seizure detection and the proposed model to establish an enhanced performance of the proposed model. [ABSTRACT FROM AUTHOR]

Low cost electroencephalography (EEG) headset devices for brain data capturing are fast becoming a key instrument on Brain Computer Interface (BCI) applications. In spite of being a controller device initially developed for gaming, the research community has adopted them as a key element to gather EEG data. However, there have been little discussion about their performance when being compared with professional and research EEG headsets. This paper provides an assessment of one of these devices, the Emotiv EPOC, on a motor imagery problem. As a benchmark, the data and results presented for the Data Set V of the BCI Competition III have been used, which were recorded with a professional Biosemi Active 2 EEG headset. From the perspective of a final working application, it is shown that the performance of this headset is comparable to that found in professional devices when using the same number of sensors and sensor positions for a three status motor imagery cognitive process. This finding implies an increase on the number of EEG headsets the researchers and manufacturers of BCI systems applied to motor imagery problems can integrate and a reduction of their cost. As part of this paper the Emotiv EPOC recorded raw and pre-processed datasets are published to allow further improvements and comparisons. [ABSTRACT FROM AUTHOR]

Autism Spectrum disorder (ASD) is a neurological disorder in which a person suffers from lifetime issues like social communication and interaction with other individuals. This disorder starts in childhood and leads to adulthood. So, a person's entire life is fully affected by this disorder. An early diagnosis is crucial to reduce the ASD's symptoms and also to enhance the ASD patient's life. The manual screening takes more time and tedious process. Hence, Electroencephalogram (EEG) signals are utilized for the brain's electrical activity recording process. The EEG signals are time-varying and non-stationary signals and also various methods are utilized to extract features using the EEG signals for the classification process. In this paper, we propose a novel hybrid ensemble model which is the ensemble of ResNet101 and a Bidirectional Gated recurrent unit (Bi-GRU) with a Weighted Average Ensemble (WAE). The preprocessing is initially carried out to remove undesirable aspects from the input signal, such as noise, computational burden, etc. A convolutional neural network-based ResNet model is utilized for the classification and detection of input data. Bi-GRU neural network learns data from two different sources such as forward and backward to provide extremely precise predictions. We integrate ResNet and Bidirectional Gated recurrent unit (Bi-GRU) to produce accurate classification results and this technique is optimized by using the Chaotic Henry Gas Solubility Optimization (CHGSO) algorithm. To enhance the efficiency of the proposed method we compare the four underlying techniques such as the Support vector machine (SVM), K-Nearest Neighbor (KNN), Modified Grasshopper Optimization Algorithm- Random forest (MGOA-RF), and Deep Neural Network (DNN), and the hybrid model is evaluated with dataset EEG microstates dataset. Accuracy, precision, sensitivity, specificity, F1-score, and MCC are utilized for assessing the classification performance more accurately. Our Hybrid ensemble model attains Sensitivity of 98%, 99% higher Specificity, 98% F1-Score, MCC of 99%, Accuracy of 98%, and Precision of 99%. [ABSTRACT FROM AUTHOR]

There is growing evidence that an increasing number of people suffer from mental illness, which seriously affects their quality of life. The study of electroencephalography (EEG) is becoming crucial for understanding the root causes of mental illnesses and preventing them proactively. However, most of the existing approaches are unable to comprehensively investigate the fine-grained EEG data of each channel and the correlations between the signals of each subject. This makes it difficult to establish associations between EEG and mental illnesses. To address this issue, this paper proposes a new method called GCNs–FSMI that combines fine-grained signals and graph mutual information maximization for EEG recognition of mental illness patients. This method integrates fine-grained EEG signals, graph mutual information maximization technology, and pre-trained graph convolutional neural networks (GCN) to explore high-level interactions of multi-channel EEG data between subjects, leading to more discriminative EEG feature representation. This is achieved by filtering the EEG signals on each channel into six frequency bands, calculating the average power of each frequency band, and combining them as fine-grained features of the nodes. Then, we splice the multi-channel EEG signals of a sample into a signal series and construct a network based on the Pearson correlation coefficient to represent the association between channels. After that, we perform pre-training and fine-tuning processes to further improve the EEG feature representations. We pre-train a three-layer GCN model, fix the first two layers of the model, and fine-tune the output layer (third layer) of the model through contrastive learning with graph mutual information maximization. Finally, we conduct several experiments to test the effectiveness of our GCNs–FSMI model. Experimental results on two real-world datasets demonstrate that the features learned by GCNs–FSMI improve the performance of downstream classifiers by 6% and 9%. Furthermore, our method outperformed state-of-the-art methods in classification accuracy. To the best of our knowledge, the GCNs–FSMI is the first EEG recognition method for patients with mental illness that uses fine-grained signals and graph mutual information maximization. Our method provides a new approach to diagnosing mental illness and processing multi-channel biological neural signal processing. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we propose a novel recognition algorithm for the discrimination of epilepsy based on electroencephalogram (EEG) signals. We validate the algorithm on a benchmark dataset in order to compare the algorithm with other algorithms in the literature. More specifically, features were extracted from the bilinear time–frequency distributions (TFD) of the EEG signal. A one-against-one decomposition is used to break the multi-class problem into binary subproblems solvable with a support vector machine (SVM). The decomposition permitted binary subproblem-dependent feature libraries to be constructed from biologically inspired features derived from conditional moments calculated from EEG TFD. This results in a flexible, class-dependent feature selection based on a forward selection wrapper representing a departure from prior work which tends to utilize the same set of features to delineate all classes. We investigated the sensitivity of the classification accuracy to changes in the proportion of data used to train the algorithm. It was found that the distribution of classification accuracies was statistically similar over a range of proportions of data used to train the algorithm. This served to validate our algorithm in a statistical sense and represents a significant departure from literature, which tends to report only the best result for a given classification algorithm. To the best of our knowledge, the newly introduced algorithm is able to outperform the best reported accuracy in literature for the problem considered in this paper. [Copyright &y& Elsevier]

Abstract: This paper describes a brain–computer interface (BCI) based on electroencephalography (EEG) that has been developed to assist disabled people. The BCI uses the evoked potentials paradigm (through P300 and N2PC waves detection), registering the EEG signals with 16 electrodes over the scalp. Three applications have been developed using this BCI paradigm. The first application is an Internet browser that allows to access to Internet and to control a computer. The second application allows controlling a robotic arm in order to manipulate objects. The third application is a basic communication tool that allows severe disabled people to interact with other people using basic commands related to emotions and needs. All the applications are composed by visual interfaces that show different options related to the application. These options are pseudo-randomly flickering in a screen. In order to select a specific command, the user must focus on the desired option. The BCI is able to obtain the desired option by detecting the P300 and N2PC waves produced as an automatic response of the brain to attended visual stimuli and finally classifying these signals. Different experiments with volunteers have been carried out in order to validate the applications. The experimental results obtained as well as the improvement achieved by using both types of evoked potentials are shown in the paper. [Copyright &y& Elsevier]

Highlights • Achieve feature extraction from both spatial and temporal domains of EEG signals. • The proposed network has accuracy up to 99.58% when using 16 EEG channels. • Reduce number of channels of the EEG systems while maintaining high performance. Abstract Electroencephalographic (EEG) signals have been widely used in medical applications, yet the use of EEG signals as user identification systems for healthcare and Internet of Things (IoT) systems has only gained interests in the last few years. The advantages of EEG-based user identification systems lie in its dynamic property and uniqueness among different individuals. However, it is for this reason that manually designed features are not always adapted to the needs. Therefore, a novel approach based on 1D Convolutional Long Short-term Memory Neural Network (1D-Convolutional LSTM) for EEG-based user identification system is proposed in this paper. The performance of the proposed approach was validated with a public database consists of EEG data of 109 subjects. The experimental results showed that the proposed network has a very high averaged accuracy of 99.58%, when using only 16 channels of EEG signals, which outperforms the state-of-the-art EEG-based user identification methods. The combined use of CNNs and LSTMs in the proposed 1D-Convolutional LSTM can greatly improve the accuracy of user identification systems by utilizing the spatiotemporal features of the EEG signals with LSTM, and lowering cost of the systems by reducing the number of EEG electrodes used in the systems. [ABSTRACT FROM AUTHOR]

Background: Classification and localization of focal epileptic seizures provide a proper diagnostic procedure for epilepsy patients. Visual identification of seizure activity from long-term electroencephalography (EEG) is tedious, time-consuming and leads to human error. Therefore, there is a need for an automated classification system. Methods: In this paper, we introduce a tool called CADFES: computerized automated detection of focal epileptic seizures. For the study, total 41.66 hours of EEG data from the Bern-Barcelona database was used. Set of 28 features were extracted from time, frequency, and statistical domain and significant features were selected using neighborhood component analysis (NCA). In NCA, optimization of regularization parameter ensured better classification accuracy (less classification loss) with seven features. The performance of the algorithm was assessed using support vector machine (SVM), K-nearest neighbor (K-NN), random forest and adaptive boosting (AdaBoost) classifiers. Results: Experimental results revealed sensitivity, specificity, accuracy, positive predictive rate, negative predictive rate, and area under the curve of 97.6%, 94.4%, 96.1%, 92.9%, 98.8% and 0.96 respectively using the SVM classifier. Finally, MATLAB based software tool referred to as CADFES was introduced for automated classification of focal and non-focal seizures. Comparison results ensure that proposed study is superior to existing methods. Hence, it is expected to perform better at the hospitals for automated classification of focal epileptic seizures in real-time. [ABSTRACT FROM AUTHOR]

Abstract: An integrated view of the automated diagnostic systems combined with spectral analysis techniques in the classification of electroencephalogram (EEG) signals is presented. The paper includes illustrative and detailed information about implementation of automated diagnostic systems and feature extraction/selection from the EEG signals. The major objective of the paper is to be a guide for the readers, who want to develop an automated diagnostic system for classification of the EEG signals. Toward achieving this objective, this paper presents the techniques which should be considered in developing automated diagnostic systems. The author suggests that the content of the paper will assist to the people in gaining a better understanding of the techniques in the classification of the EEG signals. [Copyright &y& Elsevier]

Abstract: Over the last few decades pattern classification has been one of the most challenging area of research. In the present-age pattern classification problems, the support vector machines (SVMs) have been extensively adopted as machine learning tools. SVM achieves higher generalization performance, as it utilizes an induction principle called structural risk minimization (SRM) principle. The SRM principle seeks to minimize the upper bound of the generalization error consisting of the sum of the training error and a confidence interval. SVMs are basically designed for binary classification problems and employs supervised learning to find the optimal separating hyperplane between the two classes of data. The main objective of this paper is to introduce a most promising pattern recognition technique called cross-correlation aided SVM based classifier. The idea of using cross-correlation for feature extraction is relatively new in the domain of pattern recognition. In this paper, the proposed technique has been utilized for binary classification of EEG signals. The binary classifiers employ suitable features extracted from crosscorrelograms of EEG signals. These cross-correlation aided SVM classifiers have been employed for some benchmark EEG signals and the proposed method could achieve classification accuracy as high as 95.96% compared to a recently proposed method where the reported accuracy was 94.5%. [Copyright &y& Elsevier]

This paper investigates the utility of EEG signals in suicidology, particularly for detection of suicidal ideation through data analysis of EEG signals elicit by reading of text notes, which may indicate genuine suicide attempt or a hoax suicide note. The role of emotion, attention, and memory in detection of suicidal ideation through perusal is studied. Also, the discriminatory role of alpha waves and beta waves is studied. Our study provides an important and interesting conclusion that prior experience or conditioned training in detection of suicide notes is not beneficial in discriminating between genuine and hoax suicide notes and that the brain signals are more discriminatory in participants with no prior experience or conditioning. Quantitatively, our results indicate that the beta waves in EEG channels related to memory can provide classification accuracy of more than 70%. [ABSTRACT FROM AUTHOR]

This paper reports the results of an experiment to identify EEG patterns specific to different levels of player involvement when playing a video game. To obtain unbiased results, we based our patterns on both raw EEG data and expert knowledge. We used a three-step procedure to identify patterns. First, we looked for clusters in the reduced feature space extracted from EEG data. Next, we assigned experts’ interpretations to the clusters. Finally, we analysed relations between features used to form the clusters and the class labels provided by experts. The most challenging part of the procedure was feature selection simultaneous with unsupervised classification. To accomplish this task, we developed a new approach for simultaneous feature selection and clustering based on modified GAAM (genetic algorithm with aggressive mutation). When the cGAAM algorithm was applied to EEG data, it returned the feature subsets that (a) were highly consistent across subjects and (b) provided 50% more compact clusters than clusters built over the feature subsets returned by a forward selection search strategy. The main cognitive outcome of EEG signal analysis was a set of patterns differentiating players’ involvement in a game. Conclusions included: 1. For a majority of subjects the most discriminative features were activity in the theta band in the left and right frontal areas, and activity in the delta band in the left frontal area; 2. All three features significantly differentiated between low and high, or medium and high engagement; 3. All subjects showed positive correlations between selected feature values and levels of engagement. [ABSTRACT FROM AUTHOR]

There is currently no standard or widely accepted subset of features to effectively classify different emotions based on electroencephalogram (EEG) signals. While combining all possible EEG features may improve the classification performance, it can lead to high dimensionality and worse performance due to redundancy and inefficiency. To solve the high-dimensionality problem, this paper proposes a new framework to automatically search for the optimal subset of EEG features using evolutionary computation (EC) algorithms. The proposed framework has been extensively evaluated using two public datasets (MAHNOB, DEAP) and a new dataset acquired with a mobile EEG sensor. The results confirm that EC algorithms can effectively support feature selection to identify the best EEG features and the best channels to maximize performance over a four-quadrant emotion classification problem. These findings are significant for informing future development of EEG-based emotion classification because low-cost mobile EEG sensors with fewer electrodes are becoming popular for many new applications. [ABSTRACT FROM AUTHOR]

Background Epilepsy is a brain disorder that is mainly diagnosed by neurologists based on electroencephalogram (EEG) recordings. Epileptic EEG signals are recorded as multichannel signals. A reliable technique for analysing multi-channel EEG signals is in urgent demand for the treatment and diagnosis of patients who have epilepsy and other brain disorders. Method In this paper, each single EEG channel is partitioned into four segments, with each segment is further divided into small clusters. A set of statistical features are extracted from each cluster. As a result, a vector of all the features from each EEG single channel is obtained. The resulting features vector is then mapped into an undirected weighted network. The modularity of the networks is found to be the best to detect epileptic seizures in EEG signals. Other local and global network features, including clustering coefficients, average degree and closeness centrality, are also extracted and studied. All the network attributes are ranked based on their potential to detect abnormalities in EEG signals. Results Eight pairs of combinations of EEG signals are classified by the proposed method using four well known classifiers: a least support vector machine, k -means, Naïve Bayes, and K -nearest. The proposed method achieved an average of 98%, 96.5%, 99%, rand 0.012, respectively, for its accuracy, sensitivity, specificity and the false positive rate. Comparisons were made using several existing epileptic seizures detection methods using the same datasets. The obtained results showed that the proposed method was efficient in detecting epileptic seizures in EEG signals. [ABSTRACT FROM AUTHOR]

One of the challenges in developing a Brain Computer Interface (BCI) is dealing with the high dimensionality of the data when extracting features from EEG signals. Different feature selection algorithms have been proposed to overcome this problem but most of them involve complex transformed features, which require high computation and also result in increasing size of the feature set. In this paper, we present a new hybrid method to select features that involves a Differential Evolution (DE) optimization algorithm for searching the feature space to generate the optimal feature subset, with performance evaluated by a classifier. We provide a comprehensive study of the significance of evolutionary algorithm in selecting the best features for EEG signals. The BCI competition III, dataset IVa has been used to evaluate the method. Experimental results demonstrate that the proposed method performs well with Support Vector Machine (SVM) classifier, with an average classification accuracy of above 95% with a minimum of just 10 features. We also present a comparison of Differential Evolution (DE) with other evolutionary algorithms, and the results show the superiority of DE which implies that, with the selection of a good searching algorithm, a simple Common Spatial Pattern filter features can produce good results. [ABSTRACT FROM AUTHOR]

Background This paper presents optimized configuration of multilayer perceptron neural network (MLP-NN) as a pattern classifier for recognition of intracranial electroencephalogram (iEEG) epileptic seizures. Though qualitative analysis of intracranial recordings from the patients involves cumbersome procedure, it provides significant neuronal activities of the brain that is essential for clinical decision making. Methods This study considered three epileptic seizures stages, namely, pre-ictal (set C), post-ictal (set D) and epileptic (set E) iEEG from the University of Bonn, Germany database. Four entropies, Shannon, log energy, spectral and Renyi entropies were considered as features to evaluate MLP-NN network. Four classification tasks with the dataset were formed, namely CE, DE, CDE, and CD. In order to identify the optimal configuration of MLP-NN for classification, network parameters such as input-hidden layer activation/transfer functions, hidden-output layer activation/transfer functions, network training/learning functions, the number of hidden neurons were considered and the optimality was arrived based on the mean square error (MSE), classification accuracy (CA). The efficiency of the entropy features was exploited by the parameters, mutual range of coefficient ( γ ), p-value, and z-score, which showed the band discrimination for various classification tasks. Results Simulations results showed that the tan sigmoid, pure linear were found to be optimal input-hidden, hidden-output transfer function and Levenberg-Marquardt learning algorithm as optimal training function for all the four classification tasks. It was inferred from the proposed study that the CA indirectly varies with γ value, p value, and MSE, and directly varies with z-score. From the experimental study, the best CA of 97.68%, 94.56%, 84.58%, and 57.8% was obtained for case CE, DE, CDE, and CD respectively. It can be concluded that proposed features with optimally configured MLP-NN found to be helpful for real-time iEEG classification. [ABSTRACT FROM AUTHOR]

Sleep stage scoring is generally determined in a polysomnographic (PSG) study where technologists use electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals to determine the sleep stages. Such a process is time consuming and labor intensive. To reduce the workload and to improve the sleep stage scoring performance of sleep experts, this paper introduces an intelligent rapid eye movement (REM) sleep detection method that requires only a single EEG channel. The proposed approach distinguishes itself from previous automatic sleep staging methods by introducing two sets of auxiliary features to help resolve the difficulties caused by interpersonal EEG signal differences. In addition to adopting conventional time and frequency domain features, two empirical rules are introduced to enhance REM detection performance based on sleep being a continuous process. The approach was tested with 779,661 epochs obtained from 947 overnight PSG studies. The REM sleep detection results show a kappa coefficient at 0.752, an accuracy level of 0.930, a sensitivity score of 0.814, and a positive predictive value of 0.775. The results also show that the performance of the approach varies with the ratio of REM sleep and the severity of sleep apnea of the subjects. The experimental results also show that it is possible to improve the performance of an automatic sleep staging method by tailoring it to subgroups of persons that have similar sleep architecture and clinical characteristics. [ABSTRACT FROM AUTHOR]

Detecting seizure using brain neuroactivations recorded by intracranial electroencephalogram (iEEG) has been widely used for monitoring, diagnosing, and closed-loop therapy of epileptic patients, however, computational efficiency gains are needed if state-of-the-art methods are to be implemented in implanted devices. We present a novel method for automatic seizure detection based on iEEG data that outperforms current state-of-the-art seizure detection methods in terms of computational efficiency while maintaining the accuracy. The proposed algorithm incorporates an automatic channel selection (ACS) engine as a pre-processing stage to the seizure detection procedure. The ACS engine consists of supervised classifiers which aim to find iEEG channels which contribute the most to a seizure. Seizure detection stage involves feature extraction and classification. Feature extraction is performed in both frequency and time domains where spectral power and correlation between channel pairs are calculated. Random Forest is used in classification of interictal, ictal and early ictal periods of iEEG signals. Seizure detection in this paper is retrospective and patient-specific. iEEG data is accessed via Kaggle, provided by International Epilepsy Electro-physiology Portal. The dataset includes a training set of 6.5 h of interictal data and 41 min in ictal data and a test set of 9.14 h. Compared to the state-of-the-art on the same dataset, we achieve 2 times faster in run-time seizure detection. The proposed model is able to detect a seizure onset at 89.40% sensitivity and 89.24% specificity with a mean detection delay of 2.63 s for the test set. The area under the ROC curve ( AUC ) is 96.94%, that is comparable to the current state-of-the-art with AUC of 96.29%. [ABSTRACT FROM AUTHOR]

This work presents a novel approach for automatic epilepsy seizure detection based on EEG analysis that exploits the underlying non-linear nature of EEG data. In this paper, two main contributions are presented and validated: the use of non-linear classifiers through the so-called kernel trick and the proposal of a Bag-of-Words model for extracting a non-linear feature representation of the input data in an unsupervised manner. The performance of the resulting system is validated with public datasets, previously processed to remove artifacts or external disturbances, but also with private datasets recorded under realistic and non-ideal operating conditions. The use of public datasets caters for comparison purposes whereas the private one shows the performance of the system under realistic circumstances of noise, artifacts, and signals of different amplitudes. Moreover, the proposed solution has been compared to state-of-the-art works not only for pre-processed and public datasets but also with the private datasets. The mean F1-measure shows a 10% improvement over the second-best ranked method including cross-dataset experiments. The obtained results prove the robustness of the proposed solution to more realistic and variable conditions. [ABSTRACT FROM AUTHOR]

It is well established that multiple EEG channels are required for various brain functionality studies, including classification tasks. Yet, due to the curse of dimensionality problem, the analysis of multiple channels may not lead to the desired performance. Accordingly, a number of static channel selection algorithms have been proposed to identify the most relevant subset of channels. However, static methods select a fixed subset of channels that is unchanged when processing new data, and hence cannot adapt to changes in data. In this paper, we propose a novel algorithm that utilizes the dynamic classification behaviour of channels in selecting the channel that is most relevant for each time segment of the signal. The main idea is to identify for each time segment of every channel of the signal (testing sample) the closest training samples. These training samples are used to estimate the local accuracy of each channel. The best performing channel for that time segment will then be identified as the relevant one. Results obtained using EEG data of a four-class alertness state classification problem, with two different feature sets, reveal that the proposed approach is capable of achieving competitive performance compared to a traditional static channel selection based method. More importantly, the evaluation of the selected channels reveals that our approach is able to select relevant channels for each of the four alertness states. The proposed algorithm is expected to make a valuable contribution to the field of multichannel biomedical signal classification. [ABSTRACT FROM AUTHOR]

The P 300 is an endogenous event-related potential (ERP) that is naturally elicited by rare and significant stimuli, arisen from the frontal, temporal and occipital lobe of the brain, although is usually measured in the parietal lobe. P 300 signals are increasingly used in brain-computer interfaces (BCI) because the users of ERP-based BCIs need no special training. In order to detect the P 300 signal, most studies in the field have been focused on a supervised approach, dealing with over-fitting filters and the need for later validation. In this paper we start bridging this gap by modeling an unsupervised classifier of the P 300 presence based on a weighted score. This is carried out through the use of matched filters that weight events that are likely to represent the P 300 wave. The optimal weights are determined through a study of the data’s features. The combination of different artifact cancelation methods and the P 300 extraction techniques provides a marked, statistically significant, improvement in accuracy at the level of the top-performing algorithms for a supervised approach presented in the literature to date. This innovation brings a notable impact in ERP-based communicators, appointing to the development of a faster and more reliable BCI technology. [ABSTRACT FROM AUTHOR]

ElectroEncephaloGram (EEG) gives information about the electrical characteristics of the brain. EEG can be used for various applications, such as diagnosis of diseases, neuroscience and Brain Computer Interface (BCI). Several artefacts sources can disturb the brain signals in EEG measurements. The signals caused by eye movements are the most important sources of artefacts that must be removed in order to obtain a clean EEG signal. During the removal of Ocular Artefacts (OAs), the preserve of the original EEG signal is one of the most important points to be taken into account. An ElectroOculoGram (EOG) reference signal is needed in order to remove OAs in some methods. However, long-term EOG measurements can disturb a subject. In this paper, a novel robust method is proposed in order to remove OAs automatically from EEG without EOG reference signal by combining Outlier Detection and Independent Component Analysis (OD-ICA). The OD-ICA method searches OA patterns in all components instead of a single component. Moreover, OD-ICA removes only OA patterns and preserves meaningful EEG signal. In this method, user intervention is not needed. These advantages make the method robust. The OD-ICA is tested on two real datasets. Relative Error (RE), Correlation Coefficient (CorrCoeff) and percentage of finding OA pattern are used for the performance test. Furthermore, three different methods are used as Outlier Detection (OD) methods. These are the Chauvenet Criterion, the Peirce's Criterion and the Adjusted Box Plot. The performance analysis is made between our proposed method and the method of zeroing the component with artefact. The experiment results show that the proposed OD-ICA method effectively removes OAs from EEG signals and is also successful in preserving the meaningful EEG signals during the removal of OAs. [ABSTRACT FROM AUTHOR]

Motor-imagery tasks generate event related synchronization and de-synchronization in certain subject-specific frequency ranges of the subject’s ElectroEncephaloGraphy (EEG) signals. The selection of frequency ranges for each subject is important for obtaining better classification accuracy of motor-imagery based Brain Computer Interface (BCI). Further, the spatial filters extracted corresponding to the selected spectral ranges also influence the classification accuracy. In this paper, a subject-specific spatio-spectral filter selection approach using a cognitive fuzzy inference system for classification of the motor-imagery tasks in a two step approach is presented. The cognitive fuzzy inference system (CFIS) employs an evolving interval type-2 system to classify the non-stationary features. The classifier employs a meta-cognitive sequential algorithm to determine both the structure and parameters of the CFIS. In the first step, the CFIS classifier is used to find the desired spectral filters by eliminating those frequency bands that do not affect the classification performance. In the second step, CFIS is used to eliminate those spatial filters which do not affect the performance. The performance of CFIS based spatio-spectral scheme has been evaluated using two publicly available BCI competition data sets and compared with other existing algorithms like FBCSP, DCSP and BSSFO. The results indicate that the proposed approach outperforms the CSP method by approximately 15–18% and other algorithms like FBCSP, DCSP by 8–10%. Compared to a recently proposed algorithm BSSFO, it achieves an improvement of 2%, but is simpler in comparison to BSSFO. The main impact of the work is its ability to handle non-stationarity using interval type-2 sets and provide good classification performance. In general, the proposed CFIS algorithm can be applied in the field of expert and intelligent systems where it is necessary to deal with non-stationary signals. [ABSTRACT FROM AUTHOR]

The automatic and online detection of auditory attention in multi-talker scenarios (e.g., cocktail party paradigm) is a current topic in electroencephalography (EEG)-based brain-computer interfaces (BCIs). Recent works have demonstrated a way to make it possible by means of a model based on an m-ary phase shift keying (m-PSK) detector. However, this attention detection model lacks of relevant information such as the non-stationary nature of EEG signals, the neuro-plasticity/habituation effects or the nonlinearities of the attention. In this paper we propose an enriched version of the attention detection model constituted by an automatic adaptive m-PSK detector implemented on fuzzy logic. In it, the relevant information mentioned before is modeled as two inputs that feed the fuzzy-based attention detection model. The output provides the detection. Our enriched model outperformed the results of previous works in terms of mean information transfer rate (ITR) (4-PSK: 5.41 bpm; 6-PSK: 6.03 bpm) and accuracy (4-PSK: 0.54; 6-PSK: 0.39) after only 4.63 (4-PSK) and 2.93 (6-PSK) seconds of processing. The proposed model for the automatic detection of auditory attention can have relevant impact on several areas such as education, public transport, jobs, industry, attention disorders, ubiquitous systems, sports and art. [ABSTRACT FROM AUTHOR]

Recognition of emotions based on electroencephalography (EEG) has become one of the most emerging topics for healthcare, education system, knowledge sharing, gaming, and many other fields in the last few decades. This paper proposes three non-linear features (Higuchi fractal dimension, sample entropy, and permutation entropy) and eight ensemble learning approaches to predict six basic emotions (hope, interest, excitement, shame, fear, and sad). To increase the recognition rate of each classifier, we utilized a randomized grid search technique for tunning the hyperparameter of each algorithm. Moreover, the impact of electrodes on each emotion was observed using the recursive feature elimination (RFE) method. In addition, the synthetic minority oversampling technique (SMOTE) is used to handle the imbalanced sample distribution of each emotion. Then, we have conducted all the experiments on DEAP and AMIGOS datasets and calculated the computation time and accuracy to assess each algorithm's performance. Besides, we observe statistical significance to compare the algorithm's performance. From the experimental result, we achieved the highest average accuracy, 89.38% and 94.62%, using Higuchi fractal dimension on DEAP and AMIGOS datasets, respectively. We also observed that the Higuchi fractal dimension outperforms the sample entropy and permutation entropy in terms of accuracy. Our proposed method increased the average recognition rate by 8.22% and 1.77%, respectively, compared with existing approaches working on the same dataset. Along with accuracy, precision, recall, F-measure, and AUC-ROC have been evaluated using the same experimental setting on DEAP and AMIGOS datasets. The results of each classifier have been shown in the table and graph. Moreover, the proposed method outperforms existing approaches dealing with shallow machine learning techniques. [ABSTRACT FROM AUTHOR]

The recent advances in developing assistive devices have attracted researchers to use visual imagery (VI) mental tasks as a control paradigm to design brain–computer interfaces that can produce a large number of control signals. Consequently, this can facilitate the design of control mechanisms that allow locked-in individuals to interact with the surrounding world. This paper presents a two-phase approach for decoding visually imagined digits and letters using electroencephalography (EEG) signals. The first phase employs the Choi–Williams time–frequency distribution (CWD) to construct a joint time, frequency, and spatial (TFS) representation of the EEG signals. The constructed joint TFS representation characterizes the variations in the energy encapsulated within the EEG signals over the TFS domains. The second phase presents a novel deep learning (DL) framework to automatically extract features from the constructed joint TFS representation of the EEG signals and decode the imagined digits and letters. The performance of our approach is assessed using an EEG dataset that was acquired for 16 healthy participants while imagining decimal digits and uppercase English letters. Our approach achieved an average ± standard deviation accuracy of 95. 47 ± 2. 3 % , which is significantly outperforming the accuracies obtained when the CWD is replaced with two alternative time–frequency analysis techniques, the accuracies obtained using four pre-trained DL models, and the accuracies obtained using CWD-based handcrafted features that are classified using four conventional classifiers. Moreover, the results of our proposed approach outperform those reported by several previous studies with regard to the accuracy and number of classes. • An EEG-based two-phase approach is proposed for decoding visually imagined objects. • The approach focuses on decoding visually imagined digits and letters. • The first phase builds a new time–frequency–spatial representation of EEG signals. • The second phase presents a new deep learning framework to decode imagined objects. • The results show that the approach enables accurate decoding of imagined objects. [ABSTRACT FROM AUTHOR]

Advances in materials engineering, electronic circuits, sensors, signal processing and classification techniques have allowed computational systems to interpret biological quantities, recognizing physiological conditions. The next scientific challenge is to turn those technologies portable, wearable or even implantable, above all, being energy efficient. A prospective application for the next generation of portable electroencephalogram (EEG) signal processing systems is hazard prevention in attention-demanding activities. EEG keeps closest connection to the preoptic area where sleep is originated. In this paper, a methodology for assessing alertness level based on a single EEG channel (Pz–Oz) is proposed, allowing the reduction of the required hardware and the computational time of the algorithms, besides being more portable than multi-channel based ones. Two new spectral power-based indices (i) γ / δ and (ii) ( γ + β )/( δ + α ) are computed from EEG rhythms through the normalized Haar discrete wavelet packet transform (WPT). The Haar WPT allows precisely resolving the brain rhythms into packets whilst demanding a relatively low computational cost. The effectiveness of the proposed indices in drowsiness detection is evaluated by comparison with five indices originally proposed for multi-channel processing. Statistical Wilcoxon signed rank test is applied to evaluate the performance of the entire set of indices, evidencing the significant changes in the alert-drowsy transitions of 20 subjects of a public database. The proposed indices (ii) and (i) presented the most and second more significant p -Values ( p < 0.001 and p = 0.001), respectively. [ABSTRACT FROM AUTHOR]

Current emotion recognition computational techniques have been successful on associating the emotional changes with the EEG signals, and so they can be identified and classified from EEG signals if appropriate stimuli are applied. However, automatic recognition is usually restricted to a small number of emotions classes mainly due to signal’s features and noise, EEG constraints and subject-dependent issues. In order to address these issues, in this paper a novel feature-based emotion recognition model is proposed for EEG-based Brain–Computer Interfaces. Unlike other approaches, our method explores a wider set of emotion types and incorporates additional features which are relevant for signal pre-processing and recognition classification tasks, based on a dimensional model of emotions: Valence and Arousal . It aims to improve the accuracy of the emotion classification task by combining mutual information based feature selection methods and kernel classifiers. Experiments using our approach for emotion classification which combines efficient feature selection methods and efficient kernel-based classifiers on standard EEG datasets show the promise of the approach when compared with state-of-the-art computational methods. [ABSTRACT FROM AUTHOR]

Interpretation of long-term Electroencephalography (EEG) records is a tiresome task for clinicians. This paper presents an efficient, low cost and novel approach for patient-specific classification of long-term epileptic EEG records. We aim to achieve this with the minimum supervision from the neurologist. To accomplish this objective, first a novel feature extraction method is proposed based on the mapping of EEG signals into two dimensional space, resulting into a texture image. The texture image is constructed by mapping and scaling EEG signals and their associated frequency sub-bands into the gray-level image domain. Image texture analysis using gray level co-occurrence matrix (GLCM) is then applied in order to extract multivariate features which are able to differentiate between seizure and seizure-free events. To evaluate the discriminative power of the proposed feature extraction method, a comparative study is performed, against other dedicated feature extraction methods. The comparative performance evaluations show that the proposed feature extraction method can outperform other state-of-art feature extraction methods with a low computational cost. With a training rate of 25%, the overall sensitivity of 70.19% and specificity of 97.74% are achieved in the classification of over 163 h of EEG records using support vector machine (SVM) classifiers with linear kernels and trained by the stochastic gradient descent (SGD) algorithm. [ABSTRACT FROM AUTHOR]

The early detection of drowsiness has become vital to ensure the correct and safe development of several industries' tasks. Due to the transient mental state of a human subject between alertness and drowsiness, automated drowsiness detection is a complex problem to tackle. The electroencephalography signals allow us to record variations in an individual's brain's electrical potential, where each of them gives specific information about a subject's mental state. However, due to this type of signal's nature, its acquisition, in general, is complex, so it is hard to have a large volume of data to apply techniques of Deep Learning for processing and classification optimally. Nevertheless, Capsule Neural Networks are a brand-new Deep Learning algorithm proposed for work with reduced amounts of data. It is a robust algorithm to handle the data's hierarchical relationships, which is an essential characteristic for work with biomedical signals. Therefore, this paper presents a Deep Learning-based method for drowsiness detection with CapsNet by using a concatenation of spectrogram images of the electroencephalography signals channels. The proposed CapsNet model is compared with a Convolutional Neural Network, which is outperformed by the proposed model, which obtains an average accuracy of 86,44 % and 87,57% of sensitivity against an average accuracy of 75,86% and 79,47% sensitivity for the CNN, showing that CapsNet is more suitable for this kind of datasets and tasks. • A Capsule Network for drowsiness detection method is proposed. • The system is based on spectrogram images concatenate information from Fz and Pz. • The traditional drowsiness detection methods need a big number of data. • The proposed method improves the results even with a small number of images. • The proposed method outperforms state of the art in comparison with CNN and ML. [ABSTRACT FROM AUTHOR]

Disorders of consciousness (DoC) happen frequently in brain injuries. Automated DoC detection is desirable because conventional clinical examinations, typically behavioural assessments, consume significant resources of manpower and time. Extracting knowledge from electroencephalogram (EEG) signals and then employing machine learning to classify patients are an attractive way to achieve automatic detection of DoC. However, an EEG dataset from brain injuries always has much more subjects without DoC than the subjects with DoC. Because of the heavy imbalance of the dataset, even if the re-sampling technique is used, it is still challenging to achieve good classification performance from, e.g., eXtreme Gradient Boosting (Xgboost), one of the most effective machine learning methods. This paper develops an ensemble of multiple Xgboost models (EoXgboost) to improve the machine learning performance for DoC detection in the presence of severe class distribution skews. We also explore effective connectivity indices to distinguish brain injuries with and without DoC. A new combined connectivity measure is shown to be effective in detecting DoC in a dataset of 648 brain injuries. Our classification results show that our EoXgboost classifier with the new combined connectivity index has achieved the best classification performance than the single connectivity indices. Our EoXgboost has detected DoC in brain injuries with the accuracy of 99.07%, AUC of 98.74%, specificity of 99.77%, and sensitivity of 97.71%. This demonstrates that our EoXgboost is a promising tool for DoC detection in brain injuries. [ABSTRACT FROM AUTHOR]

The nonlinear, noisy and outlier characteristics of electroencephalography (EEG) signals inspire the employment of fuzzy logic due to its power to handle uncertainty. This paper introduces an approach to classify motor imagery EEG signals using an interval type-2 fuzzy logic system (IT2FLS) in a combination with wavelet transformation. Wavelet coefficients are ranked based on the statistics of the receiver operating characteristic curve criterion. The most informative coefficients serve as inputs to the IT2FLS for the classification task. Two benchmark datasets, named Ia and Ib, downloaded from the brain–computer interface (BCI) competition II, are employed for the experiments. Classification performance is evaluated using accuracy, sensitivity, specificity and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, AdaBoost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The wavelet-IT2FLS method considerably dominates the comparable classifiers on both datasets, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II by 1.40% and 2.27% respectively. The proposed approach yields great accuracy and requires low computational cost, which can be applied to a real-time BCI system for motor imagery data analysis. [ABSTRACT FROM AUTHOR]

This paper proposes a non-invasive Electroencephalogram (EEG)-based Brain Computer Interface (BCI) system to achieve the easy-to-use and stable control of a low speed Unmanned Aerial Vehicle (UAV) for indoor target searching. The BCI system for UAV control consists of two main subsystems responsible for decision and semi-autonomous navigation. The decision subsystem is established based on the analysis of motor imagery (MI) EEG. The improved cross-correlation method (CC) is used to accomplish the MI feature extraction and the logistic regression method (LR) is employed to complete the MI feature classification and decision. The average classification accuracy rate of the BCI system reaches to 94.36%. The semi-autonomous navigation subsystem is utilized to avoid obstacles automatically for UAV and provide feasible directions for decision subsystem. The actual indoor target searching experiment is carried out to verify the performance of this BCI system. The experiment validates the feasibility and effectiveness of this BCI system for low speed UAV control by using MI and semi-autonomous navigation. [ABSTRACT FROM AUTHOR]

In this paper a seizure detector using EEG and ECG signals, as a module of a healthcare system, is presented. Specifically, the module is based on short-time analysis with time-domain and frequency-domain features and classification using support vector machines. The seizure detection module was evaluated on three subjects with diagnosed idiopathic generalized epilepsy manifested with absences. The achieved seizure detection accuracy was approximately 90% for all evaluated subjects. Feature ranking investigation and evaluation of the seizure detection module using subsets of features showed that the feature vector composed of approximately the 65%-best ranked parameters provides a good trade-off between computational demands and accuracy. This configurable architecture allows the seizure detection module to operate as part of a healthcare system in offline mode as well as in online mode, where real-time performance is needed. [ABSTRACT FROM AUTHOR]

Epileptic seizure is the most common disorder of human brain, which is generally detected from electroencephalogram (EEG) signals. In this paper, we have proposed the new features based on the phase space representation (PSR) for classification of epileptic seizure and seizure-free EEG signals. The EEG signals are firstly decomposed using empirical mode decomposition (EMD) and phase space has been reconstructed for obtained intrinsic mode functions (IMFs). For the purpose of classification of epileptic seizure and seizure-free EEG signals, two-dimensional (2D) and three-dimensional (3D) PSRs have been used. New features based on the 2D and 3D PSRs of IMFs have been proposed for classification of epileptic seizure and seizure-free EEG signals. Two measures have been defined namely, 95% confidence ellipse area for 2D PSR and interquartile range (IQR) of the Euclidian distances for 3D PSR of IMFs of EEG signals. These measured parameters show significant difference between epileptic seizure and seizure-free EEG signals. The combination of these measured parameters for different IMFs has been utilized to form the feature set for classification of epileptic seizure EEG signals. Least squares support vector machine (LS-SVM) has been employed for classification of epileptic seizure and seizure-free EEG signals, and its classification performance has been evaluated using different kernels namely, radial basis function (RBF), Mexican hat wavelet and Morlet wavelet kernels. Simulation results with various performance parameters of classifier, have been included to show the effectiveness of the proposed method for classification of epileptic seizure and seizure-free EEG signals. [ABSTRACT FROM AUTHOR]

This is, to the best of the authors knowledge, the first complete research on the state of the art on EEG based subject identification. As well as covering the full story of this field (from 1980 to 2013), an overview of the findings made in genetic and neurophysiology areas, from which it is based, is also provided. After a comprehensive search, 109 biometric publications were found and studied, from which 88 were finally included in this document. A categorization of papers is proposed based on the recording paradigm. The most used databases, some of them public, have been identified and named to allow the comparison of results from these and future works. The findings of this work show that, although basic questions remain to be answered, the EEG, and specially its power spectrum in the range of the alpha rhythm, contains subject specific information that can be used for classification. Moreover, approaches such as a multi-day-session training, the fusion of information from different electrodes and bands, and Support Vector Machines are recommended to maximize the system's performance. All in all, the problem of subject identification by means of their EEG is harder than initially expected, as it relies on information extracted from complex heterogeneous EEG traits which are the results of elaborated models of inheritance, which in turn makes the problem very sensitive to its variables (time, frequency, space, recording paradigm and algorithms). [ABSTRACT FROM AUTHOR]

Abstract: Epilepsy is one of the most common neurological disorders with 0.8% of the world population. The epilepsy is unpredictable and recurrent, so it is very difficult to treat. In this paper, we propose a new Electroencephalography (EEG) seizure detection method by using the dual-tree complex wavelet (DTCWT) – Fourier features. These features achieve perfect classification rates (100%) for the EEG database from the University of Bonn. These classification rates outperform a number of existing EEG seizure detection methods published in the literature. However, it should be mentioned that several recent works also achieved this perfect classification rate (100%). Our proposed method should be as good as these works since our method only performs the DTCWT transform for up to 5 scales and our method only conducts the FFT to the 4th and 5th scales of the DTCWT decomposition. In addition, we could replace the conventional FFT in our method by sparse FFT so that our method could be even faster. [Copyright &y& Elsevier]

Abstract: Epilepsy is one of the most common neurological disorders- approximately one in every 100 people worldwide are suffering from it. In this paper, a novel pattern recognition model is presented for automatic epilepsy diagnosis. Wavelet transform is investigated to decompose EEG into five EEG frequency bands which approximate to delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ) bands. Complexity based features such as permutation entropy (PE), sample entropy (SampEn), and the Hurst exponent (HE) are extracted from both the original EEG signals and each of the frequency bands. The wavelet-based methodology separates the alterations in PE, SampEn, and HE in specific frequency bands of the EEG. The effectiveness of these complexity based measures in discriminating between normal brain state and brain state during the absence of seizures is evaluated using the Extreme Learning Machine (ELM). It is discovered that although there exists no significant differences in the feature values extracted from the original EEG signals, differences can be recognized when the features are examined within specific EEG frequency bands. A genetic algorithm (GA) is developed to choose feature subsets that are effective for enhancing the recognition performance. The GA is also examined for weight alteration for both sensitivity and specificity. The results show that the abnormal EEG diagnosis rate of the model without the involvement of the genetic algorithm is 85.9%. However, the diagnosis rate of the model increases to 94.2% when the genetic algorithm is integrated as a feature selector. [Copyright &y& Elsevier]

Abstract: Application of neuroscience methods to analyze and understand human behavior related to markets and marketing exchange has recently gained research attention. The basic aim is to guide design and presentation of products to optimize them to be as compatible as possible with consumer preferences. This paper investigates physiological decision processes while participants undertook a choice task designed to elicit preferences for a product. The task required participants to choose their preferred crackers described by shape (square, triangle, round), flavor (wheat, dark rye, plain) and topping (salt, poppy, no topping). The two main research objectives were (1) to observe and evaluate the cortical activity of the different brain regions and the interdependencies among the Electroencephalogram (EEG) signals from these regions; and (2) unlike most research in this area that has focused mainly on liking/disliking certain products, we provide a way to quantify the importance of different cracker features that contribute to the product design based on mutual information. We used the commercial Emotiv EPOC wireless EEG headset with 14 channels to collect EEG signals from participants. We also used a Tobii-Studio eye tracker system to relate the EEG data to the specific choice options (crackers). Subjects were shown 57 choice sets; each choice set described three choice options (crackers). The patterns of cortical activity were obtained in the five principal frequency bands, Delta (0–4Hz), Theta (3–7Hz), Alpha (8–12Hz), Beta (13–30Hz), and Gamma (30–40Hz). There was a clear phase synchronization between the left and right frontal and occipital regions indicating interhemispheric communications during the chosen task for the 18 participants. Results also indicated that there was a clear and significant change (p <0.01) in the EEG power spectral activities taking a place mainly in the frontal (delta, alpha and beta across F3, F4, FC5 and FC6), temporal (alpha, beta, gamma across T7), and occipital (theta, alpha, and beta across O1) regions when participants indicated their preferences for their preferred crackers. Additionally, our mutual information analysis indicated that the various cracker flavors and toppings of the crackers were more important factors affecting the buying decision than the shapes of the crackers. [Copyright &y& Elsevier]