Single-channel EEG Signals Research Articles

Automatic Detection and Correction of Blink Artifacts in Single Channel EEG Signals

Ocular Artifacts (OAs) are inevitable during EEG acquisition and make the signal analysis critical. Detection and correction of these artifacts is a major problem now a day’s. In this paper an energy detection method is used to detect the artifacts and performed wavelet thresholding within the researched zones to protect neural data at non blink regions. Various sets of Wavelet Transform (WT) techniques and threshold functions are collated and identification of the optimum combination for OA’s separation is indicated in many research areas including Technology & Management. The output of these methods at blink regions is compared interns of various standard metrics using established techniques of Supply Chains. Results of this study demonstrate that the SWT+HT has better in rejecting the artifacts than other methods in this paradigm.

Automatic Sleep Stage Classification With Single Channel EEG Signal Based on Two-Layer Stacked Ensemble Model

Sleep stage classification, including wakefulness (W), rapid eye movement (REM), and non- rapid eye movement (NREM) which includes three sleep stages that describe the depth of sleep, is one of the most critical steps in effective diagnosis and treatment of sleep-related disorders. Clinically, sleep staging is performed by domain experts through visual inspection of polysomnography (PSG) recordings, which is time-consuming, labor-intensive and often subjective in nature. Therefore, this study develops an automatic sleep staging system, which uses single channel electroencephalogram (EEG) signal, for convenience of wearing and less interference in the sleep, to do automatic identification of various sleep stages. To achieve the automatic sleep staging system, this study proposes a two-layer stacked ensemble model, which combines the advantages of random forest (RF) and LightGBM (LGB), where RF focuses on reducing the variance of the proposed model while LGB focuses on reducing the bias of the proposed model. Particularly, the proposed model introduces a class balance strategy to improve the N1 stage recognition rate. In order to evaluate the performance of the proposed model, experiments are performed on two datasets, including Sleep-EDF database (SEDFDB) and Sleep-EDF Expanded database (SEDFEDB). In the SEDFDB, the overall accuracy (ACC), weight F1-score (WF1), Cohen’s Kappa coefficient (Kappa), sensitivity of N1 (SEN-N1) obtained by proposed model are 91.2%, 0.916, 0.864 and 72.52% respectively using subject-non-independent test (SNT). In parallel, the ACC, WF1, Kappa, SEN-N1 obtained by proposed model are 82.4%, 0.751, 0.719 and 27.15% respectively using subject-independent test (SIT). Experimental results show that the performance of the proposed model are competitive with the state-of-the-art methods and results, and the recognition rate of N1 stage is significantly improved. Moreover, in the SEDFEDB, the experimental results indicate the robustness and generality of the proposed model.

Channel Projection-Based CCA Target Identification Method for an SSVEP-Based BCI System of Quadrotor Helicopter Control.

The brain-computer interface (BCI) plays an important role in assisting patients with amyotrophic lateral sclerosis (ALS) to enable them to participate in communication and entertainment. In this study, a novel channel projection-based canonical correlation analysis (CP-CCA) target identification method for steady-state visual evoked potential- (SSVEP-) based BCI system was proposed. The single-channel electroencephalography (EEG) signals of multiple trials were recorded when the subject is under the same stimulus frequency. The CCAs between single-channel EEG signals of multiple trials and sine-cosine reference signals were obtained. Then, the optimal reference signal of each channel was utilized to estimate the test EEG signal. To validate the proposed method, we acquired the training dataset with two testing conditions including the optimal time window length and the number of the trial of training data. The offline experiments conducted a comparison of the proposed method with the traditional canonical correlation analysis (CCA) and power spectrum density analysis (PSDA) method using a 5-class SSVEP dataset that was recorded from 10 subjects. Based on the training dataset, the online 3D-helicopter control experiment was carried out. The offline experimental results showed that the proposed method outperformed the CCA and the PSDA methods in terms of classification accuracy and information transfer rate (ITR). Furthermore, the online experiments of 3-DOF helicopter control achieved an average accuracy of 87.94 ± 5.93% with an ITR of 21.07 ± 4.42 bit/min.

Novel Approaches for the Removal of Motion Artifact From EEG Recordings

The electroencephalogram (EEG) signal is contaminated with various noises or artifacts during recording. For the automated detection of neurological disorders, it is a vital task to filter out these artifacts from the EEG signal. In this paper, we propose two novel approaches for the removal of motion artifact from the single channel EEG signal. These methods are based on the multiresolution total variation (MTV) and multiresolution weighted total variation (MWTV) filtering schemes. The multiresolution analysis using the discrete wavelet transform (DWT) helps to segregate the EEG signal into various subband signals. The total variation (TV) and weighted TV (WTV) are applied to the approximation subband signal. The filtered approximation subband signal is evaluated based on the difference between the noisy approximation subband signal and the output of the TV or WTV filter. The processed EEG signal is obtained using the multiresolution wavelet-based reconstruction. The difference in the signal to noise ratio ( $\Delta $ SNR) and the percentage of reduction in correlation coefficients ( $\eta $ ) is used for evaluating the diagnostic quality of the processed EEG signal. The experimental results demonstrate that the proposed MTV and MWTV approaches have better denoising performance with (average $\Delta $ SNR, and average $\eta $ ) values of (29.12 dB and 68.56%) and (29.29 dB and 67.51%), respectively, as compared to the existing techniques.

EEG sleep stages identification based on weighted undirected complex networks

Background and ObjectiveSleep scoring is important in sleep research because any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings can cause serious problems such as incorrect diagnosis, medication errors, and misinterpretations of patient's EEG recordings. The aim of this research is to develop a new automatic method for EEG sleep stages classification based on a statistical model and weighted brain networks. MethodsEach EEG segment is partitioned into a number of blocks using a sliding window technique. A set of statistical features are extracted from each block. As a result, a vector of features is obtained to represent each EEG segment. Then, the vector of features is mapped into a weighted undirected network. Different structural and spectral attributes of the networks are extracted and forwarded to a least square support vector machine (LS-SVM) classifier. At the same time the network's attributes are also thoroughly investigated. It is found that the network's characteristics vary with their sleep stages. Each sleep stage is best represented using the key features of their networks. ResultsIn this paper, the proposed method is evaluated using two datasets acquired from different channels of EEG (Pz-Oz and C3-A2) according to the R&K and the AASM without pre-processing the original EEG data. The obtained results by the LS-SVM are compared with those by Naïve, k-nearest and a multi-class-SVM. The proposed method is also compared with other benchmark sleep stages classification methods. The comparison results demonstrate that the proposed method has an advantage in scoring sleep stages based on single channel EEG signals. ConclusionsAn average accuracy of 96.74% is obtained with the C3-A2 channel according to the AASM standard, and 96% with the Pz-Oz channel based on the R&K standard.

Automatic and tunable algorithm for EEG artifact removal using wavelet decomposition with applications in predictive modeling during auditory tasks

Brain–computer interface (BCI) systems are becoming increasingly popular nowadays. Electroencephalogram (EEG) signals recorded by BCI systems are however frequently contaminated by artifacts and while applying any artifact removal algorithm, precautions should be taken not to remove useful information. Widely and most popular approaches to remove artifacts from EEG are based on independent component analysis (ICA), which relies on the multichannel EEG signal and needs an expert to manually pick the artifactual component to remove it or needs reference signals of artifacts. Recently, wavelet-based approaches have been proposed and demonstrated as well-suited for single channel EEG signal. However, control over the loss of information still remains an issue. Therefore, in this paper, we propose an algorithm based on wavelet packet decomposition (WPD) that allows controlling the suppression or removal of presumed artifacts, by tuning intuitive parameters. The proposed algorithm has three operating modes and two tuning parameters. We study the performance of the proposed algorithm and compare it with ICA-based approaches and a comparative wavelet-based approach on an EEG dataset collected for a study of auditory tasks. In addition to visual inspection, spectral response and distribution, the results of predictive tasks show that the proposed approach performs better than ICA-based approach and performance can be further improved by properly tuning the parameters for an individual predictive model. The proposed approach also performs better in terms of retention of neural information and removal of artifactual noise measured by mutual information and correlation coefficient, as compared to the comparative wavelet-based approach.

An automatic single-channel EEG-based sleep stage scoring method based on hidden Markov Model

ObjectiveSleep stage scoring is essential for diagnosing sleep disorders. Visual scoring of sleep stages is very time-consuming and prone to human errors. In this work, we introduce an efficient approach to improve the accuracy of sleep stage scoring and classification for sleep analysis. MethodIn this approach, a set of optimal features was first selected from a pool of features extracted from sleep EEG epochs by using a feature selection method based on the relevance and redundancy analysis. EEG segments were then classified using a random forest classifier. Finally, a Hidden Markov Model (HMM) was used to reduce false positives by incorporating knowledge of the temporal structure of transitions between sleep stages. We evaluated the proposed method using single-channel EEG signals from four public sleep EEG datasets scored according to R&K and AASM guidelines. We compared the performance of our method with existing methods using different cross validation strategies. ResultsUsing a leave-one-out validation strategy, our method achieved overall accuracies in the range of (79.4–87.4%) and (77.6–80.4%) with Kappa values in the range of 0.7–0.85 for six-stage (R&K) and five-stage (AASM) classification, respectively. Our method showed a reduction in overall accuracy up to 8% using the cross-dataset validation strategy in comparison with the subject cross-validation method. Comparison with existing method(s)Our method outperformed the existing methods for all multi-stage classification. ConclusionsThe proposed single-channel method can be used for robust and reliable sleep stage scoring with high accuracy and relatively low complexity required for real time applications.

Deep convolutional neural network for classification of sleep stages from single-channel EEG signals

Using a smart method for automatic diagnosis in medical applications, such as sleep stage classification is considered as one of the important challenges of the last few years which can replace the time-consuming process of visual inspection done by specialists. One of the problems regarding the automatic diagnosis of sleep patterns is extraction and selection of discriminative features generally demanding high computational burden. This paper provides a new single-channel approach to automatic classification of sleep stages from EEG signal. The main idea is to directly apply the raw EEG signal to deep convolutional neural network, without involving feature extraction/selection, which is a challenging process in the previous literature. The proposed network architecture includes 9 convolutional layers followed by 2 fully connected layers. In order to make the samples of different classes balanced, we used a preprocessing method called data augmentation. The simulation results of the proposed method for classification of 2 to 6 classes of sleep stages show the accuracy of 98.10%, 96.86%, 93.11%, 92.95%, 93.55% and Cohen’s Kappa coefficient of 0.98%, 0.94%, 0.90%, 0.86% and 0.89%, respectively. Furthermore, comparing the obtained results with the state-of-the-art methods reveals the performance improvement of the proposed sleep stage classification in terms of accuracy and Cohen’s Kappa coefficient.

SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach.

Electroencephalogram (EEG) is a common base signal used to monitor brain activities and diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep experts and is limited by inter-rater reliability. In this paper, we propose an automatic sleep stage annotation method called SleepEEGNet using a single-channel EEG signal. The SleepEEGNet is composed of deep convolutional neural networks (CNNs) to extract time-invariant features, frequency information, and a sequence to sequence model to capture the complex and long short-term context dependencies between sleep epochs and scores. In addition, to reduce the effect of the class imbalance problem presented in the available sleep datasets, we applied novel loss functions to have an equal misclassified error for each sleep stage while training the network. We evaluated the performance of the proposed method on different single-EEG channels (i.e., Fpz-Cz and Pz-Oz EEG channels) from the Physionet Sleep-EDF datasets published in 2013 and 2018. The evaluation results demonstrate that the proposed method achieved the best annotation performance compared to current literature, with an overall accuracy of 84.26%, a macro F1-score of 79.66% and κ = 0.79. Our developed model can be applied to other sleep EEG signals and aid the sleep specialists to arrive at an accurate diagnosis. The source code is available at https://github.com/SajadMo/SleepEEGNet.

Emotion recognition from single-channel EEG signals using a two-stage correlation and instantaneous frequency-based filtering method

Background and objective:The recognition of emotional states is a crucial step in the development of a brain-computer interface (BCI) system. Emotion recognition system finds applications in medical science for the impaired and disabled people. Electroencephalography assesses the neurophysiology of the brain for recognition of different emotional states.Methods: The audio-video stimulus based experimental setup is arranged for the electroencephalogram (EEG) recordings of happy, fear, sad, and relax emotions and a two-stage filtering method is proposed for the recognition of emotion EEG signals. At the first stage, a correlation-criterion is suggested for removal of noisy intrinsic mode functions (IMFs) from the IMFs obtained by applying the empirical mode decomposition on the raw EEG signal. The noise-free IMFs are used to reconstruct the denoised EEG signal with improved stationarity characteristics. The denoised EEG signal is further decomposed into modes using the variational mode decomposition (VMD). At the second stage, the instantaneous-frequency based filtering of VMD modes is performed and filtered modes are retained for the reconstruction of denoised EEG signal with the desired frequency range. After two-stage filtering, the non-linear measures of filtered EEG signals are used as input features to multi-class least squares support vector machine (MC-LS-SVM) classifier for emotion recognition.Results:The different kernel functions are tested in MC-LS-SVM classifier for emotion recognition. The Morlet wavelet (MW) kernel function provides the best individual classification accuracies for happy, fear, sad, and relax emotions as 92.79%, 87.62%, 88.98%, and 93.13%, respectively. The MW-kernel function also obtained the best overall accuracy of 90.63%, F1-score 0.9064, and kappa value 0.8751.Conclusions: The Audio-video stimulus based emotion EEG-dataset is recorded. A new filtering method is proposed for EEG signals. The proposed method provides better emotion recognition performance as compared to the state-of-the-art methods and classifies emotions using single-bipolar EEG channel, which can greatly reduce the complexity of emotion-recognition based BCI systems.

ROM-based Inference Method Built on Deep Learning for Sleep Stage Classification

We used a classical deep feedforward neural network (DFFNN) for an automatic sleep stage scoring based on a single-channel EEG signal. We used an open-available dataset, randomly selecting one healthy young adult for both training (≈5%) and evaluation (≈95%). We also augmented the validation by using 5-fold cross validations for the result comparisons. We introduced a new method for inferring the trained network based on a ROM module (memory concept), so it would be faster than directly inferring the trained Deep Neural Network (DNN). The ROM content is filled after the DNN network is trained by the training set and inferred using the testing set. An accuracy of 97% was achieved in inferring the test datasets using ROM when compared to the classic trained DNN inference process.

Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals

Automated evaluation of a subject's neurocognitive performance (NCP) is a relevant topic in neurological and clinical studies. NCP represents the mental/cognitive human capacity in performing a specific task. It is difficult to develop the study protocols as the subject's NCP changes in a known predictable way. Sleep is time-varying NCP and can be used to develop novel NCP techniques. Accurate analysis and interpretation of human sleep electroencephalographic (EEG) signals is needed for proper NCP assessment. In addition, sleep deprivation may cause prominent cognitive risks in performing many common activities such as driving or controlling a generic device; therefore, sleep scoring is a crucial part of the process. In the sleep cycle, the first stage of non-rapid eye movement (NREM) sleep or stage N1 is the transition between wakefulness and drowsiness and becomes relevant for the study of NCP.In this study, a novel cascaded recurrent neural network (RNN) architecture based on long short-term memory (LSTM) blocks, is proposed for the automated scoring of sleep stages using EEG signals derived from a single-channel. Fifty-five time and frequency-domain features were extracted from the EEG signals and fed to feature reduction algorithms to select the most relevant ones. The selected features constituted as the inputs to the LSTM networks. The cascaded architecture is composed of two LSTM RNNs: the first network performed 4-class classification (i.e. the five sleep stages with the merging of stages N1 and REM into a single stage) with a classification rate of 90.8%, and the second one obtained a recognition performance of 83.6% for 2-class classification (i.e. N1 vs REM). The overall percentage of correct classification for five sleep stages is found to be 86.7%. The objective of this work is to improve classification performance in sleep stage N1, as a first step of NCP assessment, and at the same time obtain satisfactory classification results in the other sleep stages.

单通道脑电信号LSTM睡眠分期智能终端

单通道脑电信号LSTM睡眠分期智能终端

Identity Authentication Using Portable Electroencephalography Signals in Resting States

Biometric identity authentication technology is widely used in the field of information security. As a new biological method, electroencephalography (EEG) is gradually applied in biometric recognition, because scientists believe that practicability and portability are the development direction of EEG identity authentication. This work investigates the feasibility of using resting state EEG signals recorded by single-channel portable device for identity authentication. Single-channel EEG classification are effectively improved by using mixed-method in three feature domains (i.e., time, frequency, and time-frequency), feature selection (Rayleigh quotient) and classifier design (ensemble classifier). We invited 46 subjects to participate in the EEG identity authentication experiment. Experimental results show that the open-eyed resting state is an ideal authentication method, and the average classification accuracy of the authentication algorithm can reach 95.48% in 2 seconds, validating that the new method of processing single-channel EEG signals is especially useful in extrapolating EEG identity authentication to realistic field contexts.

Robust sleep stage classification with single-channel EEG signals using multimodal decomposition and HMM-based refinement

Sleep stage classification is a most important process in sleep scoring which is used to evaluate sleep quality and diagnose sleep-related diseases. Compared to complex sleep analysis devices, automatic sleep stage classification methods using single-channel electroencephalography (EEG) records benefit from the convenience of wearing and less interference in the sleep, thus are appropriate for home-based sleep analysis. In these methods, the design of representative features for classification plays the most important role in determining the performance. Previous works have not achieved satisfactory outcomes for ignoring several kinds of effective features. In this work, a novel multimodal signal decomposition and feature extraction strategy is presented to obtain effective features for sub-band signals. Meanwhile, a rule-free refinement process based on hidden Markov model (HMM) is proposed to optimize the classification results automatically. Experimental results show the superior classification performance of the proposed method compared to state-of-the-art works, wherein the rule-free refinement also outperforms previous rule-based correction algorithms. This sleep stage classification method is expected to contribute to the design of home-based sleep monitoring and analyzing system.

Epileptic seizure detection based on The Limited Penetrable visibility graph algorithm and graph properties

Introduction: Epileptic seizure detection is a key step for both researchers and epilepsy specialists for epilepsy assessment due to the non-stationariness and chaos in the electroencephalogram (EEG) signals. Current research is directed toward the development of an efficient method for epilepsy or seizure detection based the limited penetrable visibility graph (LPVG) algorithm. Materials and Methods: Single channel EEG signals are mapped into the LPVGs and then 14 features are calculated from these graphs. Then some features are selected using Sequential forward feature selection method and given to error-correcting output codes (ECOC) to classify signals into three groups of healthy, seizure free (interictal) and during a seizure (ictal) groups. Results: Experimental results show our method can classify normal, ictal and interictal groups with a high accuracy of 96.31%. Conclusion: The proposed method is fast and easy. Comparison the performance of the proposed method with other automatic seizure detection method also shows our method has better performance.

ResSleepNet: Automatic sleep stage classification on raw single-channel EEG

Automatic sleep stage classification is an important paradigm in intelligence and promises considerable advantages to the health. Electroencephalography (EEG) is a reflection of the electrophysiological activities of brain neurons. Most current automated methods require the multiple electroencephalogram channels and rely on hand-engineered features which require prior knowledge about sleep stage scoring. Consequently, research efforts to purse for an automatic stage scoring based on machine learning techniques have been carried out over the last years. In this paper, a deep learning model, named ResSleepNet, is proposed for automatic sleep stage scoring based on raw single-channel EEG, it can automatically learn features from raw single channel EEG signal, and build an automatic sleep staging model for assisted sleep staging. The model is applied to an open-access database named Sleep-EDF, and the results demonstrated that the model scored the EEG epochs with the accuracy of 87.9%.

Abstract 271: Discrimination Performance of a Non-Invasive and Single-Channel Electroencephalography-Based Brain Cell Viability for Comparing High vs. Low Quality Cardiopulmonary Resuscitation; A Porcine Cardiac Arrest Experimental Study

Background: Maintenance of brain cell viability is the most important goal of cardiopulmonary resuscitation (CPR) in cardiac arrest. We have developed an non-invasive and single-channel EEG-based Brain Resuscitation Index (EBRI) that can be used to measure the brain cell viability. The purpose of this study is to evaluate whether high-quality (HQ) and low-quality (LQ) CPR can be distinguished using the EBRI. Methods: A random case-crossover experimental animal study using porcine cardiac arrest model was performed. After 1 minute of untreated ventricular fibrillation, HQ-CPR (compression depth 5 cm and compression rate 100 per min) and LQ-CPR (compression depth 3 cm and compression rate 60 per min) were alternated every 50 seconds and repeated 10 cycles overall for five pigs. The computing formula of the EBRI was developed to predict the end-tidal CO2 (ETCO2) value using a single-channel EEG signals attached to forehead. The EEG signals were continuously measured and calculated simultaneously with the EBRI. Receiver operating characteristic of the area under the curve (ROC-AUC) was used to compare the discrimination power of HQ-CPR versus LQ-CPR. We compared the ROC-AUCs of two EBRI models reflecting time lags )o seconds to 16 seconds) between CPR change (HQ to LQ or LQ to HQ) and measurement of EBRI) comparing with ETCO2 itself. Results: Demographics of vital signs and hemodynamic parameters were significantly different between HQ- versus LQ-CPR. (Table 1) The ETCO2 and various models of EBRI with different time lags were concordantly displayed according to HQ- and LQ-CPR alteration in Figure 1. When comparing the AUC-ROC of EBRI models with various time lags for HQ-CPR versus LQ-CPR, the EBRI with 8 seconds time lag model showed the highest value; 0.86 (0,86-0,87). The AUC-ROC of ETCO2 itself showed 0.67 (0.66-0.68). Conclusion: A non-invasive and single-channel EEG-based brain resuscitation index than ETCO2 showed the higher performance for discriminating high-and low-quality CPR in a case-crossover animal study. Keywords: Cardiopulmonary Resuscitation, Quality, Electroencephalography, Cell Viability

Recurrent Deep Neural Networks for Real-Time Sleep Stage Classification From Single Channel EEG.

Objective: We investigate the design of deep recurrent neural networks for detecting sleep stages from single channel EEG signals recorded at home by non-expert users. We report the effect of data set size, architecture choices, regularization, and personalization on the classification performance.Methods: We evaluated 58 different architectures and training configurations using three-fold cross validation.Results: A network consisting of convolutional (CONV) layers and long short term memory (LSTM) layers can achieve an agreement with a human annotator of Cohen's Kappa of ~0.73 using a training data set of 19 subjects. Regularization and personalization do not lead to a performance gain.Conclusion: The optimal neural network architecture achieves a performance that is very close to the previously reported human inter-expert agreement of Kappa 0.75.Significance: We give the first detailed account of CONV/LSTM network design process for EEG sleep staging in single channel home based setting.

Single channel electroencephalogram (EEG) brain computer interface (BCI) feature extraction and quantization method for support vector machine classification

Over the recent years, there has been a huge interest towards Electroencephalogram (EEG) based brain computer interface (BCI) system. BCI system enables the extraction of meaningful information directly from the human brain via suitable signal processing and machine learning method and thus, many researches have applied this technology towards rehabilitation and assistive robotics. Such application is important towards improving the lives of people with motor diseases such as Amytrophic Lateral Scelorosis (ALS) disease or people with quadriplegia/tetraplegia. This paper introduces features extraction method based on the Fast Fourier Transform (FFT) with logarithmic bin-ning for rapid classification using Support Vector Machine (SVM) algorithm, with an application towards a BCI system with a shared con-trol scheme. In general, subjects wearing a single channel EEG electrode located at F8 (10-20 international standards) were required to syn-chronously imagine a star rotating and mind relaxation at specific time and direction. The imagination of a star would trigger a mobile robot suggesting that there exists a target object at certain direction. Based on the proposed algorithm, we showed that our algorithm can distin-guish between mind relaxation and mental star rotation with up to 80% accuracy from the single channel EEG signals.