Single-channel EEG Signals Research Articles

MRASleepNet: a multi-resolution attention network for sleep stage classification using single-channel EEG

Objective. Computerized classification of sleep stages based on single-lead electroencephalography (EEG) signals is important, but still challenging. In this paper, we proposed a deep neural network called MRASleepNet for automatic sleep stage classification using single-channel EEG signals. Approach. The proposed MRASleepNet model consisted of a feature extraction (FE) module, a multi-resolution attention (MRA) module, and a gated multilayer perceptron (gMLP) module, as well as a direct pathway for computing statistical features. The FE, MRA, and gMLP modules were used to extract features, establish feature attention, and obtain temporal relationships between features, respectively. EEG signals were normalized and cut into 30 s segments, and enhanced by incorporating contextual information from adjacent data segments. After data enhancement, the 40 s data segments were input to the MRASleepNet model. The model was evaluated on the SleepEDF and the cyclic alternating pattern (CAP) databases, using such metrics as the accuracy, Kappa, and macro-F1 (MF1). Main results. For the SleepEDF-20 database, the proposed model had an accuracy of 84.5%, an MF1 of 0.789, and a Kappa of 0.786. For the SleepEDF-78 database, the model had an accuracy of 81.4%, an MF1 of 0.754, and a Kappa of 0.743. For the CAP database, the model had an accuracy of 74.3%, an MF1 of 0.656, and a Kappa of 0.652. The proposed model achieved satisfactory performance in automatic sleep stage classification tasks. Significance. The time- and frequency-domain features extracted by the FE module and filtered by the MRA module, together with the temporal features extracted by the gMLP module and the statistical features extracted by the statistical highway, enabled the proposed model to obtain a satisfying performance in sleep staging. The proposed MRASleepNet model may be used as a new deep learning method for automatic sleep stage classification. The code of MRASleepNet will be made available publicly on https://github.com/YuRui8879/.

Staging study of single-channel sleep EEG signals based on data augmentation.

Accurate sleep staging is an essential basis for sleep quality assessment and plays an important role in sleep quality research. However, the occupancy of different sleep stages is unbalanced throughout the sleep process, which makes the EEG datasets of different sleep stages have a class imbalance, which will eventually affect the automatic assessment of sleep stages. In this paper, we propose a Residual Dense Block and Deep Convolutional Generative Adversarial Network (RDB-DCGAN) data augmentation model based on the DCGAN and RDB, which takes two-dimensional continuous wavelet time-frequency maps as input, expands the minority class of sleep EEG data and later performs sleep staging by Convolutional Neural Network (CNN). The results of the CNN classification comparison test with the publicly available dataset Sleep-EDF show that the overall sleep staging accuracy of each stage after data augmentation is improved by 6%, especially the N1 stage, which has low classification accuracy due to less original data, also has a significant improvement of 19%. It is fully verified that data augmentation by improving the DCGAN model can effectively improve the classification problem of the class imbalance sleep dataset.

A Deep Transfer Learning Framework for Sleep Stage Classification with Single-Channel EEG Signals.

The polysomnogram (PSG) is the gold standard for evaluating sleep quality and disorders. Attempts to automate this process have been hampered by the complexity of the PSG signals and heterogeneity among subjects and recording hardwares. Most of the existing methods for automatic sleep stage scoring rely on hand-engineered features that require prior knowledge of sleep analysis. This paper presents an end-to-end deep transfer learning framework for automatic feature extraction and sleep stage scoring based on a single-channel EEG. The proposed framework was evaluated over the three primary signals recommended by the American Academy of Sleep Medicine (C4-M1, F4-M1, O2-M1) from two data sets that have different properties and are recorded with different hardware. Different Time-Frequency (TF) imaging approaches were evaluated to generate TF representations for the 30 s EEG sleep epochs, eliminating the need for complex EEG signal pre-processing or manual feature extraction. Several training and detection scenarios were investigated using transfer learning of convolutional neural networks (CNN) and combined with recurrent neural networks. Generating TF images from continuous wavelet transform along with a deep transfer architecture composed of a pre-trained GoogLeNet CNN followed by a bidirectional long short-term memory (BiLSTM) network showed the best scoring performance among all tested scenarios. Using 20-fold cross-validation applied on the C4-M1 channel, the proposed framework achieved an average per-class accuracy of 91.2%, sensitivity of 77%, specificity of 94.1%, and precision of 75.9%. Our results demonstrate that without changing the model architecture and the training algorithm, our model could be applied to different single-channel EEGs from different data sets. Most importantly, the proposed system receives a single EEG epoch as an input at a time and produces a single corresponding output label, making it suitable for real time monitoring outside sleep labs as well as to help sleep lab specialists arrive at a more accurate diagnoses.

Detecting Depression Using Single-Channel EEG and Graph Methods

Objective: This paper applies graph methods to distinguish major depression disorder (MDD) and healthy (H) subjects using the graph features of single-channel electroencephalogram (EEG) signals. Methods: Four network features—graph entropy, mean degree, degree two, and degree three—were extracted from the 19-channel EEG signals of 64 subjects (26 females and 38 males), and then these features were forwarded to a support vector machine to conduct depression classification based on the eyes-open and eyes-closed statuses, respectively. Results: Statistical analysis showed that graph features with degree of two and three, the graph entropy of MDD was significantly lower than that for H (p < 0.0001). Additionally, the accuracy of detecting MDD using single-channel T4 EEG with leave-one-out cross-validation from H was 89.2% and 92.0% for the eyes-open and eyes-closed statuses, respectively. Conclusion: This study shows that the graph features of a short-term EEG can help assess and evaluate MDD. Thus, single-channel EEG signals can be used to detect depression in subjects. Significance: Graph feature analysis discovered that MDD is more related to the temporal lobe than the frontal lobe.

Multimodal detection of epilepsy with deep neural networks

Epilepsy constitutes a chronic noncommunicable disease of the brain affecting approximately 50 million people around the world. Most of the existing research initiatives propose methods for detecting and predicting epilepsy, which rely on the extraction of handcrafted features and the train of traditional machine learning classifiers. In this paper, we present two new methods to distinguish healthy, interictal, and ictal cases without the time-consuming procedure of feature extraction. Firstly, we apply the short-time fourier transform (STFT) to the single-channel electroencephalogram (EEG) signals and construct an image consisting of three channels. This image is passed through pretrained models, including AlexNet, DenseNet201, EfficientNet, ResNet18, etc. Secondly, we introduce a multimodal deep neural network. Specifically, we pass each single-channel EEG signal through two branches of convolutional neural networks (CNNs), which can extract low and high frequency features. Also, we apply the short-time fourier transform (STFT) to the EEG signals and create an image consisting of three channels. The image is passed through a pretrained EfficientNet-B7 model. Finally, we employ a gated multimodal unit to control the importance of each modality. We evaluate the performance of the proposed model on five different cases on the EEG database of the University of Bonn and show that our introduced model achieves comparable performance to state-of-the-art approaches.

Two-Stage Intelligent Multi-Type Artifact Removal for Single-Channel EEG Settings: A GRU Autoencoder Based Approach.

The wearable and portable Electroencephalogram (EEG) sensing systems are deeply interfered by unavoidable physiological artifacts due to the limited recording resources. In this work, an intelligent artifact removal system that handles single-channel EEG signals in the presence of mixed multi-type artifacts is investigated. The basic idea is to represent the mixed artifacts in contaminated varying EEG signals with the unchanged latent pattern features, and then employ the adaptive artifact removal scheme to separate the contamination and clean EEG signals in the encoded feature domain. To minimize the risks of corrupting clean signals and keeping artifacts by mistake, the artifact removal is formulated as an identification-removal two-stage minimization problem, and an attention based adaptive feature concentration mechanism is designed to improve the removal utility and reduce the calculation consumption. In the real implementation on open real-world dataset, this study achieves the artifact identification accuracy of 98.52% and average correlation coefficient of 0.73 for the removal of strong mixed multi-type artifacts. This study can deal with single-channel EEG signals contaminated by mixed multi-type artifacts with high accuracy and low overhead, and is more effective and stable than traditional schemes with fixed criteria. This study can significantly improve the signal quality acquired by simplified EEG sensing systems, and may extend the application of wearable and portable EEG sensing systems to medical diagnosis, cognitive science research and other applications requiring clinical setups.

Single-channel EEG signal extraction based on DWT, CEEMDAN, and ICA method.

In special application scenarios, such as portable anesthesia depth monitoring, portable emotional state recognition and portable sleep monitoring, electroencephalogram (EEG) signal acquisition equipment is required to be convenient and easy to use. It is difficult to remove electrooculogram (EOG) artifacts when the number of EEG acquisition channels is small, especially when the number of observed signals is less than that of the source signals, and the overcomplete problem will arise. The independent component analysis (ICA) algorithm commonly used for artifact removal requires the number of basis vectors to be smaller than the dimension of the input data due to a set of standard orthonormal bases learned during the convergence process, so it cannot be used to solve the overcomplete problem. The empirical mode decomposition method decomposes the signal into several independent intrinsic mode functions so that the number of observed signals is more than that of the source signals, solving the overcomplete problem. However, when using this method to solve overcompleteness, the modal aliasing problem will arise, which is caused by abnormal events such as sharp signals, impulse interference, and noise. Aiming at the above problems, we propose a novel EEG artifact removal method based on discrete wavelet transform, complete empirical mode decomposition for adaptive noise (CEEMDAN) and ICA in this paper. First, the input signals are transformed by discrete wavelet (DWT), and then CEEMDAN is used to solve the overcomplete and mode aliasing problems, meeting the a priori conditions of the ICA algorithm. Finally, the components belonging to EOG artifacts are removed according to the sample entropy value of each independent component. Experiments show that this method can effectively remove EOG artifacts while solving the overcomplete and modal aliasing problems.

A dual-stream deep neural network integrated with adaptive boosting for sleep staging

Humans spend about one-third of their time in sleep. Sleep is closely related to human physical and mental health and is a very important life activity. Automatic sleep stage classification is an important tool for analyzing sleep quality. However, due to different difficulties such as poor signal quality, the minor difference between different stages, and class imbalance problem, developing sleep staging systems with high performance is still a hot topic in current sleep-related research. In this study, we propose a novel adaptive-boosting-based dual-stream neural network to classify sleep stages using different modalities of single-channel EEG signals. The proposed network is designed based on two different forms of the single-channel EEG signal, that is, the original raw one-dimensional EEG signal and the two-dimensional time–frequency signal obtained by Continuous Wavelet Transform (CWT). The network for one-dimensional signal consists of a double-branch convolution neural network in order to extract low-frequency and high-frequency features separately. The backbone network for the two-dimensional signal is the modified ResNet50 network structure, which is enhanced by a multi-scale attention mechanism for learning more discriminate features. Furthermore, adaptive boosting algorithm is utilized to alleviate the class-imbalance problem that existed in many sleep-related datasets. Specifically, multiple weak classifiers are trained, and then a boosting-based ensemble strategy is used to obtain a strong classifier for sleep stage classification. In our experiments, two public datasets are utilized to evaluate the performance of the proposed network. Compared with other state-of-the-art methods, the method proposed in this study obtained competitive performance in terms of metrics such as Accuracy (Acc) and Cohen Kappa (κ), which are 85.8%, and 0.80 respectively on the Sleep-EDF-20 dataset and 81.0%, 0.73 respectively on Sleep-EDF-78 dataset.

Remove Artifacts from a Single-Channel EEG Based on VMD and SOBI.

With the development of portable EEG acquisition systems, the collected EEG has gradually changed from being multi-channel to few-channel or single-channel, thus the removal of single-channel EEG signal artifacts is extremely significant. For the artifact removal of single-channel EEG signals, the current mainstream method is generally a combination of the decomposition method and the blind source separation (BSS) method. Between them, a combination of empirical mode decomposition (EMD) and its derivative methods and ICA has been used in single-channel EEG artifact removal. However, EMD is prone to modal mixing and it has no relevant theoretical basis, thus it is not as good as variational modal decomposition (VMD) in terms of the decomposition effect. In the ICA algorithm, the implementation method based on high-order statistics is widely used, but it is not as effective as the implementation method based on second order statistics in processing EMG artifacts. Therefore, aiming at the main artifacts in single-channel EEG signals, including EOG and EMG artifacts, this paper proposed a method of artifact removal combining variational mode decomposition (VMD) and second order blind identification (SOBI). Semi-simulation experiments show that, compared with the existing EEMD-SOBI method, this method has a better removal effect on EOG and EMG artifacts, and can preserve useful information to the greatest extent.

An End-to-End Deep Leaning Approach for Sleep-Wake Classification Using Single Channel EEG Signals

Abstract Sleep quality has a significant impact on human physical and mental health. The detection of sleep-wake states is thus of significant importance in the study of sleep. The performance of classical machine learning models for automated sleep detection depends on the signals considered and feature extraction methods. Moreover, hand-crafted features are highly dependent on the experts’ experience and their prior knowledge about different physiological signals and conditions of the subjects. To overcome this limitation, this paper develops an end-to-end deep learning approach for automated feature extraction and detection of sleep-wake states using single channel raw EEG signals. Moreover, we leverage transfer learning to train and fine tune the proposed model to avoid the complexities associated with building a deep learning model from scratch. Using polysomnography (PSG) data of 20 patients, our results demonstrate the effectiveness of the proposed deep learning pipeline, achieving an excellent test performance in detecting sleep events with an overall sensitivity and precision of 92.7% and 92.1% respectively. The results demonstrate that the proposed approach can achieve superior performance compared to state-of-the-art studies on Sleep- Wake classification. Furthermore, it can attain reliable results as an alternative to classical methods that heavily rely on expert defined features.

Deep Learning Model of Sleep EEG Signal by Using Bidirectional Recurrent Neural Network Encoding and Decoding

Electroencephalogram (EEG) is a signal commonly used for detecting brain activity and diagnosing sleep disorders. Manual sleep stage scoring is a time-consuming task, and extracting information from the EEG signal is difficult because of the non-linear dependencies of time series. To solve the aforementioned problems, in this study, a deep learning model of sleep EEG signal was developed using bidirectional recurrent neural network (BiRNN) encoding and decoding. First, the input signal was denoised using the wavelet threshold method. Next, feature extraction in the time and frequency domains was realized using a convolutional neural network to expand the scope of feature extraction and preserve the original EEG feature information to the maximum extent possible. Finally, the time-series information was mined using the encoding–decoding module of the BiRNN, and the automatic discrimination of the sleep staging of the EEG signal was realized using the SoftMax function. The model was cross-validated using Fpz-Cz single-channel EEG signals from the Sleep-EDF dataset for 19 nights, and the results demonstrated that the proposed model can achieve a high recognition rate and stability.

Developing a robust model to predict depth of anesthesia from single channel EEG signal

Monitoring depth of anaesthesia (DoA) from electroencephalograph (EEG) signals is an ongoing challenge for anaesthesiologists. In this study, we propose an intelligence model that predicts the DoA from a single channel electroencephalograph (EEG) signal. A segmentation technique based on a sliding window is employed to partition EEG signals. Hierarchical dispersion entropy (HDE) is applied to each EEG segment. A set of features is extracted from each EEG segment. The extracted features are investigated using a community graph detection approach (CGDA), and the most relevant features are selected to trace the DoA. The proposed model, based on HDE coupled with CGDA, is evaluated in term of BIS index using several statistical metrics such Q-Q plot, regression, and correlation coefficients. In addition, the proposed model is evaluated against the BIS index in the case of the poor signal quality. The results demonstrated that the proposed model showed an earlier reaction compared with the BIS index when patient’s state transits from deep anaesthesia to moderate anaesthesia in the case of poor signal quality. The highest Pearson correlation coefficient obtained by the proposed is 0.96.

Alzheimer's Disease Analysis Algorithm Based on No-threshold Recurrence Plot Convolution Network.

Alzheimer's disease is a neurological disorder characterized by progressive cognitive dysfunction and behavioral impairment that occurs in old. Early diagnosis and treatment of Alzheimer's disease is great significance. Electroencephalography (EEG) signals can be used to detect Alzheimer's disease due to its non-invasive advantage. To solve the problem of insufficient analysis by single-channel EEG signal, we analyze the relationship between multiple channels and build PLV framework. To solve the problem of insufficient representation of 1D signal, a threshold-free recursive plot convolution network was constructed to realize 2D representation. To solve the problem of insufficient EEG signal characterization, a fusion algorithm of clinical features and imaging features was proposed to detect Alzheimer's disease. Experimental results show that the algorithm has good performance and robustness.

An Automatic Method for Epileptic Seizure Detection Based on Deep Metric Learning.

Electroencephalography (EEG) is a commonly used clinical approach for the diagnosis of epilepsy which is a life-threatening neurological disorder. Many algorithms have been proposed for the automatic detection of epileptic seizures using traditional machine learning and deep learning. Although deep learning methods have achieved great success in many fields, their performance in EEG analysis and classification is still limited mainly due to the relatively small sizes of available datasets. In this paper, we propose an automatic method for the detection of epileptic seizures based on deep metric learning which is a novel strategy tackling the few-shot problem by mitigating the demand for massive data. First, two one-dimensional convolutional embedding modules are proposed as a deep feature extractor, for single-channel and multichannel EEG signals respectively. Then, a deep metric learning model is detailed along with a stage-wise training strategy. Experiments are conducted on the publicly-available Bonn University dataset which is a benchmark dataset, and the CHB-MIT dataset which is larger and more realistic. Impressive averaged accuracy of 98.60% and specificity of 100% are achieved on the most difficult classification of interictal (subset D) vs ictal (subset E) of the Bonn dataset. On the CHB-MIT dataset, an averaged accuracy of 86.68% and specificity of 93.71% are reached. With the proposed method, automatic and accurate detection of seizures can be performed in real time, and the heavy burden of neurologists can be effectively reduced.

Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis.

The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. Since successful detection of various neurological and neuromuscular disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove/reduce motion artifacts from EEG and fNIRS signals using reliable and robust methods. In this regard, this paper proposes two robust methods: (i) Wavelet packet decomposition (WPD) and (ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: (i) difference in the signal to noise ratio ( ) and (ii) percentage reduction in motion artifacts ( ). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average  (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average  (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique, i.e., the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average  and  values of 30.76 dB and 59.51%, respectively, for all the EEG recordings. On the other hand, for the available 16 fNIRS recordings, the two-stage motion artifacts removal technique, i.e., WPD-CCA has produced the best average  (16.55 dB, utilizing db1 wavelet packet) and largest average  (41.40%, using fk8 wavelet packet). The highest average  and  using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively, for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed in comparison with the single-stage WPD method. In addition, the average  also increases when WPD-CCA techniques are used instead of single-stage WPD for both EEG and fNIRS signals. The increment in both  and  values is a clear indication that two-stage WPD-CCA performs relatively better compared to single-stage WPD. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.

SleepContextNet: A temporal context network for automatic sleep staging based single-channel EEG

Background and objective: Single-channel EEG is the most popular choice of sensing modality in sleep staging studies, because it widely conforms to the sleep staging guidelines. The current deep learning method using single-channel EEG signals for sleep staging mainly extracts the features of its surrounding epochs to obtain the short-term temporal context information of EEG epochs, and ignore the influence of the long-term temporal context information on sleep staging. However, the long-term context information includes sleep stage transition rules in a sleep cycle, which can further improve the performance of sleep staging. The aim of this research is to develop a temporal context network to capture the long-term context between EEG sleep stages.Methods: In this paper, we design a sleep staging network named SleepContextNet for sleep stage sequence. SleepContextNet can extract and utilize the long-term temporal context between consecutive EEG epochs, and combine it with the short-term context. we utilize Convolutional Neural Network(CNN) layers for learning representative features from each sleep stage and the representation features sequence learned are fed into a Recurrent Neural Network(RNN) layer for learning long-term and short-term context information among sleep stage in chronological order. In addition, we design a data augmentation algorithm for EEG to retain the long-term context information without changing the number of samples.Results: We evaluate the performance of our proposed network using four public datasets, the 2013 version of Sleep-EDF (SEDF), the 2018 version of Sleep-EDF Expanded (SEDFX), Sleep Heart Health Study (SHHS) and the CAP Sleep Database. The experimental results demonstrate that SleepContextNet outperforms state-of-the-art techniques in terms of different evaluation metrics by capturing long-term and short-term temporal context information. On average, accuracy of 84.8% in SEDF, 82.7% in SEDFX, 86.4% in SHHS and 78.8% in CAP are obtained under subject-independent cross validation.Conclusions: The network extracts the long-term and short-term temporal context information of sleep stages from the sequence features, which utilizes the temporal dependencies among the EEG epochs effectively and improves the accuracy of sleep stages. The sleep staging method based on forward temporal context information is suitable for real-time family sleep monitoring system.

Online interaction method of mobile robot based on single-channel EEG signal and end-to-end CNN with residual block model

The development of alternative pathways to communicate with outside world independent on language or limb motions is important. However, one of the challenges is the multi-dimensional and accurate control of robot using head signals. This paper proposes an end-to-end CNN with residual block model, which uses the filtered raw signal as input and detects left eye blink, right eye blink, continuous eye blink, and grit teeth tasks, and develops an online brain–computer interface (BCI) system to control the left turn, right turn, forward, stop, and speed of the mobile robot platform, TurtleBot. The portable EEG measurement device TGAM module is used to collect single channel dry electrode EEG signals. Through time domain (TD) and time–frequency analysis, the time period and frequency range of each task signal are analyzed, which lays the foundation for analysis window parameter determination and feature extraction. The proposed model uses one-dimensional convolution to extract local features and two-dimensional convolution to extract global features, and the average detection accuracy is 97.399%, which is significantly higher than that of the state-of-the-art machine learning classifiers with the TD and frequency domain (FD) fusion features as input (p<0.01). The detection performance of FD features outperforms the TD features (p<0.01). Online BCI system based on the proposed method is developed to interact with the TurtleBot.

Automated Ocular Artifacts Removal Framework Based on Adaptive Chirp Mode Decomposition

The removal of ocular artifacts (OAs) from electroencephalogram (EEG) signals is crucial for effective and accurate analysis of the signals in different neurological and brain computer interface (BCI) applications. Towards this aspect, various multi-channel and single-channel techniques have been proposed for the removal/suppression of OAs from EEG signals. Recently, due to the rising demand of resource-limited ambulatory systems, single-channel techniques are more preferred for OAs removal. Although the existing techniques effectively remove OAs, they introduce distortion in the denoised EEG signal and the corresponding neurological features. Therefore, in this paper, we propose a framework for automated detection and removal of OAs from single-channel EEG signal based on discrete Fourier transform (DFT) and adaptive chirp mode decomposition (ACMD). The proposed framework is built in four stages: application of DFT to single-channel EEG signal; rejection of low-frequency baseline wander components; processed EEG signal decomposition using ACMD; rejection of mode containing OAs based on proposed peak point count threshold criteria. The performance of the proposed framework is assessed using noise-free EEG and EEG corrupted with OAs signals with different shapes and frequencies taken from three publicly available databases and in terms of different performance metrics. Further, comparative performance analysis demonstrates that the proposed framework outperforms the existing OAs removal techniques based on wavelet thresholding, variational mode decomposition (VMD), and Savitzky-Golay filter (SG-filter).

DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection.

Brain Computer Interface technology enables a pathway for analyzing EEG signals for seizure detection. EEG signal decomposition, features extraction and machine learning techniques are more familiar in seizure detection. However, selecting decomposition technique and concatenation of their features for seizure detection is still in the state-of-the-art phase. This work proposes DWT-EMD Feature level Fusion-based seizure detection approach over multi and single channel EEG signals and studied the usability of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) feature fusion with respect to individual DWT and EMD features over classifiers SVM, SVM with RBF kernel, decision tree and bagging classifier for seizure detection. All classifiers achieved an improved performance over DWT-EMD feature level fusion for two benchmark seizure detection EEG datasets. Detailed quantification results have been mentioned in the Results section.

SSA with CWT and k-Means for Eye-Blink Artifact Removal from Single-Channel EEG Signals

Recently, the use of portable electroencephalogram (EEG) devices to record brain signals in both health care monitoring and in other applications, such as fatigue detection in drivers, has been increased due to its low cost and ease of use. However, the measured EEG signals always mix with the electrooculogram (EOG), which are results due to eyelid blinking or eye movements. The eye-blinking/movement is an uncontrollable activity that results in a high-amplitude slow-time varying component that is mixed in the measured EEG signal. The presence of these artifacts misled our understanding of the underlying brain state. As the portable EEG devices comprise few EEG channels or sometimes a single EEG channel, classical artifact removal techniques such as blind source separation methods cannot be used to remove these artifacts from a single-channel EEG signal. Hence, there is a demand for the development of new single-channel-based artifact removal techniques. Singular spectrum analysis (SSA) has been widely used as a single-channel-based eye-blink artifact removal technique. However, while removing the artifact, the low-frequency components from the non-artifact region of the EEG signal are also removed by SSA. To preserve these low-frequency components, in this paper, we have proposed a new methodology by integrating the SSA with continuous wavelet transform (CWT) and the k-means clustering algorithm that removes the eye-blink artifact from the single-channel EEG signals without altering the low frequencies of the EEG signal. The proposed method is evaluated on both synthetic and real EEG signals. The results also show the superiority of the proposed method over the existing methods.