Classification Of EEG Signals Research Articles

A novel moving window-based power spectrum features for single-channel EEG classification using machine learning

Electroencephalogram (EEG) signal classification is a crucial and very difficult task. Meanwhile, extracting features that are representative and able to discriminate different types of EEG signals is a complex task. Such features are usually fed to machine learning algorithms to classify the EEG signals based on the extracted features. This paper proposed a highly accurate and real-time features extraction method that can be used to help physicians in detecting different types of seizures and states in EEG signals characterized by a set of features extracted from the power spectrum of the EEG window. This is achieved by applying the following four steps. First, the EEG signals dataset contains different classes of EEG signals: Normal Eye Closed, Normal Eye Opened, Focal Seizure, Non-Focal Seizure, and Ictal Seizure activities. Second, each EEG signal has a length of 4097 samples sampled with a sampling frequency of 173.6 Hz which resulted in 23.6 seconds in length, this signal will be truncated into windows (Sub-signals) with a length of 349 samples (Approximately 2 seconds) with a total number of 12 windows for each signal. Afterward, the Fourier Transform (FT) based power spectrum will be computed for each window, then a set of different features are extracted from each window's FT power spectrum, and these features are classified using different Machine Learning (ML) algorithms. The results showed that the proposed methodology yields around 98% accuracy for the five different classification scenarios using different ML algorithms. The suggested method is hence robust, fast, real-time, accurate, and simple.

Exploiting Asymmetric EEG Signals with EFD in Deep Learning Domain for Robust BCI

Motor imagery (MI) is a domineering paradigm in brain–computer interface (BCI) composition, personifying the imaginary limb motion into digital commandments for neural rehabilitation and automation exertions, while many researchers fathomed myriad solutions for asymmetric MI EEG signals classification, the existence of a robust, non-complex, and subject-invariant system is far-reaching. Thereupon, we put forward an MI EEG segregation pipeline in the deep-learning domain in an effort to curtail the existing limitations. Our method amalgamates multiscale principal component analysis (MSPCA), a novel empirical Fourier decomposition (EFD) signal resolution method with Hilbert transform (HT), followed by four pre-trained convolutional neural networks for automatic feature estimation and segregation. The conceived architecture is validated upon three binary class datasets: IVa, IVb from BCI Competition III, GigaDB from the GigaScience repository, and one tertiary class dataset V from BCI competition III. The average 10-fold outcomes capitulate 98.63%, 96.33%, and 89.96%, the highest classification accuracy for the aforesaid datasets accordingly using the AlexNet CNN model in a subject-dependent context, while in subject-independent cases, the highest success score was 97.69%, outperforming the contemporary studies by a fair margin. Further experiments such as the resolution scale of EFD, comparison with other signal decomposition (SD) methods, deep feature extraction, and classification with machine learning methods also accredits the supremacy of our proposed EEG signal processing pipeline. The overall findings imply that pre-trained models are reliable in identifying EEG signals due to their capacity to maintain the time-frequency structure of EEG signals, non-complex architecture, and their potential for robust classification performance.

EEG Signal Feature Extraction and Classification for Epilepsy Detection

Epilepsy is a neurological disorder of the central nervous system, characterized by sudden seizures caused by abnormal electrical discharges in the brain. Electroencephalogram (EEG) is the most common technique used for Epilepsy diagnosis. Generally, it is done by the manual inspection of the EEG recordings of active seizure periods (ictal). Several techniques have been proposed throughout the years to automate this process. In this study, we have developed three different approaches to extract features from the filtered EEG signals. The first approach was to extract eight statistical features directly from the time-domain signal. In the second approach, we have used only the frequency domain information by applying the Discrete Cosine Transform (DCT) to the EEG signals then extracting two statistical features from the lower coefficients. In the last approach, we have used a tool that combines both time and frequency domain information, which is the Discrete Wavelet Transform (DWT). Six different wavelet families have been tested with their different orders resulting in 37 wavelets. The first three decomposition levels were tested with every wavelet. Instead of feeding the coefficients directly to the classifier, we summarized them in 16 statistical features. The extracted features are then fed to three different classifiers k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Artificial Neural Network (ANN) to perform two binary classification scenarios: healthy versus epileptic(mainly from interictal activity),and seizure-free versus ictal. We have used a benchmark database, the Bonn database, which consists of five different sets. In the first scenario, we have taken six different combinations of the available data. While in the second scenario, we have taken five combinations. For Epilepsy detection (healthy vs epileptic), the first approach performed badly. Using the DCT improved the results, but the best accuracies were obtained with the DWT-based approach. For seizure detection, the three methods performed quite well. However, the third method had the best performance and was better than many state-of-the-art methods in terms of accuracy. After carrying out the experiments on the whole EEG signal, we separated the five rhythms and applied the DWT on them with the Daubechies7(db7) wavelet for feature extraction. We have observed that close accuracies to those recorded before can be achieved with only the Delta rhythm in the first scenario (Epilepsy detection) and the Beta rhythm in the second scenario (seizure detection).

An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications.

Traditional advertising techniques seek to govern the consumer's opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers' actual purchase behaviors. Neuromarketing is the new paradigm of understanding customer buyer behavior and decision making, as well as the prediction of their gestures for product utilization through an unconscious process. Existing methods do not focus on effective preprocessing and classification techniques of electroencephalogram (EEG) signals, so in this study, an effective method for preprocessing and classification of EEG signals is proposed. The proposed method involves effective preprocessing of EEG signals by removing noise and a synthetic minority oversampling technique (SMOTE) to deal with the class imbalance problem. The dataset employed in this study is a publicly available neuromarketing dataset. Automated features were extracted by using a long short-term memory network (LSTM) and then concatenated with handcrafted features like power spectral density (PSD) and discrete wavelet transform (DWT) to create a complete feature set. The classification was done by using the proposed hybrid classifier that optimizes the weights of two machine learning classifiers and one deep learning classifier and classifies the data between like and dislike. The machine learning classifiers include the support vector machine (SVM), random forest (RF), and deep learning classifier (DNN). The proposed hybrid model outperforms other classifiers like RF, SVM, and DNN and achieves an accuracy of 96.89%. In the proposed method, accuracy, sensitivity, specificity, precision, and F1 score were computed to evaluate and compare the proposed method with recent state-of-the-art methods.

Recognition of EEG based on Improved Black Widow Algorithm optimized SVM

As a classifier suitable for nonlinear samples, support vector machines (SVM) is widely used in Electroencephalogram (EEG) signals pattern recognition. The performance of SVM depends mainly on the selection of model parameters. This paper modifies the reproductive formula of the Black Widow Optimization (BWO) algorithm to obtain an improved Black Widow Optimization (IBWO) algorithm, which solves the problem of the restricted search radius of the BWO algorithm. We construct an IBWO-SVM model in which IBWO is used to optimize the penalty parameter C and the kernel function parameter g of the SVM, and the optimized SVM model is applied to EEG signals classification. The effectiveness and superiority of IBWO-SVM is verified by using the BCI competition III dataset IVa EEG public dataset, and compared with the SVM optimized by the original BWO algorithm (BWO-SVM), the artificial fish swarm algorithm (AFSA-SVM) and the particle swarm optimization algorithm (PSO-SVM), The simulation results demonstrate that all indicators of the IBWO-SVM model are better than the three comparison models, in which the average classification accuracy on five subjects reaches 97.29%. In addition, the two-class motor imagery (left arm stretch and right fist clenched) EEG data collected from our experiment is also used to test the performance of the IBWO-SVM model. The average classification accuracy of the final twelve subjects is 5.21%, 4.16% and 1.47% higher than that of BWO-SVM, AFSA-SVM, and PSO-SVM, respectively. Therefore, the IBWO-SVM model can effectively improve the accuracy of EEG signals pattern recognition, which has a very important practical value.

Multi frequency band fusion method for EEG signal classification

This paper proposes a novel convolutional neural network (CNN) fusion method for electroencephalography (EEG) motor imagery (MI) signal classification. The method is named MFBF, which stands for multifrequency band fusion. The MFBF method relies on filtering the input signal with different frequency bands and feeding each band signal to a duplicate of a CNN model; then, all duplicates are concatenated to form a fusion model. This paper also introduces the second release of Coleeg software, which is used for evaluation. The MFBF method has the advantage of the flexibility of choosing any model and any number of frequency bands. In the experimental evaluation, the CNN1D model and three frequency bands were used to form the CNN1D_MFBF model, and it was evaluated against the EEGNet_fusion model on three different datasets, which are: Physionet, BCI competition IV-2a, and a dataset from the Hungarian Academy of Sciences Research Centre for Natural Sciences (MTA-TTK). The CNN1D_MFBF model had comparable or better accuracy results with less than one-fifth of the training time, which is a significant advantage for the proposed method.

Convolutional Neural Network Classification of Rest EEG Signals among People with Epilepsy, Psychogenic Non Epileptic Seizures and Control Subjects.

Identifying subjects with epileptic seizures or psychogenic non-epileptic seizures from healthy subjects via interictal EEG analysis can be a very challenging issue. Indeed, at visual inspection, EEG can be normal in both cases. This paper proposes an automatic diagnosis approach based on deep learning to differentiate three classes: subjects with epileptic seizures (ES), subjects with non-epileptic psychogenic seizures (PNES) and control subjects (CS), analyzed by non-invasive low-density interictal scalp EEG recordings. The EEGs of 42 patients with new-onset ES, 42 patients with PNES video recorded and 19 patients with CS all with normal interictal EEG on visual inspection, were analyzed in the study; none of them was taking psychotropic drugs before registration. The processing pipeline applies empirical mode decomposition (EMD) to 5s EEG segments of 19 channels in order to extract enhanced features learned automatically from the customized convolutional neural network (CNN). The resulting CNN has been shown to perform well during classification, with an accuracy of 85.7%; these results encourage the use of deep processing systems to assist clinicians in difficult clinical settings.

Classification of SSVEP-EEG signals using CNN and Red Fox Optimization for BCI applications.

Classification of electroencephalography (EEG) signals associated with Steady-state visually evoked potential (SSVEP) is prominent because of its potential in restoring the communication and controlling capability of paralytic people. However, SSVEP signals classification is a challenging task for researchers because of its low signal-to-noise ratio, non-stationary and high dimensional properties. A proficient technique has to be evolved to classify the SSVEP-based EEG data. In recent times, convolutional neural network (CNN) has reached a quantum leap in EEG signal classification. Therefore, the proposed system employs CNN to classify the SSVEP-based EEG signals. Though CNN has proved its proficiency in handling EEG signal classification problems, the calibration of hyperparameters is required to enhance the performance of the model. The calibration of a hyperparameter is a time-consuming task, hence proposed an automated hyperparameter optimization technique using the Red Fox Optimization Algorithm (RFO). The effectiveness of the algorithm is evaluated by comparing it with the performance of Harris Hawk Optimization (HHO), Flower Pollination Algorithm (FPA), Grey Wolf Optimization Algorithm (GWO) and Whale Optimization Algorithm (WOA) based hyperparameter optimized CNN applied to the SSVEP based EEG signals multiclass dataset. The experimental results infer that the proposed algorithm can achieve a testing accuracy of 88.91% which is higher than other comparative algorithms like HHO, FPA, GWO and WOA. The above-mentioned values clearly show that the proposed algorithm achieved competitive performance when compared to the other reported algorithm.

Sparse measures with swarm-based pliable hidden Markov model and deep learning for EEG classification.

In comparison to other biomedical signals, electroencephalography (EEG) signals are quite complex in nature, so it requires a versatile model for feature extraction and classification. The structural information that prevails in the originally featured matrix is usually lost when dealing with standard feature extraction and conventional classification techniques. The main intention of this work is to propose a very novel and versatile approach for EEG signal modeling and classification. In this work, a sparse representation model along with the analysis of sparseness measures is done initially for the EEG signals and then a novel convergence of utilizing these sparse representation measures with Swarm Intelligence (SI) techniques based Hidden Markov Model (HMM) is utilized for the classification. The SI techniques utilized to compute the hidden states of the HMM are Particle Swarm Optimization (PSO), Differential Evolution (DE), Whale Optimization Algorithm (WOA), and Backtracking Search Algorithm (BSA), thereby making the HMM more pliable. Later, a deep learning methodology with the help of Convolutional Neural Network (CNN) was also developed with it and the results are compared to the standard pattern recognition classifiers. To validate the efficacy of the proposed methodology, a comprehensive experimental analysis is done over publicly available EEG datasets. The method is supported by strong statistical tests and theoretical analysis and results show that when sparse representation is implemented with deep learning, the highest classification accuracy of 98.94% is obtained and when sparse representation is implemented with SI-based HMM method, a high classification accuracy of 95.70% is obtained.

Motor Imaging EEG Signal Recognition of ResNet18 Network Based on Deformable Convolution

EEG signals with a weak amplitude, complex background noise, randomness, significant individual differences, and small data volume lead to insufficient feature extraction and low classification accuracy. Spurred by these concerns, this paper proposes a motor imaging EEG signal classification method based on fusing the improved ResNet18 network with the deformable convolutional network (DCN). Specifically, the original signal’s local airspace characteristics are enhanced by the common spatial patterns (CSP), and the time-frequency domain characteristics are displayed using the short-time Fourier transform (STFT). Then, the signal is converted into a time-frequency map, where a deformable convolution is applied to capture the contour characteristics of the time-frequency map. This strategy solves the problems of traditional convolution related to hard rules, i.e., the convolutional kernel shape can only be a square or rectangular core and cannot be dynamically changed according to the recognition target, resulting in a low recognition rate, prohibiting the network from extracting hidden features and affording enhanced identification and classification. Experimental results demonstrate that our method attains an average classification accuracy on a two-classification and two four-classification motor imaging EEG signals of 90.30%, 86.50%, and 88.08%, respectively, which is much higher than current work, proving our method’s effectiveness.

EEG-Based Emotion Classification Using Stacking Ensemble Approach.

Rapid advancements in the medical field have drawn much attention to automatic emotion classification from EEG data. People's emotional states are crucial factors in how they behave and interact physiologically. The diagnosis of patients' mental disorders is one potential medical use. When feeling well, people work and communicate more effectively. Negative emotions can be detrimental to both physical and mental health. Many earlier studies that investigated the use of the electroencephalogram (EEG) for emotion classification have focused on collecting data from the whole brain because of the rapidly developing science of machine learning. However, researchers cannot understand how various emotional states and EEG traits are related. This work seeks to classify EEG signals' positive, negative, and neutral emotional states by using a stacking-ensemble-based classification model that boosts accuracy to increase the efficacy of emotion classification using EEG. The selected features are used to train a model that was created using a random forest, light gradient boosting machine, and gradient-boosting-based stacking ensemble classifier (RLGB-SE), where the base classifiers random forest (RF), light gradient boosting machine (LightGBM), and gradient boosting classifier (GBC) were used at level 0. The meta classifier (RF) at level 1 is trained using the results from each base classifier to acquire the final predictions. The suggested ensemble model achieves a greater classification accuracy of 99.55%. Additionally, while comparing performance indices, the suggested technique outperforms as compared with the base classifiers. Comparing the proposed stacking strategy to state-of-the-art techniques, it can be seen that the performance for emotion categorization is promising.

Automatic Epileptic Seizure Detection Using PSO-Based Feature Selection and Multilevel Spectral Analysis for EEG Signals

Automatic epileptic seizure detection technologies for clinical diagnosis mainly rely on electroencephalogram (EEG) recordings, which are immensely useful tools for epileptic location and identification. Currently, traditional seizure detection methods based only on single-view features have great limitations for the typical dynamic and nonlinear EEG signals. An objective of this paper is to investigate the effect of multiview feature selection and multilevel spectral analysis methods on the identification of the EEG signals for seizure detection. Here, multiview features are extracted from time domain, frequency domain, and information theory to collect adequate information of EEG signals. And a feature selection algorithm based on particle swarm optimization (PSO) is proposed for automatic seizure detection. Moreover, due to the different frequency components of the EEG signals, they are divided into four kinds of brain waves for multilevel spectral analysis. The effect of these four rhythm waves on seizure detection is compared. Three well-known classifiers are employed to classify EEG signals concerning seizure or nonseizure events. The result shows that the average accuracy, specificity, and sensitivity of classification with the CHB-MIT database are 98.14%, 98.64%, and 96.79%, respectively. The application of the PSO-based feature selection method for automatic seizure detection improves accuracy by 5.99% with the SVM classifier. Compared with the state-of-the-art methods, the proposed method has superior competence with high performance for automatic seizure detection. It is further shown that the feature selection method is an indispensable step in seizure detection. With PSO-based feature selection and multilevel spectral analysis, the θ wave in the frequency range of 4-7 Hz shows better performance in the identification of EEG signals and is more suitable for the proposed method. The PSO-based feature selection algorithm for automatic seizure detection can be a useful assistant tool for clinical diagnosis.

Optimization enabled deep residual neural network for motor imagery EEG signal classification

The brain computer interface (BCI) aimed to offer an improved and quality life for people having disabilities. Various physiological sensors are utilized for designing the BCI application. Here, the electroencephalogram (EEG) is well-known for modeling the brain signals. However, the existing techniques based on EEG signal classification are computationally expensive and not so accurate. This paper devises Competitive Swarm Dragonfly Algorithm (CSDA) for classifying the EEG signals. In this model, the input EEG signal artifacts are discarded in pre-processing phase. The feature extraction is done to extract imperative features that include spectral-based features, like Amplitude modulation spectrogram, frequency-based features, like spectral flux, tonal power ratio, spectral centroid, spectral spread, Power Spectrum Density,logarithmic band power, and statistical features like kurtosis, entropy and skewness. Here, data augmentation is performed for making the data suitable for further processing. Deep Residual Network (DRN) is used to classify the motor imagery EEG signal. The suggested CSDA is used to train DRN, which is obtained by combining the competitive Swarm Optimizer and Dragonfly Algorithm. The performance of the adapted approach is determined using motor imagery multi-class dataset and motor imagery small training sets, in which the motor imagery multi-class dataset offers the highest specificity, accuracy, and sensitivity of 91.9% 91.6%, and 92.3%, respectively.

A multiwavelet-based sparse time-varying autoregressive modeling for motor imagery EEG classification

Brain-computer Interface (BCI) system based on motor imagery (MI) heavily relies on electroencephalography (EEG) recognition with high accuracy. However, modeling and classification of MI EEG signals remains a challenging task due to the non-linear and non-stationary characteristics of the signals. In this paper, a new time-varying modeling framework combining multiwavelet basis functions and regularized orthogonal forward regression (ROFR) algorithm is proposed for the characterization and classification of MI EEG signals. Firstly, the time-varying coefficients of the time-varying autoregressive (TVAR) model are precisely approximated with the multiwavelet basis functions. Then a powerful ROFR algorithm is employed to dramatically alleviate the redundant model structure and accurately recover the relevant time-varying model parameters to obtain high resolution power spectral density (PSD) features. Finally, the features are sent to different classifiers for the classification task. To effectively improve the accuracy of classification, a principal component analysis (PCA) algorithm is utilized to determine the best feature subset and Bayesian optimization algorithm is performed to obtain the optimal parameters of the classifier. The proposed method achieves satisfactory classification accuracy on the public BCI Competition II Dataset III, which proves that this method potentially improves the recognition accuracy of MI EEG signals, and has great significance for the construction of BCI system based on MI.

Multi-source manifold feature transfer learning with domain selection for brain-computer interfaces

Transfer learning uses the knowledge in source domains to improve the learning performance in the target domain, which is useful in electroencephalogram (EEG) based brain-computer interfaces (BCIs) with small training datasets. However, the existing transfer learning methods for EEG based BCI mainly consider the knowledge transfer from single-to-single (STS) domain or simply merge different source domains into a bigger one. In this paper, we propose a multi-source manifold feature transfer learning (MMFT) framework to transfer multi-source knowledge for EEG signals classification. MMFT minimizes marginal probability distribution on the Riemannian manifold using Riemannian alignment and Grassmann manifold feature learning, then transfers the manifold features with a conditional probability distribution adaptation in the structural risk minimization (SRM) function. Based on MMFT, w-MMFT is proposed to tackle the class imbalance issue for SRM, and the label similarity analysis (LSA) is proposed to select source domains for MMFT, forming a new LSA-MMFT framework. Experimental results on six datasets demonstrate that the proposed MMFT has achieved superior performance in classification accuracy and computational efficiency compared to state-of-the-art methods. The LSA-MMFT can get more stable performance than two other domain selection methods.

O001 / #892 A DEEP LEARNING APPROACH TO EVALUATING SEX DIFFERENCES IN ANTIDEPRESSANT RESPONSE TO NEUROMODULATION USING EEG IN MAJOR DEPRESSIVE DISORDER: TRACK 2: BRAIN STIMULATION FOR TREATMENT RESISTANT DEPRESSION, PAIN, AND PARKINSON'S DISEASE

Sex differences in the antidepressant response to repetitive transcranial magnetic stimulation (rTMS) for major depressive disorder (MDD) are poorly understood. Electroencephalography (EEG) can serve as a biomarker for treatment response, and deep learning (DL) can classify EEG signals to predict response. In this study, five DL models were created to classify males and females as rTMS responders/nonresponders using EEG.

A new common spatial pattern-based unified channels algorithm for driver’s fatigue EEG signals classification

A new common spatial pattern-based unified channels algorithm for driver’s fatigue EEG signals classification

Autoencoder-based improved deep learning approach for schizophrenic EEG signal classification

Autoencoder-based improved deep learning approach for schizophrenic EEG signal classification

Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals.

Electroencephalography (EEG) records the electrical activity of the brain, which is an important tool for the automatic detection of epileptic seizures. It is certainly a very heavy burden to only recognize EEG epilepsy manually, so the method of computer-assisted treatment is of great importance. This paper presents a seizure detection algorithm based on variational modal decomposition (VMD) and a deep forest (DF) model. Variational modal decomposition is performed on EEG recordings, and the first three variational modal functions (VMFs) are selected to construct the time–frequency distribution of the EEG signals. Then, the log−Euclidean covariance matrix (LECM) is computed to represent the EEG properties and form EEG features. The deep forest model is applied to complete the EEG signal classification, which is a non-neural network deep model with a cascade structure that performs feature learning through the forest. In addition, to improve the classification accuracy, postprocessing techniques are performed to generate the discriminant results by moving average filtering and adaptive collar expansion. The algorithm was evaluated on the Bonn EEG dataset and the Freiburg long−term EEG dataset, and the former achieved a sensitivity and specificity of 99.32% and 99.31%, respectively. The mean sensitivity and specificity of this method for the 21 patients in the Freiburg dataset were 95.2% and 98.56%, respectively, with a false detection rate of 0.36/h. These results demonstrate the superior performance advantage of our algorithm and indicate its great research potential in epilepsy detection.

Investigating the role of data preprocessing, hyperparameters tuning, and type of machine learning algorithm in the improvement of drowsy EEG signal modeling

• Electroencephalography measured driver drowsiness in simulator tests. • Driver drowsiness was classified with four modeling approaches. • Six algorithms of DT, ET, KNN, MLP, RF, and SVC were applied to the dataset. • The type of algorithm is more effective than the modeling approach. • Tree-based ensemble algorithms are more appropriate for drowsy EEG signal modeling. Driver drowsiness leads to fatal road traffic accidents. The effects of drowsy driving on electroencephalogram (EEG) signal are well visible. Accordingly, classifying EEG signal with machine learning (ML) is known as a reliable and accurate drowsiness detection technique. The type of ML algorithm, data preprocessing, and tuning the hyperparameters influence classification results greatly. In this paper, the drowsy EEG signal of 20 participants was measured in simulator tests. Six ML algorithms of decision tree, extra trees, K-nearest neighbors, multi-layer perceptron, random forest, and support vector classification were trained and cross-validated with different approaches to understand how data preprocessing and tuning the ML hyperparameters can improve drowsy EEG signal modeling. Results indicated that the type of ML algorithm has a more notable effect on modeling accuracy and modeling error than the modeling approach. Data preprocessing generally improved modeling results. But tuning the hyperparameters with the random search method wasn't helpful. Comparison of algorithms showed that tree-based ensemble algorithms (extra trees and random forest) were the most accurate models. They are more practical for real-time applications of drowsy EEG signal modeling.