Features Of EEG Signals Research Articles

AN EPILEPSY DIAGNOSIS METHOD BASED ON A TRANSFERRED ALEXNET MODEL AND EEG SIGNALS

Epilepsy is a neurological disorder, and its sudden seizures pose a serious threat to the quality of life of patients. Not only does this condition cause patients to potentially lose control and consciousness during seizures, leading to possible injuries or dangerous situations, but it also has a significant impact on their mental health, triggering issues such as anxiety and depression. An intelligent epilepsy diagnosis process based on electroencephalogram (EEG) signals offers notable advantages. First, it provides noninvasive brainwave signals that accurately monitor and record the patterns and characteristics of epileptic seizures, offering objective diagnostic criteria for doctors. Second, with the use of artificial intelligence and machine learning technologies, large amounts of EEG data can be efficiently analyzed, improving the speed and accuracy of diagnosis and providing timely and effective treatment plans for patients. Additionally, intelligent diagnostic systems can achieve real-time monitoring, promptly alert people to potential epileptic seizures, and provide a safer living environment for patients. In this context, this paper proposes an epilepsy diagnosis method based on a transferred AlexNet model and EEG signals. The main contributions of this paper are as follows. (1) A transfer learning mechanism is incorporated into the AlexNet model through the direct transfer of its neural network structure and the modification of some existing neural network structures, followed by collaborative training with the addition of a domain adaptation layer in the network. This introduced transfer mechanism can address small sample size issues. (2) The traditional AlexNet model suffers from redundant feature extraction, leading to slow training. This paper adds batch normalization (BN) layers after each convolutional layer in the AlexNet model to normalize the features extracted from the convolutional layers. This emphasizes the representation of the important features of EEG signals and enables the lower layers of the network to learn the features needed for EEG signal processing. (3) The transferred AlexNet model proposed in this paper is applied to extract the features of epilepsy EEG signals, and the extracted features are input into a support vector machine (SVM) classifier to obtain epilepsy diagnosis results. Comparative experiments show that the diagnostic method used in this paper yields superior results and shorter training times than those of the competing approaches.

MOTION-STIMULATED EEG RECOGNITION BY YOLO DEEP NETWORK

In order to elucidate the relationship between sportsman motion awareness and brain waves from a physiological perspective, research was conducted on the recognition of motion-stimulated brain waves. A motion-stimulated electroencephalogram (EEG) recognition method based on the You Only Look Once (YOLO) deep network is proposed to address the high complexity, high interference, strong randomness, and strong time-varying characteristics of EEG signals. Under the YOLO deep network framework, replacing the Efficient Layer Aggregation Networks-A (ELAN-A) module with the Convolution-Next (CvNX) module achieves the goal of lightweight processing of the YOLO network. Furthermore, the Simple Attention Mechanism (SimAM) attention mechanism is introduced, and a special loss function is set. During the experiment, the short-time Fourier transform was used to convert the EEG signals of the test subjects into time-frequency EEG images with rich features, which can be used as inputs for the YOLO network. The experimental results show that our method achieves recognition accuracy of over 90% and the highest of over 95% in the recognition of five types of motion-induced EEG waves. Compared to CNN and traditional YOLO methods, our method has 3–5% improved with recognition accuracy.

Epilepsy EEG signals classification based on sparse principal component logistic regression model

Epilepsy is a chronic brain disease caused by excessive discharge of brain neurons. Long-term recurrent seizures bring a lot of trouble to patients and their families. Prediction of different stages of epilepsy is of great significance. We extract pearson correlation coefficients (PCC) between channels in different frequency bands as features of EEG signals for epilepsy stages prediction. However, the features are of large feature dimension and serious multi-collinearity. To eliminate these adverse influence, the combination of traditional dimension reduction method principal component analysis (PCA) and logistic regression method with regularization term is proposed to avoid over-fitting and achieve the feature sparsity. The experiments are conducted on the widely used CHB-MIT dataset using different regularization terms L 1 and L 2 , respectively. The proposed method identifies various stages of epilepsy quickly and efficiently, and it presents the best average accuracy of 94.86%, average precision of 96.71%, average recall of 93.48%, average kappa value of 0.90 and average Matthews correlation coefficient (MCC) value of 0.90 for all patients.

Technological Vanguard: the outstanding performance of the LTY-CNN model for the early prediction of epileptic seizures

Background:Epilepsy is a common neurological disorder that affects approximately 60 million people worldwide. Characterized by unpredictable neural electrical activity abnormalities, it results in seizures with varying intensity levels. Electroencephalography (EEG), as a crucial technology for monitoring and predicting epileptic seizures, plays an essential role in improving the quality of life for people with epilepsy.Method:This study introduces an innovative deep learning model, a lightweight triscale yielding convolutional neural network” (LTY-CNN), that is specifically designed for EEG signal analysis. The model integrates a parallel convolutional structure with a multihead attention mechanism to capture complex EEG signal features across multiple scales and enhance the efficiency achieved when processing time series data. The lightweight design of the LTY-CNN enables it to maintain high performance in environments with limited computational resources while preserving the interpretability and maintainability of the model.Results:In tests conducted on the SWEC-ETHZ and CHB-MIT datasets, the LTY-CNN demonstrated outstanding performance. On the SWEC-ETHZ dataset, the LTY-CNN achieved an accuracy of 99.9%, an area under the receiver operating characteristic curve (AUROC) of 0.99, a sensitivity of 99.9%, and a specificity of 98.8%. Furthermore, on the CHB-MIT dataset, it recorded an accuracy of 99%, an AUROC of 0.932, a sensitivity of 99.1%, and a specificity of 93.2%. These results signify the remarkable ability of the LTY-CNN to distinguish between epileptic seizures and nonseizure events. Compared to other existing epilepsy detection classifiers, the LTY-CNN attained higher accuracy and sensitivity.Conclusion:The high accuracy and sensitivity of the LTY-CNN model demonstrate its significant potential for epilepsy management, particularly in terms of predicting and mitigating epileptic seizures. Its value in personalized treatments and widespread clinical applications reflects the broad prospects of deep learning in the health care sector. This also highlights the crucial role of technological innovation in enhancing the quality of life experienced by patients.

MISNet: multi-source information-shared EEG emotion recognition network with two-stream structure.

When constructing machine learning and deep neural networks, the domain shift problem on different subjects complicates the subject independent electroencephalography (EEG) emotion recognition. Most of the existing domain adaptation methods either treat all source domains as equivalent or train source-specific learners directly, misleading the network to acquire unreasonable transfer knowledge and thus resulting in negative transfer. This paper incorporates the individual difference and group commonality of distinct domains and proposes a multi-source information-shared network (MISNet) to enhance the performance of subject independent EEG emotion recognition models. The network stability is enhanced by employing a two-stream training structure with loop iteration strategy to alleviate outlier sources confusing the model. Additionally, we design two auxiliary loss functions for aligning the marginal distributions of domain-specific and domain shared features, and then optimize the convergence process by constraining gradient penalty on these auxiliary loss functions. Furthermore, the pre-training strategy is also proposed to ensure that the initial mapping of shared encoder contains sufficient emotional information. We evaluate the proposed MISNet to ascertain the impact of several hyper-parameters on the domain adaptation capability of network. The ablation experiments are conducted on two publically accessible datasets SEED and SEED-IV to assess the effectiveness of each loss function. The experimental results demonstrate that by disentangling private and shared emotional characteristics from differential entropy features of EEG signals, the proposed MISNet can gain robust subject independent performance and strong domain adaptability.

Fractal based feature extraction technique for classifying EEG signal for color visualization tasks

Exploring and finding Significant features for colour visualization tasks using the EEG signals is crucial in developing a robust Brain-machine Interface (BMI). The visually evoked potential carries multiple pieces of information, and finding its best feature is a tedious task. The main objective of this research is to concentrate on various linear and non-linear features which classifies the visually evoked potential when visualizing various colours for a certain period with reduced computational time and with higher accuracy. The feature extraction techniques utilized for extracting the features of EEG signals while visualizing various colours are Power Spectral Intensity (PSI), Spectral Entropy (SE), Detrended Fluctuation analysis (DFA), Higuchi Fractal Dimension (HFD), Petrossian Fractal Dimension (PFD), Multifractal Detrended Fluctuation Analysis (MFDFA). The extracted features were classified using the Multiclass classifier using one vs rest technique Support Vector Machine algorithm. The result shows that the MFDFA method with multiclass classifier combination has achieved 97.4 percent of classification accuracy when compared with other features.

Graph-Based Information Separator and Area Convolutional Network for EEG-Based Intention Decoding

In the current research on brain-computer interface (BCI), the electroencephalography (EEG) signal is usually only represented by a two-dimensional matrix, and the installation position of the EEG electrodes and the correlation between them are not considered. Actually, the cerebral cortex is a continuous potential surface and the information collected directly from each electrode is influenced by the other electrodes, so direct use of the raw data results in information redundancy. This paper converts the EEG signal into a graph, and then creates an information separator (IS) based on the Laplace matrix of the graph to obtain the independent source information of electrode nodes, and propose an IS-based area convolutional network (IS-ACN). Integrating the proposed IS with some advanced methods, the experimental results show that the incorporation of IS can enhance the performance of these methods. By observing and tracking samples with abnormal noise in the BCI competition IV dataset 2a, it is demonstrated that the proposed method can greatly reduce the influence of noise and effectively obtain the source features of EEG signals with low signal-to-noise ratio, and the average accuracy and kappa coefficient of the proposed IS-ACN on this dataset are 80.59% and 74.1%, respectively.

A Dual-Branch Spatio-Temporal-Spectral Transformer Feature Fusion Network for EEG-Based Visual Recognition

Recognizing visual objects from single-trial electroencephalograph (EEG) signals is a promising brain-computer interface (BCI) technology. However, due to the redundant features from noisy multi-channel EEG signals, it is still a challenging task to achieve high precision recognition. Recent deep learning approaches commonly extract spatio-temporal features of EEG signals, which neglect important spectral-temporal features and may degrade the EEG recognition performance. To address the deficiency, we propose a novel channel attention weighting and multi-level adaptive spectral aggregation based dual-branch spatio-temporal-spectral Transformer feature fusion network (CAW-MASA-STST) for EEG-based visual recognition. Specially, we first develop a channel attention weighting (CAW) to automatically learn the channel weights of EEG signals. Then, a graph convolution-based multi-level adaptive spectral aggregation (MASA) is employed to aggregate spectral-temporal features of different sub-bands. Finally, a spatio-temporal-spectral Transformer (STST) is designed to fuse spatio-temporal and spectral-temporal features, which enhances the comprehensive learning ability by modeling the temporal dependencies of the fused features. Competitive experimental results on two public datasets demonstrate that the proposed method is able to achieve superior recognition performance compared with the state-of-the-art methods, indicating a feasible solution for visual recognition-based BCI technology. The code of our proposed method will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ljbuaa/VisualDecoding</uri> .

Dynamic Threshold Distribution Domain Adaptation Network: A Cross-Subject Fatigue Recognition Method Based on EEG Signals

Electroencephalogram (EEG) has been widely used in driver fatigue recognition due to its high-resolution and non-invasive characteristics in recent years. However, EEG signals have strong variability and significant individual differences, which put forward extremely high requirements on the generalization ability of the model in practical use. To address these issues, a dynamic threshold distribution domain adaptation network (DTDDAN) is proposed to classify the EEG signals for driver fatigue recognition. In the model, the domain discriminator is introduced to alleviate the marginal distribution difference of different domains, and a new loss, Jensen–Shannon loss (JS loss), is applied to relieve the conditional distribution difference of different domains to learn cross-subject invariant deep features of EEG signals. In addition, we propose a dynamic threshold pseudo label strategy to assign pseudo labels to the samples of the target domain in the training process of the model. This strategy can make sure that the high-quality unlabeled data of the target domain contribute to the model training and the model can fully learn the information of the target domain. With extensive experiments on our self-constructed EEG-based fatigue driving dataset, competitive performance is achieved for the cross-subject classification task, with average classification sensitivity, specificity, and accuracy of 89.8%,93.8% and 92.2%, respectively. And it is shown that our model can effectively extract cross-subject invariant EEG signals deep features by the experimental results.

Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.

The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals.

An Affective EEG Analysis Method Without Feature Engineering

Emotional electroencephalography (EEG) signals are a primary means of recording emotional brain activity.Currently, the most effective methods for analyzing emotional EEG signals involve feature engineering and neuralnetworks. However, neural networks possess a strong ability for automatic feature extraction. Is it possible to discardfeature engineering and directly employ neural networks for end-to-end recognition? Based on the characteristics of EEGsignals, this paper proposes an end-to-end feature extraction and classification method for a dynamic self-attention network(DySAT). The study reveals significant differences in brain activity patterns associated with different emotions acrossvarious experimenters and time periods. The results of this experiment can provide insights into the reasons behind thesedifferences.

CTCNet: A CNN Transformer capsule network for sleep stage classification

In this paper, we propose a novel neural network architecture called CTCNet. First, we adopt a multi-scale convolutional neural network (MSCNN) to extract low and high-frequency features, adaptive channel feature recalibration (ACFR) to enhance the model’s sensitivity to important channel features in the feature maps and reduce dependence on irrelevant or redundant features, a multi-scale dilated convolutional block (MSDCB) to capture characteristics of different types among feature channels. Second, we use Transformer to extract global temporal context features. Third, we employ capsule network to capture spatial location relationships among EEG features and refine these features. Besides, the capsule network module is used as our model’s classifier to classify the final results. It is worth noting that our model better solves the problem that previous researches failed to take into account the simultaneous extraction of local features and global temporal context characteristics of EEG signals, and ignored the spatial location relationships between these features. Eventually, we assess our model on three datasets and it achieves better or comparable performance than most state-of-the-art methods.

Sleep Stage Classification Via Multi-View Based Self-Supervised Contrastive Learning of EEG.

Self-supervised learning (SSL) is a challenging task in sleep stage classification (SSC) that is capable of mining valuable representations from unlabeled data. However, traditional SSL methods typically focus on single-view learning and do not fully exploit the interactions among information across multiple views. In this study, we focused on a multi-domain view of the same EEG signal and developed a self-supervised multi-view representation learning framework via time series and time-frequency contrasting (MV-TTFC). In the MV-TTFC framework, we built-in a cross-domain view contrastive learning prediction task to establish connections between the temporal view and time-frequency (TF) view, thereby enhancing the information exchange between multiple views. In addition, to improve the quality of the TF view inputs, we introduced an enhanced multisynchrosqueezing transform, which can create high energy concentration TF image views to compensate for the inaccurate representations in traditional TF processing techniques. Finally, integrating temporal, TF, and fusion space contrastive learning effectively captured the latent features in EEG signals. We evaluated MV-TTFC based on two real-world SSC datasets (SleepEDF-78 and SHHS) and compared it with baseline methods in downstream tasks. Our method exhibited state-of-the-art performance, achieving accuracies of 78.64% and 81.45% with SleepEDF-78 and SHHS, respectively, and macro F1-scores of 70.39% with SleepEDF-78 and 70.47% with SHHS.

Epileptic Seizure Detection Based on Path Signature and Bi-LSTM Network With Attention Mechanism.

Automatic seizure detection using electroen-cephalogram (EEG) can significantly expedite the diagnosis of epilepsy, thereby facilitating prompt treatment and reducing the risk of future seizures and associated complications. While most existing EEG-based epilepsy detection studies employ deep learning models, they often ignore the chronological relationships between different EEG channels. To tackle this limitation, a novel automatic epilepsy detection method is proposed, which leverages path signature and Bidirectional Long Short-Term Memory (Bi-LSTM) neural network with an attention mechanism. The path signature algorithm is used to extract discriminative features for capturing the dynamic dependencies between different channels of EEG, while Bi-LSTM with attention further analyzes the inherent temporal dependencies hidden in EEG signal features. Our method is evaluated on two public EEG databases with different sizes (CHB-MIT and TUEP) and a private database from a local hospital. Two experimental settings are used, i.e., five-fold cross-validation and leave-one-out cross-validation. Experimental results show that our method achieves 99.09%, 95.60%, and 99.87% average accuracies on CHB-MIT, TUEP with 250Hz, and TUEP with 256Hz, respectively. On the private dataset, our method also achieves 99.40% average accuracy, which outperforms other methods. Furthermore, our method exhibits robustness in patients, as demonstrated by the evaluation results of cross-patient experiments.

Research on the Creative Performance of Digital Film and Television Works Based on Virtual Reality Technology

Abstract This study investigates the application of virtual reality technology in the creative expression of digital film and television productions, especially the role of EEG signal denoising and feature recognition methods in enhancing the audience experience. The study uses wavelet threshold denoising and parallel RLS adaptive filtering algorithms to process EEG signals to improve the accuracy and reliability of the data. Then, the EEG signals were feature extracted using a bihemispheric domain adversarial neural network (BiDANN) to more accurately recognize the user’s emotional responses. The experimental results show that in the virtual reality environment, the users’ concentration and emotional reactions are significantly improved, with the average concentration reaching 74.21 and the average value of the electrodermal test data being 6.19. In addition, the eye-movement interaction experiments show that different types of digital movie and television works can cause additional attention allocation of users in the VR environment, leading other creative performance effects. The study’s results prove that virtual reality technology can significantly enhance the innovative performance of digital movie and television works and improve the audience’s viewing experience.

Hierarchical Dynamic Graph Convolutional Network With Interpretability for EEG-Based Emotion Recognition.

Graph convolutional networks (GCNs) have shown great prowess in learning topological relationships among electroencephalogram (EEG) channels for EEG-based emotion recognition. However, most existing GCN-only methods are designed with a single spatial pattern, lacking connectivity enhancement within local functional regions and ignoring the data dependencies of EEG original data. In this article, hierarchical dynamic GCN (HD-GCN) is proposed to explore dynamic multilevel spatial information among EEG channels, with discriminative features of EEG signals as auxiliary information. Specifically, representation learning in topological space consists of two branches: one for extracting global dynamic information and one for exploring augmentation information in local functional regions. In each branch, a layerwise adjacency matrix is utilized to enrich the expressive power of GCN. Furthermore, a data-dependent auxiliary information module (AIM) is developed to capture multidimensional fusion features. Extensive experiments on two public datasets, SJTU emotion EEG dataset (SEED) and DREAMER, demonstrate that the proposed method consistently exceeds state-of-the-art methods. Interpretability analysis of the proposed model is performed, discovering the active brain regions and important electrode pairs related to emotion.

Iteratively Calibratable Network for Reliable EEG-Based Robotic Arm Control.

Robotic arms are increasingly being utilized in shared workspaces, which necessitates the accurate interpretation of human intentions for both efficiency and safety. Electroencephalogram (EEG) signals, commonly employed to measure brain activity, offer a direct communication channel between humans and robotic arms. However, the ambiguous and unstable characteristics of EEG signals, coupled with their widespread distribution, make it challenging to collect sufficient data and hinder the calibration performance for new signals, thereby reducing the reliability of EEG-based applications. To address these issues, this study proposes an iteratively calibratable network aimed at enhancing the reliability and efficiency of EEG-based robotic arm control systems. The proposed method integrates feature inputs with network expansion techniques. This integration allows a network trained on an extensive initial dataset to adapt effectively to new users during calibration. Additionally, our approach combines motor imagery and speech imagery datasets to increase not only its intuitiveness but also the number of command classes. The evaluation is conducted in a pseudo-online manner, with a robotic arm operating in real-time to collect data, which is then analyzed offline. The evaluation results demonstrated that the proposed method outperformed the comparison group in 10 sessions and demonstrated competitive results when the two paradigms were combined. Therefore, it was confirmed that the network can be calibrated and personalized using only the new data from new users.

Robust Epileptic Seizure Detection Based on Biomedical Signals Using an Advanced Multi-View Deep Feature Learning Approach.

Epilepsy is a neurological disorder characterized by abnormal neuronal discharges that manifest in life-threatening seizures. These are often monitored via EEG signals, a key aspect of biomedical signal processing (BSP). Accurate epileptic seizure (ES) detection significantly depends on the precise identification of key EEG features, which requires a deep understanding of the data's intrinsic domain. Therefore, this study presents an Advanced Multi-View Deep Feature Learning (AMV-DFL) framework based on machine learning (ML) technology to enhance the detection of relevant EEG signal features for ES. Our method initially applies a fast Fourier transform (FFT) on EEG data for traditional frequency domain feature (TFD-F) extraction and directly incorporates time domain (TD) features from the raw EEG signals, establishing a comprehensive traditional multi-view feature (TMV-F). Deep features are subsequently extracted autonomously from optimal layers of one-dimensional convolutional neural networks (1D CNN), resulting in multi-view deep features (MV-DF) integrating both time and frequency domains. A multi-view forest (MV-F) is an interpretable rule-based advanced ML classifier used to construct a robust, generalized classification. Tree-based SHAP explainable artificial intelligence (T-XAI) is incorporated for interpreting and explaining the underlying rules. Experimental results confirm our method's superiority, surpassing models using TMV-FL and single-view deep features (SV-DF) by 4% and outperforming other state-of-the-art methods by an average of 3% in classification accuracy. The AMV-DFL approach aids clinicians in identifying EEG features indicative of ES, potentially discovering novel biomarkers, and improving diagnostic capabilities in epilepsy management.

Spectrum-based channel attention cooperating with time continuity encoding in transformer for EEG emotion analysis

Recently, electroencephalogram (EEG) emotion analysis has attracted increasing attention in many fields, such as neuroscience and brain-computer fusion. Due to the spatial channel difference and time continuity of emotional responses, it is necessary to capture relevant properties from EEG signals. In this paper, spectrum-based spatial channel attention is developed to reveal the emotional responses of different electrode channels located in different cerebral cortex regions. In addition, a time continuity encoding mechanism is developed to encode the temporal relations of time series signals in the transformer. In our model, the spectrum-based spatial channel attention cooperates with the time continuity encoding mechanism to fully utilize the spectral, spatial and temporal features of EEG signals (SST-Emo). Extensive experiments with different window sizes (1 s and 3 s) are carried out on DEAP for both binary and multiclass classification tasks. The accuracies are 96.28 %, 95.25 %, 95.52 %, 95.83 % and 93.88 % for 1 s window-size on arousal, valence, dominance, liking and 5-class, respectively. For 3 s window-size, the accuracies are 96.13 %, 95.74 %, 95.23 %, 95.7 % and 95.83 %, respectively. The results show that SST-Emo is superior to the state-of-the-art methods, which indicates that SST-Emo is able to extract more discriminative emotional representations.

Multiscale Hjorth Descriptor on Epileptic EEG Classification

The electroencephalogram (EEG) examination provides information on the brain’s electricity, especially in cases of epilepsy. Since the characteristics of EEG signals are nonlinear and nonstationary, visual inspection becomes very difficult. To overcome this problem, digital EEG signal processing was developed. Automatic epileptic EEG recognition is an area of interest on which much research focuses. The complexity approach to EEG signal analysis is interesting to be used as feature extraction, referring to the nonlinear characteristics of the signal. This study proposed an automatic epileptic EEG classification method based on the multiscale Hjorth descriptor measurement. EEG signals consisting of normal, interictal, and seizure (ictal) were simulated. The signal is scaled into new signals using the coarse-grained procedure on a scale of 1–20. Then, the Hjorth parameter which consists of activity, mobility, and complexity is calculated on the new signal. This process produces a feature vector that is used in the classification stage. Support vector machine (SVM) is used to evaluate the proposed feature extraction method. Simulation results showed that the Hjorth parameter on a scale of 1–15 yields 99.5% accuracy. The proposed method is expected to be applied to digital EEG for seizure detection and prediction.