Spectral Entropy Features Research Articles

Simultaneous EEG-fNIRS Data Classification Through Selective Channel Representation and Spectrogram Imaging.

The integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can facilitate the advancement of brain-computer interfaces (BCIs). However, existing research in this domain has grappled with the challenge of the efficient selection of features, resulting in the underutilization of the temporal richness of EEG and the spatial specificity of fNIRS data.To effectively address this challenge, this study proposed a deep learning architecture called the multimodal DenseNet fusion (MDNF) model that was trained on two-dimensional (2D) EEG data images, leveraging advanced feature extraction techniques. The model transformed EEG data into 2D images using a short-time Fourier transform, applied transfer learning to extract discriminative features, and consequently integrated them with fNIRS-derived spectral entropy features. This approach aimed to bridge existing gaps in EEG-fNIRS-based BCI research by enhancing classification accuracy and versatility across various cognitive and motor imagery tasks.Experimental results on two public datasets demonstrated the superiority of our model over existing state-of-the-art methods.Thus, the high accuracy and precise feature utilization of the MDNF model demonstrates the potential in clinical applications for neurodiagnostics and rehabilitation, thereby paving the method for patient-specific therapeutic strategies.

Wearable Sleep Monitoring System Based on Machine Learning Using Snoring Sound Signal

Abstract According to the obstructive sleep apnea Syndrome (OSAS), a wearable sleep monitoring system is designed based on machine learning using snoring sound signal. The system picks up snoring signal via bone conduction sensor, and calculates the apnea-hypopnea index (AHI). By analyzing the snoring signal in frequency domain, spectral entropy and other frequency-domain features are selected. Finally, the neural network classifier model is established. In the model, the input variables are eight frequency-domain features, and the output response is related to AHI value. Trained by machine learning, the result shows that the average accuracy in identifying the severity of the four kinds of OSAS categories is 59%. The system uses the measured data of snoring to analyze the symptoms of OSAS, so as to realize the preliminary forecast based on the snoring data. The system proposed in this paper has a good application development prospect in intelligent monitoring and medical instruments.

Features of the Earth’s seasonal hydroclimate: characterizations and comparisons across the Köppen–Geiger climates and across continents

Detailed investigations of time series features across climates, continents and variable types can progress our understanding and modelling ability of the Earth’s hydroclimate and its dynamics. They can also improve our comprehension of the climate classification systems appearing in their core. Still, such investigations for seasonal hydroclimatic temporal dependence, variability and change are currently missing from the literature. Herein, we propose and apply at the global scale a methodological framework for filling this specific gap. We analyse over 13,000 earth-observed quarterly temperature, precipitation and river flow time series. We adopt the Köppen–Geiger climate classification system and define continental-scale geographical regions for conducting upon them seasonal hydroclimatic feature summaries. The analyses rely on three sample autocorrelation features, a temporal variation feature, a spectral entropy feature, a Hurst feature, a trend strength feature and a seasonality strength feature. We find notable differences to characterize the magnitudes of these features across the various Köppen–Geiger climate classes, as well as between continental-scale geographical regions. We, therefore, deem that the consideration of the comparative summaries could be beneficial in water resources engineering contexts. Lastly, we apply explainable machine learning to compare the investigated features with respect to how informative they are in distinguishing either the main Köppen–Geiger climates or the continental-scale regions. In this regard, the sample autocorrelation, temporal variation and seasonality strength features are found to be more informative than the spectral entropy, Hurst and trend strength features at the seasonal time scale.

Driver Vigilance Detection Based on Limited EEG Signals

Driver vigilance detection is an important topic for traffic safety improvement and brain-computer interface design. Electroencephalography (EEG) can directly provide real-time information and is sensitive to the change of vigilance. Thus, EEG signals are widely used for driver vigilance detection. Previous studies commonly acquired EEG signals from a large number of electrode channels and utilized all the collected data for recognition tasks, which may introduce a large amount of noise and redundant data and limit their application potentials in practical systems. This paper examines the potential of using EEG signals from only one frequency band or from only a small subset of related electrode channels in recognizing driver vigilance state. The widely used SEED VIG dataset is adopt for experiments. Four candidate sets of EEG frequency bands and four candidate sets of EEG electrode channels are determined according to the statistical significance results of EEG power spectral density (PSD) features and differential entropy (DE) features. The experimental results show that the recognition accuracy is higher when using EEG signals from the selected frequency band (i.e., alpha band) or the selected electrodes (i.e., T7, TP7, CP1) than when using all the data. Specifically, the best recognition accuracy is 91.31% for the alpha band and 76.81% for the selected electrodes. These results indicate that higher driver vigilance recognition accuracy can be achieved with much less amount of data, which would facilitate the development of wearable equipment based on EEG signals.

EEG-Based Driver Fatigue Monitoring within a Human–Ship–Environment System: Implications for Ship Braking Safety

To address the uncontrollable risks associated with the overreliance on ship operators’ driving in current ship safety braking methods, this study aims to reduce the impact of operator fatigue on navigation safety. Firstly, this study established a human–ship–environment monitoring system with functional and technical architecture, emphasizing the investigation of a ship braking model that integrates brain fatigue monitoring using electroencephalography (EEG) to reduce braking safety risks during navigation. Subsequently, the Stroop task experiment was employed to induce fatigue responses in drivers. By utilizing principal component analysis (PCA) to reduce dimensionality across multiple channels of the data acquisition device, this study extracted centroid frequency (CF) and power spectral entropy (PSE) features from channels 7 and 10. Additionally, a correlation analysis was conducted between these features and the Fatigue Severity Scale (FSS), a five-point scale for assessing fatigue severity in the subjects. This study established a model for scoring driver fatigue levels by selecting the three features with the highest correlation and utilizing ridge regression. The human–ship–environment monitoring system and fatigue prediction model proposed in this study, combined with the ship braking model, achieve a safer and more controllable ship braking process. By real-time monitoring and prediction of driver fatigue, appropriate measures can be taken in a timely manner to ensure navigation safety and driver health.

A novel approach to automatic sleep stage classification using forehead electrophysiological signals

BackgroundSleep stage scoring is very important for the effective diagnosis and intervention of sleep disorders. However, the current automatic sleep staging methods generally have the problems of poor model generalization ability and non-portable acquisition equipment. MethodIn this paper, we propose a novel automatic sleep scoring system based on forehead electrophysiological signals that is more effective and convenient than other systems. We extract 3 channel signals from the forehead, named forehead electroencephalogram 1 (Fh1), forehead electroencephalogram 2 (Fh2), and forehead center electrooculogram (Fhz). Spectral features, statistical features, and entropy features are extracted using the discrete wavelet transform (DWT) method. Light gradient boosting machine (LGB), random forest (RF), and support vector machine (SVM) are employed to classify four stages: awake, light sleep (LS), deep sleep (DS), and rapid eye movement (REM). ResultThe performance of the proposed method is validated using databases of the Sleep-EDFX and our Own-data, which include polysomnograms (PSGs) and forehead signals of 28 subjects. The overall classification accuracy of using the combination of Fh1, Fh2, and Fhz can reach up to 90.25% accuracy with a kappa coefficient of 0.857. ConclusionsThe proposed method could provide state-of-art multichannel sleep stage scoring performance with higher portability. This will facilitate the application of long-term monitoring of sleep quality in the future.

Environmental Sound Classification Algorithm Based on Region Joint Signal Analysis Feature and Boosting Ensemble Learning

Environmental sound classification is an important branch of acoustic signal processing. In this work, a set of sound classification features based on audio signal perception and statistical analysis are proposed to describe the signal from multiple aspects of the time and frequency domain. Energy features, spectral entropy features, zero crossing rate (ZCR), and mel-frequency cepstral coefficient (MFCC) are combined to form joint signal analysis (JSA) features to improve the signal expression of the features. Then, based on the JSA, a novel region joint signal analysis feature (RJSA) for environment sound classification is also proposed. It can reduce feature extraction computation and improve feature stability, robustness, and classification accuracy. Finally, a sound classification framework based on the boosting ensemble learning method is provided to improve the classification accuracy and model generalization. The experimental results show that compared with the highest classification accuracy of the baseline algorithm, the environmental sound classification algorithm based on our proposed RJSA features and ensemble learning methods improves the classification accuracy, and the accuracy of the LightGBM-based sound classification algorithm improves by 14.6%.

Prediction of broken rotor bar in induction motor using spectral entropy features and TLBO optimized SVM

The information of the fault frequency characteristics is of great importance for all associated fault diag nostics. This requires a high-resolution spectrum analysis to achieve efficient monitoring of machinery faults, especially while diagnosing rotor bar breakage under light load conditions, because the fault frequencies almost overlap with the fundamental. In this context, rather than looking for frequencies associated with rotor faults, several frequency bands are observed separately in terms of the entropy contained within these bands. First, the motor current signal has been divided into several frequency bands using the continuous wavelet transform (CWT), and the spectral entropy is calculated from each band as the features to describe the rotor condition. Principal component analysis (PCA) has been used as a feature reduction tool, and the features projected onto the first two principal components have been fed into the SVM for inference. SVM is a supervised learning method used for classification and regression analysis. To improve classification performance, a radial basis function (RBF) kernel has been employed, and to find the optimal value of the kernel parameters, a metaheuristic approach, namely teaching learning-based optimization (TLBO), is utilized. The ANSYS 2D workbench is used to simulate the finite element model (FEM) of an induction motor with broken rotor bars, and the efficacy of the proposed method is then tested using simulated data. To investigate the robustness of the proposed approach, white Gaussian noise has also been added to the simulated data, and the performance of the SVM is tested with these spectral features.

The Effect of Time Window Length on EEG-Based Emotion Recognition.

Various lengths of time window have been used in feature extraction for electroencephalogram (EEG) signal processing in previous studies. However, the effect of time window length on feature extraction for the downstream tasks such as emotion recognition has not been well examined. To this end, we investigate the effect of different time window (TW) lengths on human emotion recognition to find the optimal TW length for extracting electroencephalogram (EEG) emotion signals. Both power spectral density (PSD) features and differential entropy (DE) features are used to evaluate the effectiveness of different TW lengths based on the SJTU emotion EEG dataset (SEED). Different lengths of TW are then processed with an EEG feature-processing approach, namely experiment-level batch normalization (ELBN). The processed features are used to perform emotion recognition tasks in the six classifiers, the results of which are then compared with the results without ELBN. The recognition accuracies indicate that a 2-s TW length has the best performance on emotion recognition and is the most suitable to be used in EEG feature extraction for emotion recognition. The deployment of ELBN in the 2-s TW can further improve the emotion recognition performances by 21.63% and 5.04% when using an SVM based on PSD and DE features, respectively. These results provide a solid reference for the selection of TW length in analyzing EEG signals for applications in intelligent systems.

Wearable EEG Entropy and Spectral Measures for Classification of Consumer Reward-based Evaluation of Odor Stimuli.

Consumer neuroscience is a rapidly emerging field, with the ability to detect consumer attitudes and states via real-time passive technologies being highly valuable. While many studies have attempted to classify consumer emotions and perceived pleasantness of olfactory products, no known machine learning approach has yet been developed to directly predict consumer reward-based decision-making, which has greater behavioral relevance. In this proof-of-concept study, participants indicated their decision to have fragrance products repeated after fixed exposures to them. Single-trial power spectral density (PSD) and approximate entropy (ApEn) features were extracted from EEG signals recorded using a wearable device during fragrance exposures, and served as subject-independent inputs for 4 supervised learning algorithms (kNN, Linear-SVM, RBF- SVM, XGBoost). Using a cross-validation procedure, kNN yielded the best classification accuracy (77.6%) using both PSD and ApEn features. Acknowledging the challenging prospects of single-trial classification of high-order cognitive states especially with wearable EEG devices, this study is the first to demonstrate the viability of using sensor-level features towards practical objective prediction of consumer reward experience.

A Novel Multivariate-Multiscale Approach for Computing EEG Spectral and Temporal Complexity for Human Emotion Recognition

This work proposes a novel multivariate-multiscale approach for computing the spectral and temporal entropies from the multichannel electroencephalogram (EEG) signal. This facilitates the recognition of three human emotions: positive, neutral, and negative. The proposed approach is based on the application of the Fourier-Bessel series expansion based empirical wavelet transform (FBSE-EWT). We have extended the existing FBSE-EWT method for multichannel signals and derived FBSE-EWT based multivariate Hilbert marginal spectrum (MHMS) for computing spectral Shannon and K-nearest neighbor (K-NN) entropies. The multivariate FBSE-EWT decomposes the multichannel EEG signals into narrow band subband signals. The multiscaling operation adapted in the spectral domain is based on the selection of successive joint instantaneous amplitude and frequency functions of the subband signals. On the other hand, the time domain multiscale K-NN entropy is computed from the cumulatively added multidimensional subband signals. The extracted spectral and temporal entropy features are smoothed and fed to the sparse autoencoder based random forest (ARF) classifier architecture for emotion classification. The proposed approach is tested using multichannel EEG signals available in a public database (SJTU emotion EEG dataset (SEED)). The bivariate EEG signals from different channel pairs with distinct spatial locations over the scalp are considered as input to our proposed system. The obtained overall classification accuracy of 94.4% reveals that the proposed approach is useful in classifying human emotions. The method is also validated using DREAMER emotion EEG public database. The method outperforms the existing state-of-the-art methods evaluated in these databases.

EEG Beta-Band Spectral Entropy Can Predict the Effect of Drug Treatment on Pain in Patients With Herpes Zoster.

Medication is the main approach for early treatment of herpes zoster, but it could be ineffective in some patients. It is highly desired to predict the medication responses to control the degree of pain for herpes zoster patients. The present study is aimed to elucidate the relationship between medication outcome and neural activity using EEG and to establish a machine learning model for early prediction of the medication responses from EEG. The authors acquired and analyzed eye-closed resting-state EEG data 1 to 2 days after medication from 70 herpes zoster patients with different drug treatment outcomes (measured 5-6 days after medication): 45 medication-sensitive pain patients and 25 medication-resistant pain patients. EEG power spectral entropy of each frequency band was compared at each channel between medication-sensitive pain and medication-resistant pain patients, and those features showing significant difference between two groups were used to predict medication outcome with different machine learning methods. Medication-sensitive pain patients showed significantly weaker beta-band power spectral entropy in the central-parietal regions than medication-resistant pain patients. Based on these EEG power spectral entropy features and a k-nearest neighbors classifier, the medication outcome can be predicted with 80% ± 11.7% accuracy, 82.5% ± 14.7% sensitivity, 77.7% ± 27.3% specificity, and an area under the receiver operating characteristic curve of 0.85. EEG beta-band power spectral entropy in the central-parietal region is predictive of the effectiveness of drug treatment on herpes zoster patients, and it could potentially be used for early pain management and therapeutic prognosis.

Tensor-Based EEG Network Formation and Feature Extraction for Cross-Session Driving Drowsiness Detection.

Drowsy driving is one of the major causes in traffic accidents worldwide. Various electroencephalography (EEG)-based feature extraction methods are proposed to detect driving drowsiness, to name a few, spectral power features and fuzzy entropy features. However, most existing studies only concentrate on features in each channel separately to identify drowsiness, making them vulnerable to variability across different sessions and subjects without sufficient data. In this paper, we propose a method called Tensor Network Features (TNF) to exploit underlying structure of drowsiness patterns and extract features based on tensor network. This TNF method first introduces Tucker decomposition to tensorized EEG channel data of training set, then features of training and testing tensor samples are extracted from the corresponding subspace matrices through tensor network summation. The performance of the proposed TNF method was evaluated through a recently published EEG dataset during a sustained-attention driving task. Compared with spectral power features and fuzzy entropy features, the accuracy of TNF method is improved by 6.7% and 10.3% on average with maximum value 17.3% and 29.7% respectively, which is promising in developing practical and robust cross-session driving drowsiness detection system.

Blind Mesh Assessment Based on Graph Spectral Entropy and Spatial Features.

With the wide applications of three-dimensional (3D) meshes in intelligent manufacturing, digital animation, virtual reality, digital cities and other fields, more and more processing techniques are being developed for 3D meshes, including watermarking, compression, and simplification, which will inevitably lead to various distortions. Therefore, how to evaluate the visual quality of 3D mesh is becoming an important problem and it is necessary to design effective tools for blind 3D mesh quality assessment. In this paper, we propose a new Blind Mesh Quality Assessment method based on Graph Spectral Entropy and Spatial features, called as BMQA-GSES. 3D mesh can be represented as graph signal, in the graph spectral domain, the Gaussian curvature signal of the 3D mesh is firstly converted with Graph Fourier transform (GFT), and then the smoothness and information entropy of amplitude features are extracted to evaluate the distortion. In the spatial domain, four well-performing spatial features are combined to describe the concave and convex information and structural information of 3D meshes. All the extracted features are fused by the random forest regression to predict the objective quality score of the 3D mesh. Experiments are performed successfully on the public databases and the obtained results show that the proposed BMQA-GSES method provides good correlation with human visual perception and competitive scores compared to state-of-art quality assessment methods.

An Effective Hybrid Model for EEG-Based Drowsiness Detection

Early detection of driver drowsiness and the development of a functioning driver alertness system may support the prevention of numerous vehicular accidents worldwide. Wearable sensors and camera-based systems are generally employed in the driver drowsiness detection. Electroencephalogram (or EEG) is considered another effective option for the driver drowsiness detection. Various EEG-based drowsiness detection systems have been proposed to date. In this paper, EEG signals are also used for the detection of drowsiness, with the proposed method being composed of three main building blocks. Both raw EEG signals and their corresponding spectrograms are used in the proposed building blocks. In the first building block, while energy distribution and zero-crossing distribution features are calculated from the raw EEG signals, spectral entropy and instantaneous frequency features are extracted from the EEG spectrogram images. In the second building block, deep feature extraction is employed directly on the EEG spectrogram images using pre-trained AlexNet and VGGNet. In the third building block, the tunable Q-factor wavelet transform (TQWT) is used to decompose the EEG signals into related sub-bands. The spectrogram images of the obtained sub-bands and statistical features, such as mean and standard deviation of the sub-bands’ instantaneous frequencies, are then calculated. Each feature group from each building block is fed to a long-short term memory (LSTM) network for the purposes of classification. The obtained results from the LSTM networks are then fused with a majority voting layer. The MIT-BIH Polysomnographic database was used in the experimental works. The evaluation of the proposed method was carried out with ten-fold cross validation test and the average accuracy represented accordingly. The obtained average accuracy score was 94.31%. The obtained result was also compared with other results to be found in the literature. The comparison shows that the proposed method’s achievement was found to be better than the compared results.

Dysfluency Recognition by using Spectral Entropy Features

In recent decades, speech recognition technology has improved effectively and significantly, but it is restricted only for a stream of words. These recognition systems assist human’s effectively for structured speech but for unstructured speech this assistance is not so effective for humans to communicate with machines, because unstructured stream of word lacks in providing useful information about pronunciation and punctuation. Recovering of such structural information by detecting the position of each phones in a sentence by locating the sentence boundaries, repeated words and missing phones in each phrase. The proposed work investigates the spectral entropy features, for the automatic detection of voiced and non-voiced regions, in the process of dysfluent speech recognition. The entropy features are estimated by normalizing the Fourier transform spectrum as Probability mass function (PMF). For clear formants of speech, the value of entropy is low and the value of entropy is high for flat distribution of silence part or if there is any noise in speech sample. A comparison of entropy features with Word Error Rate is presented in the proposed work.

Novel Nonlinear Approach for Real-Time Fatigue EEG Data: An Infinitely Warped Model of Weighted Permutation Entropy

Workload assessment faces two major issues. That is, how to learn effective fatigue characteristics and how to find the potential state of the workload. This paper proposes a solution to assess the brain fatigue workload of pilots through an instantaneous spectral entropy feature and an infinitely warped model. The instantaneous characteristics of electroencephalography (EEG) signals are extracted by Hilbert transform, and Euclidean norm weighted permutation entropy is proposed. The infinitely warped model is a new automatic learning model for detecting arbitrary shapes of EEG data. In addition, we propose a rapid learning framework to learn mental fatigue by integrating Treelet transform and infinitely warped models. Compared to other state-of-the-art methods, our approach is better able to handle complex data in complex shapes. The experimental results show that this method can more effectively assess the brain fatigue of pilots.

Driving Fatigue Classification Based on Fusion Entropy Analysis Combining EOG and EEG

The rising number of traffic accidents has become a major issue in our daily life, which has attracted the concern of society and governments. To deal with this issue, in our previous study, we have designed a real-time driving fatigue detection system using power spectrum density and sample entropy. By using the wireless technology and dry electrodes for EEG collection, we further integrated virtual reality simulated driving environment, which made our study more applicable to realistic settings. However, the high accuracy of classification for driving fatigue has not been obtained. To measure the time series complexity of the EEG signal, we proposed a fusion entropy (sample entropy, approximate entropy, and spectral entropy) analysis method of EEG and EOG. First, a sample entropy was applied for feature extraction from the horizontal and vertical EOG. Second, an approximate entropy, sample entropy, and spectral entropy features of each sub-band of EEG are extracted. Third, feature fusion for sub-band is performed by canonical correlation analysis (CCA). Finally, the features of EOG and EEG are classified using a relevant vector machine (RVM). Twenty-two subjects participated in the driving fatigue experiments for a duration of 90 min. The results demonstrated that the fusion entropy analysis combining EOG and EEG could provide an alternative method for driving fatigue detection, and the average accuracy rate was up to 99.1 ± 1.2%. The authors further analyzed the effect of feature fusion in four sub-bands (δ, α, β and θ) and compared with every single sub-band on classification performance, it is proved that the former is superior to the latter presenting the proposed method can provide effective indicators for driving fatigue detection.

Analysis of Feature Fatigue EEG Signals Based on Wavelet Entropy

Fatigue driving is bringing more and more serious harm, but there are various reasons for fatigue driving, it is still difficult to test the driver’s fatigue. This paper defines a method to test driver’s fatigue based on the EEG, and different from other researches into fatigue driving, this paper mainly takes the fatigue features of EEG signals in fatigue state and uses wavelet entropy as the feature extraction method to analyze the features of wavelet entropy and spectral entropy features as well as the classification accuracy under the same classifier. The SVM is used to show the classifier’s results. The accuracy of the driver fatigue state monitoring using the wavelet entropy is 90.7%, which is higher than the use of spectral entropy as the characteristic accuracy rate of 81.3%. The results show that the frequency characteristics of EEG can be well applied to driving fatigue testing, but different frequency feature calculation methods will affect the classification accuracy.

Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound images

Ultrasound imaging is one of the most common visualizing tools used by radiologists to identify the location of thyroid nodules. However, visual assessment of nodules is difficult and often affected by inter- and intra-observer variabilities. Thus, a computer-aided diagnosis (CAD) system can be helpful to cross-verify the severity of nodules. This paper proposes a new CAD system to characterize thyroid nodules using optimized multi-level elongated quinary patterns. In this study, higher order spectral (HOS) entropy features extracted from these patterns appropriately distinguished benign and malignant nodules under particle swarm optimization (PSO) and support vector machine (SVM) frameworks. Our CAD algorithm achieved a maximum accuracy of 97.71% and 97.01% in private and public datasets respectively. The evaluation of this CAD system on both private and public datasets confirmed its effectiveness as a secondary tool in assisting radiological findings.