Time Domain Feature Extraction Research Articles

Enhanced frequency and time domain feature extraction for communication infrastructure type classification using optical fiber sensing

We present a feature extraction method for a feedforward-type neural network (FNN) designed to realize a distributed acoustic sensing (DAS) technique that suits conventional optical fiber. This is, to the best of our knowledge, the first trial in which an FNN is used to interpret the field communication infrastructure type surrounding optical cables. Three classes are taken to represent the field environment: the cable tunnel (class 1), circular duct (class 2), and overhead area (class 3). We investigate and compare frequency- and time-domain feature extraction. We also show that the frequency-domain features yielded by spectral envelope shape (SES) processing have better performance than simple fast Fourier transform features. Two types of time-domain features are verified: one is the short-time maximum magnitude (STMM), which shows the largest value in the time frame, and the other is the short-time average magnitude (STAM), which indicates the average value in a time frame. Note that all features are optimized for multi-class classification. In this paper, we present the suitable number of both features and the number of training iterations. An accuracy rate of 79.0% is achieved using FNN analysis with the features studied here. Furthermore, by considering the similarity of neighboring classes, classes are refined into higher probability classes. As a result, accuracy is improved to 87.2%.

Apnoea detection using ECG signal based on machine learning classifiers and its performances

Sleep apnoea is a common disorder affecting sleep quality by obstructing the respiratory airway. This disorder can also be correlated to certain diseases like stroke, depression, neurocognitive disorder, non-communicable disease, etc. We implemented machine learning techniques for detecting sleep apnoea to make the diagnosis easier, feasible, convenient, and cost-effective. Electrocardiography signals are the main input used here to detect sleep apnoea. The considered ECG signal undergoes pre-processing to remove noise and other artefacts. Next to pre-processing, extraction of time and frequency domain features is carried out after finding out the R-R intervals from the pre-processed signal. The power spectral density is calculated by using the Welch method for extracting the frequency-domain features. The extracted features are fed to different machine learning classifiers like Support Vector Machine, Decision Tree, k-nearest Neighbour, and Random Forest, for detecting sleep apnoea and performances are analysed. The result shows that the K-NN classifier obtains the highest accuracy of 92.85% compared to other classifiers based on 10 extracted features. The result shows that the proposed method of signal processing and machine learning techniques can be reliable and a promising method for detecting sleep apnoea with a reduced number of features.

Online BCI systems: cross-subject motor imagery classification based on weighted time-domain feature extraction methods

Motor imagery electroencephalogram (MI-EEG) is becoming increasingly important. This paper solves the problem of online signal recognition for motor imagery across subjects by finding common features across multiple subjects to improve the generality of the classification model. We analysed the EEG data from left/right-hand motor imagery of eight subjects and proposed a weighted time-domain (WTD) feature extraction method based on a weighted channel screening method. The classification model constructed by combining this feature extraction method with the support vector machine (SVM) classification method was faster in classification and achieved good cross-subject classification accuracy (The average offline classification accuracy was 91.39%). In this paper, an online control system for asynchronous brain-controlled wheelchairs was built with good performance. The online average motor imagery classification accuracy was 81.67%, and the average response time was 1.36s. This method contributes to bringing the online Brain-computer interface (BCI) system out of the laboratory and into wider application.

Pattern recognition for EMG based forearm orientation and contraction in myoelectric prosthetic hand

Communication is an essential component of human nature. It connects humans, allowing them to learn, grow, col-laborate, and resolve conflicts. Several aspects of human society, relationships, and growth would be significantly hampered in the absence of efficient communication. Hand gesture recognition is a way to interact with technology that can be particularly useful for individuals with disabilities. This hand gesture recognition is mainly employed in sign language translation, healthcare, rehabilitation, prosthesis, and Human-Computer Interaction (HCI). The high degree of dexterity is a main challenge for prosthetic limbs. In order to meet this challenge, hand gesture recognition is employed for the prosthetic limb, which can be used for rehabilitation. The objective of this article is to show the methodology for the recognition of hand gestures using Electromyography (EMG) signals. This article uses the pro-posed time domain feature extraction method called Absolute Fluctuation Analysis (AFA) along with the Root Mean Square (RMS) for the feature extraction method. Along with these feature extraction methods, repeated stratified K-fold cross validation is used for the validation of the classifiers such as the XGB classifier, the K-Nearest Neighbour (KNN) classifier, the Decision Tree classifier, the Random Forest classifier, and the SVM classifier, whose mean recognition accuracy is given by 93.26%, 87.42%, 85.26%, 92.23%, and 91.78%, respectively. The recognition accuracy of machine learning classifiers is being compared with state-of-the-art networks such as artificial neural net-works (ANN), long short-term memory (LSTM), bidirectional LSTM, gated recurrent units (GRU), and convolution-al neural networks (CNN), which provide recognition accuracy of 96.65%, 99.16%, 99.94%, and 99.99%, respectively.

A hybrid denoising approach for PPG signals utilizing variational mode decomposition and improved wavelet thresholding.

Photoplethysmography (PPG) signals are sensitive to motion-induced interference, leading to the emergence of motion artifacts (MA) and baseline drift, which significantly affect the accuracy of PPG measurements. The objective of our study is to effectively eliminate baseline drift and high-frequency noise from PPG signals, ensuring that the signal's critical frequency components remain within the range of 1 ∼ 10Hz. This paper introduces a novel hybrid denoising method for PPG signals, integrating Variational Mode Decomposition (VMD) with an improved wavelet threshold function. The method initially employs VMD to decompose PPG signals into a set of narrowband intrinsic mode function (IMF) components, effectively removing low-frequency baseline drift. Subsequently, an improved wavelet thresholding algorithm is applied to eliminate high-frequency noise, resulting in denoised PPG signals. The effectiveness of the denoising method was rigorously assessed through a comprehensive validation process. It was tested on real-world PPG measurements, PPG signals generated by the Fluke ProSim™8 Vital Signs Simulator with synthesized noise, and extended to the MIMIC-III waveform database. The application of the improved threshold function let to a substantial 11.47% increase in signal-to-noise ratio (SNR) and an impressive 26.75% reduction in root mean square error (RMSE) compared to the soft threshold function. Furthermore, the hybrid denoising method improved SNR by 15.54% and reduced RMSE by 37.43% compared to the improved threshold function. This study proposes an effective PPG denoising algorithm based on VMD and an improved wavelet threshold function, capable of simultaneously eliminating low-frequency baseline drift and high-frequency noise in PPG signals while faithfully preserving their morphological characteristics. This advancement establishes the foundation for time-domain feature extraction and model development in the domain of PPG signal analysis.

Attention-Based Multimodal tCNN for Classification of Steady-State Visual Evoked Potentials and Its Application to Gripper Control.

The classification problem for short time-window steady-state visual evoked potentials (SSVEPs) is important in practical applications because shorter time-window often means faster response speed. By combining the advantages of the local feature learning ability of convolutional neural network (CNN) and the feature importance distinguishing ability of attention mechanism, a novel network called AttentCNN is proposed to further improve the classification performance for short time-window SSVEP. Considering the frequency-domain features extracted from short time-window signals are not obvious, this network starts with the time-domain feature extraction module based on the filter bank (FB). The FB consists of four sixth-order Butterworth filters with different bandpass ranges. Then extracted multimodal features are aggregated together. The second major module is a set of residual squeeze and excitation blocks (RSEs) that has the ability to improve the quality of extracted features by learning the interdependence between features. The final major module is time-domain CNN (tCNN) that consists of four CNNs for further feature extraction and followed by a fully connected (FC) layer for output. Our designed networks are validated over two large public datasets, and necessary comparisons are given to verify the effectiveness and superiority of the proposed network. In the end, in order to demonstrate the application potential of the proposed strategy in the medical rehabilitation field, we design a novel five-finger bionic hand and connect it to our trained network to achieve the control of bionic hand by human brain signals directly. Our source codes are available on Github: https://github.com/JiannanChen/AggtCNN.git.

A FUSION OF A DISCRETE WAVELET TRANSFORM-BASED AND TIME-DOMAIN FEATURES EXTRACTION FOR MOTOR IMAGERY CLASSIFICATION

A motor imagery (MI)-based brain-computer interface (BCI) has performed successfully as a control mechanism with multiple electroencephalogram (EEG) channels. For practicality, fewer EEG channels are preferable. This paper investigates a single-channel EEG signal for MI. However, there are insufficient features that can be extracted due to a single-channel EEG signal being used in one region of the brain. An effective feature extraction technique plays a critical role in overcoming this limitation. Therefore, this study proposes a fusion of discrete wavelet transform (DWT)-based and time-domain feature extraction to provide more relevant information for classification. The highest accuracy obtained on the BCI Competition III (IVa) dataset is 87.5% with logistic regression (LR) while the OpenBMI dataset attained the highest accuracy of 93% with support vector machine (SVM) as the classifier. Addressing the potential of enhancing the performance of a single EEG channel located on the forehead, the achieved result is relatively promising.

Current-Signal-Based Fault Diagnosis of Railway Point Machines Using Machine Learning

The majority of railway operators still implement conventional maintenance for railway point machines (RPMs), which is one of the most vital pieces of equipment for ensuring the safety of train operation. The conventional maintenance method lacks accuracy, is less efficient, and has high labor costs. This study developed a cost-effective and accurate fault diagnosis (FD) method based on current data to increase the overall efficiency of RPM maintenance. The FD method for RPM equipment discussed in this paper consists of three working conditions: normal, working, and failure. The method was proposed based on time-series current signals, which were gathered when the RPM was in operation. Time-series data were extracted and filtered using time-domain feature extraction based on scalable hypothesis testing. The selected features became the datasets for machine learning modeling. Six machine learning algorithms were compared in order to find the algorithm with the best FD accuracy. The results showed 100% accuracy for the Decision Tree and Random Forest algorithms in the FD method. The results of the FD method could be important for maintenance teams in determining suitable maintenance activities based on RPM working conditions.

Research Progress on Identification and Suppression Methods for Monitoring the Cavitation State of Centrifugal Pumps

Cavitation is a detrimental phenomenon in hydraulic machinery, adversely impacting its performance, inducing vibration and noise, and leading to corrosion damage of overflow components. Centrifugal pump internal cavitation will lead to severe vibration and noise, and not only will the performance of hydraulic machinery be adversely affected but the impact generated by the collapse of the vacuole will also cause damage to the impeller wall structure, seriously affecting the safety of the equipment’s operation. To prevent the generation and development of internal cavitation in centrifugal pumps, to prevent the hydraulic machinery from being in a state of cavitation for a long time, to avoid the failure of the unit, and to realize the predictive maintenance of centrifugal pumps, therefore, it is of great significance to research the methods for monitoring the cavitation of hydraulic machinery and the methods for suppressing the cavitation. This paper comprehensively describes the centrifugal pump cavitation mechanism and associated hazards. It also discusses the current state of centrifugal pump cavitation monitoring methods, including commonly used approaches such as the flow-head method, high-speed photography, pressure pulsation method, acoustic emission method, and vibration method. A comparative analysis of these methods is presented. Additionally, the paper explores signal characterization methods for centrifugal pump cavitation, including time-domain feature extraction, frequency-domain feature extraction, and time–frequency-domain feature extraction. The current research status is elaborated upon. Moreover, the paper presents methods to mitigate cavitation and prevent its occurrence. Finally, it summarizes the ongoing research on identifying and determining the cavitation state in centrifugal pumps and offers insights into future research directions.

Utilizing a knowledge-based training algorithm and time-domain extraction for pattern recognition in cylindrical features through vibration and sound signals

This study presents a new solution to address challenges encountered in additive manufacturing, specifically in the context of 3D printing, where failures can occur due to complications associated with the nozzle or filament. The proposed solution in this research involves using a time-domain feature extraction method that leverages sound and vibration patterns. By implementing sensors to capture these signals in a controlled and noise-free environment, and then utilizing a Multi-Layer Perceptron (MLP) model trained accurately to predict upcoming signals and vibrations, proactive anticipation of printing outcomes is facilitated, including potential failures. Simulation results obtained using MATLAB for the MLP showcase the effectiveness of this approach, demonstrating remarkably low error rates. Furthermore, through rigorous data validation, the proposed method's ability to accurately identify sound and vibration signals is confirmed. As a result, the likelihood of failures is significantly reduced, thereby preventing defects in the filament. The implications of this solution hold great promise in substantially enhancing the reliability and efficiency of additive manufacturing processes.

A communication-less islanding detection scheme for hybrid distributed generation systems using recurrent neural network

The proposed scheme in this research paper is a communication-less islanding detection system based on recurrent neural network (RNN) for hybrid distributed generator (DG) systems that include both synchronous and inverter-based DG devices. The scheme consists of three stages: time-domain feature extraction (FE) from the three-phase voltage signal at the point of common coupling (PCC), feature selection using the wrapper method, and detection using a long short-term memory (LSTM) RNN algorithm. To assess the performance of the proposed scheme, a comprehensive set of islanding and grid-connected scenarios was created using the IEC 61850-7-420 test system in MATLAB/Simulink. After feature extraction in MATLAB, the feature selection and LSTM model training/testing steps were conducted in Python. Thirteen features were extracted from the voltage signal, and the most significant features were selected for detection using the wrapper method. The detection process encompassed various events, such as line faults, load switching, and capacitor switching, occurring in both islanded and grid-connected modes. The proposed scheme was compared against existing techniques, artificial neural networks, and support vector machines under the same conditions. The proposed scheme demonstrated excellent accuracy (99.68 %), selectivity (99.44 %), and sensitivity (100 %) under normal conditions with all features. It also performed well in noisy environments, with accuracies of 98.94 % and 97.04 % with 40 dB and 30 dB noise, respectively. This research highlights the importance of quality and quantity of data for training intelligent RNN-based IDSs and demonstrates the effectiveness of the proposed scheme in detecting islanding in hybrid DG systems.

Classification of present faults in rotating machinery based on time and frequency domain feature extraction

The need for an effective and efficient maintenance process increases with the level of complexity of modern rotating machinery. This paper introduces a methodology for transforming raw vibration signals into adequate inputs for machine learning classification algorithms in order to identify present faults in rotating machinery. It complements a previous study by the same authors, which covers the processing of vibrational signals by determining the optimal sampling frequency and using appropriate filters for the raw data. The first part of this study covers feature extraction using time and frequency-domain techniques and correlation matrices are plotted to determine which extracted features are significantly connected and what is the level of their correlation. The study continues with the use of Neighborhood Component Analysis (NCA) where weight factors of the features are calculated in terms of recognizing the present rotating machinery faults. Only the ones with the highest level of importance have been used as input for the classification algorithms. The MATLAB Add-in Classification Learner has been used for training and testing various classification algorithms. K-nearest neighbors classifier (KNN), Support vector machines (SVM), and Wide Neural Network (NN) showed the highest accuracy in distinguishing ten different fault conditions. For this case study, the MaFaulda vibration dataset has been used and ten operating conditions have been considered: normal, imbalance, horizontal misalignment, vertical misalignment, and three faults in the underhang bearing and the overhang bearing.

Machine Learning Technique to Classify EMG Signal for Diabetes Person

Diabetes can cause a disease known as diabetic peripheral neuropathy (DPN), which affects the blood vessels and nerves in the legs and feet. This condition can result in plantar foot ulcers and muscular weakness. Detecting DPN early stage is crucial so patients can receive early treatment before their disease worsens. Most technology that detects this disease is usually expensive, like an Electromyography machine (EMG). But, with the increasing popularity of machine learning classification in the health sciences, DPN can be identified early by producing a low-cost equipment. This study aimed to develop a low-cost surface EMG (sEMG) system to detect electrical activity in the lower limb muscles and classify healthy and diabetic subjects during muscle fatigue using K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN) as two methods of machine learning technique. This study used Muscle Sensor V3 as sEMG to record the signal and extract using time domain feature extraction before classification. In KNN, 1–10 values (K) are used, while in ANN, 1–10 values of the number of hidden neurons are used to compare the classification performance. The result shows that ANN is suitable compared to KNN with the method used in this study. ANN algorithm performs better using four hidden neurons with an accuracy of 100% for the training and testing process. The study can conclude that this low-cost sEMG system, with the help of ANN, can effectively classify two subjects (healthy and diabetic) according to the EMG data obtained. This system can help to identify any diabetes neuropathy at an early stage and able to prevent lower limb complications related to the disease.

Machine learning based hybrid anomaly detection technique for automatic diagnosis of cardiovascular diseases using cardiac sympathetic nerve activity and electrocardiogram.

Coronary artery diseases (CADs) are the leading cause of death worldwide and early diagnosis is crucial for timely treatment. To address this, our study presents a novel automated Artificial Intelligence (AI)-based Hybrid Anomaly Detection (AIHAD) technique that combines various signal processing, feature extraction, supervised, and unsupervised machine learning methods. By jointly and simultaneously analyzing 12-lead cardiac sympathetic nerve activity (CSNA) and electrocardiogram (ECG) data, the automated AIHAD technique performs fast, early, and accurate diagnosis of CADs. In order to develop and evaluate the proposed automated AIHAD technique, we utilized the fully labeled STAFF III and PTBD databases, which contain the 12-lead wideband raw recordings non-invasively acquired from 260 subjects. Using these wideband raw recordings, we developed a signal processing technique that simultaneously detects the 12-lead CSNA and ECG signals of all subjects. Using the pre-processed 12-lead CSNA and ECG signals, we developed a time-domain feature extraction technique that extracts the statistical CSNA and ECG features critical for the reliable diagnosis of CADs. Using the extracted discriminative features, we developed a supervised classification technique based on Artificial Neural Networks (ANNs) that simultaneously detects anomalies in the 12-lead CSNA and ECG data. Furthermore, we developed an unsupervised clustering technique based on Gaussian mixture models (GMMs) and Neyman-Pearson criterion, which robustly detects outliers corresponding to CADs. Using the automated AIHAD technique, we have, for the first time, demonstrated a significant association between the increase in CSNA signals and anomalies in ECG signals during CADs. The AIHAD technique achieved highly reliable detection of CADs with a sensitivity of 98.48 %, specificity of 97.73 %, accuracy of 98.11 %, positive predictive value of 97.74 %, negative predictive value of 98.47 %, and F1-score of 98.11 %. Hence, the automated AIHAD technique demonstrates superior performance compared to the gold standard diagnostic test ECG in the diagnosis of CADs. Additionally, it outperforms other techniques developed in this study that separately utilize either only CSNA data or only ECG data. Therefore, it significantly increases the detection performance of CADs by taking advantage of the diversity in different data types and leveraging their strengths. Furthermore, its performance is comparatively better than that of most previously proposed machine and deep learning methods that exclusively used ECG data to diagnose or classify CADs. Additionally, it has a very low implementation time, which is highly desirable for real-time detection of CADs. The proposed automated AIHAD technique may serve as an efficient decision-support system to increase physicians' success in fast, early, and accurate diagnosis of CADs. It may be highly beneficial and valuable, particularly for asymptomatic patients, for whom the diagnostic information provided by ECG alone is not sufficient to reliably diagnose the disease. Hence, it may significantly improve patient outcomes by enabling timely treatments and considerably reducing the mortality of cardiovascular diseases (CVDs).

Ultrasonic Detection of Aliased Signal Separation Based on Adaptive Feature Dictionary and K–SVD Algorithm for Protective Coatings of Assembled Steel Structure

When using ultrasound to detect the thickness of protective coatings on assembled steel structures, the coatings are extremely thin, which can cause echo signals to overlap and impair the detection accuracy. Therefore, the study of the separation of the superimposed signals is essential for the precise measurement of the thickness of thinner coatings. A method for signal time domain feature extraction based on an adaptive feature dictionary and K–SVD is investigated. First, the wavelet transform, which is sensitive to singular signal values, is used to identify the extreme values of the signal and use them as the new signal to be processed. Then, the feature signal extracted by wavelet transform is transformed into Hankel matrix form, and the initial feature dictionary is constructed by period segmentation and random extraction. The optimized feature dictionary is subsequently obtained by enhancing the K–SVD algorithm. Finally, the time domain signal is reconstructed using the optimized feature dictionary. Simulations and experiments demonstrate that the method is more accurate in separating mixed signals and extracting signal time domain feature information than the conventional wavelet transform and Gabor dictionary-based MP algorithm, and that it is more advantageous in detecting the thickness of protective coatings.

Detection of Simultaneous Bearing Faults Fusing Cross Correlation With Multikernel SVM

Detection of simultaneous bearing faults for condition monitoring (CM) of bearings using time-domain analysis is quite challenging and open area particularly in noisy environment. This work presents a new scheme for simultaneous bearing fault detection using vibration signal, in cases where single-point localized bearing fault and multiple-point compound fault coexist. Bearings of a 415 V, 3kW, 3-phase Squirrel Cage Induction Motor (SCIM) have been used for data collection, while the loading arrangement is done using a 110V, 4kW DC generator connected with a load box and coupled to the motor. A cross-correlation-based time-domain feature extraction approach has been introduced. The neighborhood component analysis (NCA) technique has been applied to the cross-correlation-based features to reduce the complexity of the proposed model. Furthermore, the selected features have been fed into a multi-kernel support vector machine (MKSVM) to classify simultaneous bearing faults. This method has also been tested on signals contaminated with white Gaussian noise to verify reliability in the industrial environment. It is found that with only five features, the proposed model yields 100% classification performance metrics for raw signal and under noisy environments with signal-to-noise ratio (SNR) of 20 dB to 50 dB for both full load and no-load conditions. Whereas at 10 dB SNR value, performance decreases slightly, still an overall classification performance metrics of more than 99% is achieved by this method. Furthermore, this method has enhanced performance when compared to earlier studies with publicly available databases for localized bearing failure identification.

A Light-Weight Artificial Neural Network for Recognition of Activities of Daily Living

Human activity recognition (HAR) is essential for the development of robots to assist humans in daily activities. HAR is required to be accurate, fast and suitable for low-cost wearable devices to ensure portable and safe assistance. Current computational methods can achieve accurate recognition results but tend to be computationally expensive, making them unsuitable for the development of wearable robots in terms of speed and processing power. This paper proposes a light-weight architecture for recognition of activities using five inertial measurement units and four goniometers attached to the lower limb. First, a systematic extraction of time-domain features from wearable sensor data is performed. Second, a small high-speed artificial neural network and line search method for cost function optimization are used for activity recognition. The proposed method is systematically validated using a large dataset composed of wearable sensor data from seven activities (sitting, standing, walking, stair ascent/descent, ramp ascent/descent) associated with eight healthy subjects. The accuracy and speed results are compared against methods commonly used for activity recognition including deep neural networks, convolutional neural networks, long short-term memory and convolutional–long short-term memory hybrid networks. The experiments demonstrate that the light-weight architecture can achieve a high recognition accuracy of 98.60%, 93.10% and 84.77% for seen data from seen subjects, unseen data from seen subjects and unseen data from unseen subjects, respectively, and an inference time of 85 μs. The results show that the proposed approach can perform accurate and fast activity recognition with a reduced computational complexity suitable for the development of portable assistive devices.

Fingerprint feature and dynamic threshold mechanism based on acoustic emission for bearing fault detection

The acoustic emission (AE) technology has emerged as a promising diagnostic tool for the damage detection of bearings. However, the conventional time-domain feature extraction based on AE hits lacks adequate consideration of the periodicity related to faults. To address this issue, taking the outer ring of a bearing as an example and studying the periodic AE hit behavior of bearings, a condition monitoring method, called fingerprint feature (FPF), is established in this study, providing a visual mode for bearing fault detection, along with the ability to track the bearing state. Since the dynamic threshold is essential to the formation of FPF, the dynamic threshold mechanism is investigated in detail, and a threshold coefficient Rt is defined to obtain the adaptive dynamic threshold, which allows instant AE hit extraction. The proposed distributed aggregation index can not only optimize Rt but also help evaluate the damage state of bearings. In addition, based on the FPF, a fingerprint fault spectrum (FFS) is proposed by associating the hit statistics per unit time with the fault characteristic frequency. Finally, the validity of the proposed method is proved by Hilbert envelope demodulation and its capability and practicality are verified using test rig data of rolling bearings and high-speed train bearings that operate close to actual working conditions. This study provides valuable insights into the development of online condition monitoring for bearings in industrial environment.

A novel abnormal data detection method based on dynamic adaptive local outlier factor for the vibration signals of rotating parts

Abnormal signals are inevitable in big data acquired from harsh industrial environments. Abnormal data detection is a crucial component of condition monitoring for rotating parts and is also the premise of data cleaning, compensation, and mining. To detect abnormal data segments of rolling bearings, this paper proposes a dynamic adaptive local outlier factor (DALOF) anomaly detection method. First, a data dynamic segmentation method based on sliding windows is designed to determine samples with variable lengths. Then, a time-domain feature extraction and fusion method based on principal component analysis is exploited to reduce the feature space discrepancy. To improve the accuracy of abnormal data detection, a data quality evaluation model is established to assess each data segment using DALOF. The validity of the proposed method is also verified by analyzing signals including missing data, random interference data, and drift data. Several other methods are respectively applied to identify these abnormal data to further demonstrate the benefits of the developed methodology.

Design of subject independent 3D VAD emotion detection system using EEG signals and machine learning algorithms

This work aims to develop a subject-independent Emotion Detection System (EDS) based on EEG signals and the 3D Valence-Arousal-Dominance (VAD) model. The DEAP database physiological signals are considered for the system design. A multi-domain feature extraction is performed using the Wavelet-based Atomic Function time–frequency domain technique; and various time and frequency domain feature extraction techniques. Further, principal component analysis reduces the data dimensionality and redundancy in the obtained feature set. The minimal feature set is analysed using machine learning classifiers, i.e., gradient boosting, decision tree, and random forest. Additionally, the hyperparameters of machine learning algorithms are tuned using Optuna to improve the performance of the proposed model. Three EDS are designed in this work; EDS1 considers the 3D VAD model for three class classifications. 2D VA model is used in EDS2 to determine 9 discrete emotions, and EDS3 detects 12 discrete emotions using the 3D VAD model. Results reveal the highest classification accuracy of about 99% with EDS1, whereas an average accuracy of 99.82% and 98.44% is obtained with EDS2 and EDS3, respectively. The results reveal that both EDS1 and EDS2 show improvement as compared to the existing literature. Also, the proposed EDS3 provides the classification of the highest number of discrete emotions, which may facilitate the design of an efficient human–computer interface system.