Feature Extraction Of EEG Signals Research Articles

Lightweight Privacy-Preserving Feature Extraction for EEG Signals Under Edge Computing

Lightweight Privacy-Preserving Feature Extraction for EEG Signals Under Edge Computing

An IoT-Based Intelligent Selection of Multidomain Feature for Smart Healthcare Using Reinforcement Learning in Schizophrenia

Currently, the health industry by the Internet of Things (IoT) is developing rapidly, and Electroencephalogram (EEG) signal has become a bridge for human-machine communication. EEG signal feature selection is a key link in brain nerves research and has important practical application value. Therefore, we propose an IoT-based Intelligent Selection of Multi-domain Feature for multimodal transformer EEG signal using reinforcement learning in Schizophrenia. First of all, the features of the multimodal EEG signal sequence by the IoT were extracted from time domain, frequency domain, and spatial domain; Second, the average pooling layer was added to improve the design of the transformer model for deep feature extraction of EEG signal; finally, reinforcement learning was introduced to run operation with the extracted features as input and the improved transformer model as agent. At the same time, entropy and Pearson correlation coefficient calculation were introduced to select feature subsets, and feature intelligent selection of EEG signal in multiple domains was completed through interaction between the agent and the environment. Experiments were performed on DEAP, EEG Motor Movement/Imagery Data set, BCI2008 competition data set and data set of a hospital. The results show that the feature extraction and feature visualization effect of the proposed algorithm is good, and the feature selection precision is as high as about 90%, and the average time is only about 13 seconds. These findings indicate the feasibility of the proposed feature intelligent selection algorithm in the study of EEG signal in schizophrenia, which provides a good basis for further study on the integration of IoT with the healthy industry.

A Study on Second Language Academic Writing Anxiety of Chinese Students Studying in Japan

Abstract Anxiety is an important factor that affects the academic writing process and the quality of writing. In this paper, we first study the principle of emotional processing of EEG signals and discuss the pre-processing, feature extraction and emotional recognition of EEG signals. Then the signal features are extracted based on the EEG features of Daubechies wavelets, and the Mallat algorithm is improved by using the half-wavelet packet algorithm. The improved Mallat algorithm is then used to perform data downscaling of the resulting feature dimensions to achieve classification operations and feature selection. Finally, the model was used to assess the second language academic writing anxiety of college students in Japan, and the levels of second language academic writing anxiety of students in Japan with different levels of a second language, length of exposure to a second language, gender, and grade were analyzed. The mean score of academic writing anxiety was 68.04, and the overall level of academic writing anxiety among international students in Japan was moderate. The mean anxiety level of international students with a Level 1 second language level was 2.9, the mean anxiety level of international students with a Level 2 second language level was 3.07, and the mean anxiety level of international students with a Level 5 fifth language level was 3.15. The lower the second language level, the higher the anxiety level. This study provides insight and references for improving the academic writing skills of international students.

Research on Mental Workload of Deep-Sea Oceanauts Driving Operation Tasks from EEG Data.

A person's present mental state is closely associated with the frequency and temporal domain features of spontaneous electroencephalogram (EEG) impulses, which directly reflect neurophysiological signals of brain activity. EEG signals are employed in this study to measure the mental workload of drivers while they are operating a vehicle. A technique based on the quantum genetic algorithm (QGA) is suggested for improving the kernel function parameters of the multi-class support vector machine (MSVM). The performance of the algorithm based on the quantum genetic algorithm is found to be superior to that of other ways when other methods and the quantum genetic algorithm are evaluated for the parameter optimization of kernel function via simulation. A multi-classification support vector machine based on the quantum genetic algorithm (QGA-MSVM) is applied to identify the mental workload of oceanauts through the collection and feature extraction of EEG signals during driving simulation operation experiments in a sea basin area, a seamount area, and a hydrothermal area. Even with a limited data set, QGA-MSVM is able to accurately identify the cognitive burden experienced by ocean sailors, with an overall accuracy of 91.8%.

Brain–Computer-Interface-Based Smart-Home Interface by Leveraging Motor Imagery Signals

In this work, we propose a brain–computer-interface (BCI)-based smart-home interface which leverages motor imagery (MI) signals to operate home devices in real-time. The idea behind MI-BCI is that different types of MI activities will activate various brain regions. Therefore, after recording the user’s electroencephalogram (EEG) data, two approaches, i.e., Regularized Common Spatial Pattern (RCSP) and Linear Discriminant Analysis (LDA), analyze these data to classify users’ imagined tasks. In such a way, the user can perform the intended action. In the proposed framework, EEG signals were recorded by using the EMOTIV helmet and OpenVibe, a free and open-source platform that has been utilized for EEG signal feature extraction and classification. After being classified, such signals are then converted into control commands, and the open communication protocol for building automation KNX (“Konnex”) is proposed for the tasks’ execution, i.e., the regulation of two switching devices. The experimental results from the training and testing stages provide evidence of the effectiveness of the users’ intentions classification, which has subsequently been used to operate the proposed home automation system, allowing users to operate two light bulbs.

Feature Extraction of Motor Imagery EEG via Discrete Wavelet Transform and Generalized Maximum Fuzzy Membership Difference Entropy: A Comparative Study

Identifying motor imagery (MI) electroencephalogram (EEG) is an important way to achieve brain–computer interface (BCI), but its applicability is heavily dependent on the performance of feature extraction procedure. In this paper, a feature extraction method based on generalized maximum fuzzy membership difference entropy (GMFMDE) and discrete wavelet transform (DWT) was proposed for the feature extraction of EEG signals. The influence of different distance calculation methods, embedding dimensions and tolerances were studied to find the best configuration of GMFMDE for the feature extraction of MI–EEG. The gradient boosting decision tree (GBDT) classifier was used to classify the features extracted from GMFMDE and DWT. The average classification accuracy of 93.71% and the maximum classification accuracy of 96.96% were obtained, which proved the effectiveness of the proposed feature extraction method for EEG signal feature extraction.

Feature Extraction and Classification of EEG Signal Using Multilayer Perceptron

Feature Extraction and Classification of EEG Signal Using Multilayer Perceptron

Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method

The Human Activity Recognition (HAR) system is the hottest research area in clinical research. The HAR plays a vital role in learning about a patient's abnormal activities; based upon this information, the patient's psychological state can be estimated. An epileptic seizure is a neurological disorder of the human brain and affects millions of people worldwide. If epilepsy is diagnosed correctly and in an early stage, then up to 70% of people can be seizure-free. There is a need for intelligent automatic HAR systems that help clinicians diagnose neurological disorders accurately. In this research, we proposed a Deep Learning (DL) model that enables the detection of epileptic seizures in an automated way, addressing a need in clinical research. To recognize epileptic seizures from brain activities, EEG is a raw but good source of information. In previous studies, many techniques used raw data from EEG to help recognize epileptic patient activities; however, the applied method of extracting features required much intensive expertise from clinical aspects such as radiology and clinical methods. The image data are also used to diagnose epileptic seizures, but applying Machine Learning (ML) methods could address the overfitting problem. In this research, we mainly focused on classifying epilepsy through physical epileptic activities instead of feature engineering and performed the detection of epileptic seizures in three steps. In the first step, we used the open-source numerical dataset of epilepsy of Bonn university from the UCI Machine Learning repository. In the second step, data were fed to the proposed ELM model for training in different training and testing ratios with a little bit of rescaling because the dataset was already pre-processed, normalized, and restructured. In the third step, epileptic and non-epileptic activity was recognized, and in this step, EEG signal feature extraction was automatically performed by a DL model named ELM; features were selected by a Feature Selection (FS) algorithm based on ELM and the final classification was performed using the ELM classifier. In our presented research, seven different ML algorithms were applied for the binary classification of epileptic activities, including K-Nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Regression (LR), Stochastic Gradient Boosting Classifier (SGDC), Gradient Boosting Classifier (GB), Decision Trees (DT), and three deep learning models named Extreme Learning Machine (ELM), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN). After deep analysis, it is observed that the best results were obtained by our proposed DL model, Extreme Learning Machine (ELM), with an accuracy of 100% accuracy and a 0.99 AUC. Such high performance has not attained in previous research. The proposed model's performance was checked with other models in terms of performance parameters, namely confusion matrix, accuracy, precision, recall, F1-score, specificity, sensitivity, and the ROC curve.

Multi-band spatial feature extraction and classification for motor imaging EEG signals based on OSFBCSP-GAO-SVM model : EEG signal processing.

Electroencephalogram (EEG) is a non-stationary random signal with strong background noise, which makes its feature extraction difficult and recognition rate low. This paper presents a feature extraction and classification model of motor imagery EEG signals based on wavelet threshold denoising. Firstly, this paper uses the improved wavelet threshold algorithm to obtain the denoised EEG signal, divides all EEG channel data into multiple partially overlapping frequency bands, and uses the common spatial pattern (CSP) method to construct multiple spatial filters to extract the characteristics of EEG signals. Secondly, EEG signal classification and recognition are realized by the support vector machine algorithm optimized by a genetic algorithm. Finally, the dataset of the third brain-computer interface (BCI) competition and the dataset of the fourth BCI competition is selected to verify the classification effect of the algorithm. The highest accuracy of this method for two BCI competition datasets is 92.86% and 87.16%, respectively, which is obviously superior to the traditional algorithm model. The accuracy of EEG feature classification is improved. It shows that an overlapping sub-band filter banks common spatial pattern-genetic algorithms optimization-support vector machines (OSFBCSP-GAO-SVM) model is an effective model for feature extraction and classification of motor imagination EEG signals.

Trends in EEG signal feature extraction applications.

This paper will focus on electroencephalogram (EEG) signal analysis with an emphasis on common feature extraction techniques mentioned in the research literature, as well as a variety of applications that this can be applied to. In this review, we cover single and multi-dimensional EEG signal processing and feature extraction techniques in the time domain, frequency domain, decomposition domain, time-frequency domain, and spatial domain. We also provide pseudocode for the methods discussed so that they can be replicated by practitioners and researchers in their specific areas of biomedical work. Furthermore, we discuss artificial intelligence applications such as assistive technology, neurological disease classification, brain-computer interface systems, as well as their machine learning integration counterparts, to complete the overall pipeline design for EEG signal analysis. Finally, we discuss future work that can be innovated in the feature extraction domain for EEG signal analysis.

Revised Tunable Q-Factor Wavelet Transform for EEG-Based Epileptic Seizure Detection.

Electroencephalogram (EEG) signals are an essential tool for the detection of epilepsy. Because of the complex time series and frequency features of EEG signals, traditional feature extraction methods have difficulty meeting the requirements of recognition performance. The tunable Q-factor wavelet transform (TQWT), which is a constant-Q transform that is easily invertible and modestly oversampled, has been successfully used for feature extraction of EEG signals. Because the constant-Q is set in advance and cannot be optimized, further applications of the TQWT are restricted. To solve this problem, the revised tunable Q-factor wavelet transform (RTQWT) is proposed in this paper. RTQWT is based on the weighted normalized entropy and overcomes the problems of a nontunable Q-factor and the lack of an optimized tunable criterion. In contrast to the continuous wavelet transform and the raw tunable Q-factor wavelet transform, the wavelet transform corresponding to the revised Q-factor, i.e., RTQWT, is sufficiently better adapted to the nonstationary nature of EEG signals. Therefore, the precise and specific characteristic subspaces obtained can improve the classification accuracy of EEG signals. The classification of the extracted features was performed using the decision tree, linear discriminant, naive Bayes, SVM and KNN classifiers. The performance of the new approach was tested by evaluating the accuracies of five time-frequency distributions: FT, EMD, DWT, CWT and TQWT. The experiments showed that the RTQWT proposed in this paper can be used to extract detailed features more effectively and improve the classification accuracy of EEG signals.

Feature extraction and analysis of overt and covert EEG signals with speller devices

Feature extraction and analysis of overt and covert EEG signals with speller devices

Feature extraction and analysis of overt and covert EEG signals with speller devices

Feature extraction and analysis of overt and covert EEG signals with speller devices

EEG Signal Feature Extraction and Classification for Epilepsy Detection

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

A compact multi-branch 1D convolutional neural network for EEG-based motor imagery classification

Motor imagery (MI) EEG signals are considered a promising paradigm for BCI systems that enable humans to communicate with the outside world through the brain and have a wide range of applications to improve patients' quality of life with muscle or nerve damage. Due to the low signal-to-noise ratio of the acquired EEG signals, it is challenging to decode the intent accurately and even more challenging to decode the raw EEG signals. Currently, there is no deep learning method to achieve high classification performance in decoding raw EEG signals. We propose a new end-to-end network for decoding MI EEG signals, Compact Multi-Branch One-dimensional Convolutional Neural Network (CMO-CNN), without some pre-processing such as filtering, using the original EEG signals. The 1D convolution is used as the feature extractor to extract diverse and multi-level features for fusion using different filter scales and depths of different branches. 1D Squeeze-and-Excitation blocks (SE-blocks) and shortcut connections are added to further improve the generalization and robustness of the network. 83.92% and 87.19% classification accuracies were achieved in the BCI Competition IV-2a and the BCI Competition IV-2b datasets. An 8% improvement to 63.34% was achieved in the cross-subject test, demonstrating that our proposed CMO-CNN outperforms the current state-of-the-art methods. Visual analysis of the network shows that the proposed model can accurately learn the event-related desynchronization/synchronization (ERD/ERS) phenomenon in the signal, and 1D convolution is actively used for feature extraction suitable for feature extraction of the original EEG signal.

Measuring and Computing Cognitive Statuses of Construction Workers Based on Electroencephalogram: A Critical Review

Construction workers’ cognitive statuses affecting their safety and productivity are essential for successful construction management. Electroencephalogram (EEG) provides a potential objective tool for measuring and computing the cognitive status of construction workers. This study aims to answer how to adopt EEG for measuring and computing construction workers’ cognitive statuses through a critical review. The literature search and selection process included 21 eligible articles. The content analysis was then conducted from three aspects of investigated cognitive statuses, experiment design, and data analysis. The investigated cognitive statuses include vigilance, mental fatigue, mental stress, attention, mental workload, and emotional state. The experiment design is reviewed from the perspective of participant selection, device selection and electrode deployment, task design, and label selection. The data analysis involves EEG signal preprocessing, feature extraction, and cognitive status computation. Five major limitations of the existing studies have been identified, and five future research directions are proposed to address these limitations. This review provides guidance for researchers to use EEG for measuring and computing various cognitive statuses of construction workers. It also provides valuable suggestions for future research and on-site construction management.

Brain computer interface system based on monocular vision and motor imagery for UAV indoor space target searching

This paper puts forward a brain computer interface (BCI) system that integrates the monocular vision navigation and decision subsystems to realize the indoor three-dimensional (3D) space target searching with a low speed Unmanned Aerial Vehicle (UAV). The monocular vision navigation subsystem provides 3D space feasible flight directions for the decision subsystem. It utilizes the Scale Invariant Feature Transform (SIFT) algorithm and Brute-Force (BF) algorithm to extract the key points and match the extracted feature points. Obstacles and their positions are estimated by comparing the changes in the size of key points and the ratio of the size of the “convex points” area of the interest targets. The decision subsystem collects four kinds of motor imaginary (MI) tasks (left/right-hand, feet and tongue) electroencephalogram (EEG) signals from 15 electrodes firstly. Then, it applies two fifth-order Butterworth Band-Pass Filter (BPF) to preprocess the raw MI tasks EEG signals. Next, it adopts the Common Spatial Pattern (CSP) to spatially filter the preprocess EEG signals. To implement the quaternary classification,the decision subsystem utilizes the single convolutional layer Convolutional Neural Network (CNN) to realize the MI tasks EEG signals feature extraction, classification and final feasible flight direction decision finally. During this process, the spectrograms of the spatially filtered signals are given as the input to CNN. The actual indoor 3D space target searching experiment verifies that this BCI system has good adaptability and control stability.

Feature Extraction Method of Epileptic EEG Signal based on Wavelet Packet and Improved Fuzzy Entropy

Epileptic eeg signal has obvious characteristic information, which can be used as an important basis to judge whether epileptic seizure occurs. Because of the low recognition rate of single feature extraction method, a method of eeg feature extraction based on wavelet packet transform and improved fuzzy entropy was proposed. In view of the characteristics of eeg signal with large noise and weak signal, the Wavelet packet Transform (WPT) is used to decompose the EEG signal with multi-resolution and make it into the signal with different characteristics. The original Fuzzy entropy (Fuzzy EN) algorithm was improved to improve its ability of reflecting the degree of irregularity and complexity of time series. Finally, the feature extraction of epileptic EEG signal was completed by combining the wavelet packet transform method.

Brain Network for Exploring the Change of Brain Neurotransmitter 5-Hydroxytryptamine of Autism Children by Resting-State EEG.

The study was aimed at understanding the brain network and the change rule of brain neurotransmitter 5-hydroxytryptamine (5-HT) in autism children through resting-state electroencephalogram (EEG). 20 autistic children in hospital were selected and defined as the observation group. Meanwhile, 20 healthy children were defined as the control group. EEG signals were collected for the two groups. Fuzzy C-means (FCM) algorithm was used to extract features of EEG signals, and DTF was applied for the causal association between multichannel EEG signals. The two groups were compared for the average function value and regional efficiency of the brain neurotransmitter 5-HT. The results showed that the classification accuracy of frontal F7 channel, left frontal FP1 channel, and temporal T6 channel was 95.2%, 95.3%, and 91.2%, respectively. The average of high beta frequency band, low beta frequency band, theta frequency band, and alpha frequency band in the control group was significantly higher than that in the observation group under the optimal threshold (P < 0.05). Compared with normal subjects (34.27), the average function of 5-HT in the brain was 20.13 in patients with low function and 45.74 in patients with hyperfunction. In conclusion, FCM algorithm can feature extraction of EEG signals, especially in the frontal F7 channel, the left frontal FP1 channel, and the TEMPORAL T6 channel, which has high classification accuracy and can well express the EEG signals of autistic children. The level of 5-HT in autistic children is lower than that in healthy people, and it is closely related to loneliness and depression.

A Survey for Lie Detection Methodology Using EEG Signal Processing

Electroencephalography (EEG) is a hot topic all around the world. EEG signals, a series of measurements taken using electrodes on the scalp, can indicate brain activity. They are more private, sensitive, and difficult to steal and recreate. EEG data are increasingly commonly employed in diagnosing brain illnesses and the field of Brain-Computer interfaces thanks to advancements in biomedical signal processing techniques (BCI). BCI is a brain-computer interface that uses electrical impulses from the brain to communicate. EEG signals are used to interpret the electrical activity of the brain. The electrical activity of the brain is read using EEG signals. Many studies are being conducted in many fields to benefit from this technology. Studying EEG gives a solid understanding of how brain signals function in various moods and activities. Lie detection is a new technology that is being used to combat crime. Traditionally, this has been accomplished by language analysis, face and body movement recognition, training observation, and voice stress analysis. EEG analysis provides a better understanding of brain activity thanks to advances in cognitive science and neuroscience. Deception identification has become a severe issue as crime has increased. Previous surveys have discussed numerous approaches supported with experimental outcomes and compared them. This paper addressed each direction and offered different sets of characteristics and electrodes, EEG signal preprocessing, feature extraction, feature selection, and classification. Also, it discusses many methods which may need some adjustments at each phase of brain signal processing for lie detection.