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![Electrodes placement of electroencephalogram (EEG) measurement [4] (reproduced with permission by Elsevier (License Number 4781771458692)).](profile/Mohammad-Reza-Faisal/publication/339736502/figure/fig1/AS:865896323088384@1583457179056/Electrodes-placement-of-electroencephalogram-EEG-measurement-4-reproduced-with.png)
Electrodes placement of electroencephalogram (EEG) measurement [4] (reproduced with permission by Elsevier (License Number 4781771458692)).
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Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonst...
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Context 1
... the monitoring of respiratory functions may be desired in the diagnosis of respiratory disorders such as sleep apnea and require the addition of other tools applied in conjunction with the EEG electrodes, most often a pulse oximeter, oral thermometer, nasal cannula, thoracic and abdominal belt, and a throat microphone [4,5]. Figure 1 represents the standard system used for measuring the EEG signal, termed as the 10-20 system, in which the minimum number of electrodes used is 21. This method regulates the physical placement and designations of electrodes on the scalp. ...
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Interpretation of data obtained from electroencephalogram (EEG) has been commonly used for studying the condition of the brain and to diagnose any abnormalities. However, it is a common occurrence that different characteristic waves from EEG may overlap each other which may cause inaccuracies leading to wrong interpretation and hence incorrect diag...
Citations
... Power spectra of ECoG epochs corresponding to different experimental conditions were decomposed in various frequency bands by calculating the discrete 512-point Fourier transform (Hanning window) (Al-Fahoum and Al-Fraihat, 2014;Delimayanti et al., 2020;Kentar et al., 2022). The following power frequency bands were considered for each ECoG epoch: Slow frequencies: Delta (0.1-4 Hz) c and theta (4-7 Hz). ...
Objective
Characterize the neurophysiological effects of mild hypothermia on stroke and spreading depolarizations (SDs) in gyrencephalic brains.
Methods
Left middle cerebral arteries (MCAs) of six hypothermic and six normothermic pigs were permanently occluded (MCAo). Hypothermia began 1 h after MCAo and continued throughout the experiment. ECoG signals from both frontoparietal cortices were recorded. Five-minute ECoG epochs were collected 5 min before, at 5 min, 4, 8, 12, and 16 h after MCAo, and before, during, and after SDs. Power spectra were decomposed into fast (alpha, beta, and gamma) and slow (delta and theta) frequency bands.
Results
In the vascular insulted hemisphere under normothermia, electrodes near the ischemic core exhibited power decay across all frequency bands at 5 min and the 4th hour after MCAo. The same pattern was registered in the two furthest electrodes at the 12th and 16th hour. When mild hypothermia was applied in the vascular insulted hemispheres, the power decay was generalized and seen even in electrodes with uncompromised blood flow. During SD analysis, hypothermia maintained increased delta and beta power during the three phases of SDs in the furthest electrode from the ischemic core, followed by the second furthest and third electrode in the beta band during preSD and postSD segments. However, in hypothermic conditions, the third electrode showed lower delta, theta, and alpha power.
Conclusion
Mild hypothermia attenuates all frequency bands in the vascularly compromised hemisphere, irrespective of the cortical location. During SD formation, it preserves power spectra more significantly in electrodes further from the ischemic core.
... This paper considers three different EEG applications: sleep state classification, Stress monitoring, and Epileptic Seizure [1] 0.5-3.5 3.5-7.5 7.5-12 12-30 >31 Stress [9] 0-3.9 4-7.9 8-10 14-29.9 30-47 detection [18]. ...
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... For instance, artifacts can be caused by actions like moving one's head, jaw or eye movements, blinking, or any muscle activity that generates a significant electromagnetic field. [23]. ...
Mental stress is a prevalent and consequential condition that impacts individuals' well-being and productivity. Accurate classification of mental stress levels using electroencephalogram (EEG) signals is a promising avenue for early detection and intervention. In this study, we present a comprehensive investigation into mental stress classification using EEG data processed with the MNE-Python library. Our research leverages a diverse set of machines learning algorithms, including Random Forest (RF), Decision Tree, K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Adaboost, and Extreme Gradient Boosting (XGBoost), to discern differences in classification performance. We employed a single dataset to ensure consistency in our experiments, facilitating a direct comparison of these algorithms. The EEG data were pre-processed using MNE-Python, which included tasks such as signal cleaning, and feature selection. Subsequently, we applied the selected machine learning models to the processed data and assessed their classification performance in terms of accuracy, precision, recall, and F1-score. Our results demonstrate notable variations in the classification accuracy of mental stress levels across the different algorithms. These findings suggest that the choice of machine learning technique plays a pivotal role in the effectiveness of EEG-based mental stress classification. Our study not only highlights the potential of MNE-Python for EEG signal processing but also provides valuable insights into the selection of appropriate machine learning algorithms for accurate and reliable mental stress assessment. These outcomes hold promise for the development of robust and practical systems for real-time mental stress monitoring, contributing to enhanced well-being and performance in various domains such as healthcare, education, and workplace environments
... used. This shows that the utilization of an automated system leads to greater benefits for experts when identifying sleep stages [6], [7]. ...
... Only one or two EEG channels is/are deployed in this experiment, ensuring fewer complications for the operational installation than the sleep scoring process utilizing multimodal signals [14]. The subject's degree of comfort is also enhanced, with the Fast Fourier Transform (FFT) applied to the high-dimensional characteristics acquired from single-or multi-channel EEG recordings [6]. This is because FFT is a robust and time-tested method for extracting features from EEG signals. ...
... This confirmed that the tuning parameter affected the overall performance rating, and was noticeable in classification accuracy. These results were useful for implementing big data in health care, especially in medical diagnosis [6], [31]. Table 5 displays the efficacy of a 3000-feature SVM classifier before and after adjustment with the accuracy of classifiers for 2-6 classes. ...
Sleep analysis is widely and experimentally considered due to its importance to body health care. Since its sufficiency is essential for a healthy life, people often spend almost a third of their lives sleeping. In this case, a similar sleep pattern is not practiced by every individual, regarding pure healthiness or disorders such as insomnia, apnea, bruxism, epilepsy, and narcolepsy. Therefore, this study aims to determine the classification patterns of sleep stages, using big data for health care. This used a high-dimensional FFT extraction algorithm, as well as a feature importance and tuning classifier, to develop accurate classification. The results showed that the proposed method led to more accurate classification than previous techniques. This was because the previous experiments had been conducted with the feature selection model, with accuracy implemented as a performance evaluation. Meanwhile, the EEG Sleep Stages classification model in this present report was composed of the feature selection and importance of the extraction stage. The previous and present experiments also reached the highest values of accuracy, with the Random Forest and SVM models using 2000 and 3000 features (87.19% and 89.19%, respectively. In this article, we proposed an analysis that the feature importance subsequently influenced the model's accuracy. This was because the proposed method was easily fine-tuned and optimized for each subject to improve sensitivity and reduce false negative occurrences.
... In this work, we will focus on the delta-to-alpha neural cross-frequency coupling. Namely, it is well known that delta and alpha brainwave oscillations play an important role in the brain dynamics [17][18][19][20][21][22] . For instance, there are differences in frequency and power during different sleep stages which appear in the separate delta and alpha brainwave dynamics [23][24][25][26] and in their related delta-alpha effect 27,28 . ...
Neural interactions occur on different levels and scales. It is of particular importance to understand how they are distributed among different neuroanatomical and physiological relevant brain regions. We investigated neural cross-frequency couplings between different brain regions according to the Desikan–Killiany brain parcellation. The adaptive dynamic Bayesian inference method was applied to EEG measurements of healthy resting subjects in order to reconstruct the coupling functions. It was found that even after averaging over all subjects, the mean coupling function showed a characteristic waveform, confirming the direct influence of the delta-phase on the alpha-phase dynamics in certain brain regions and that the shape of the coupling function changes for different regions. While the averaged coupling function within a region was of similar form, the region-averaged coupling function was averaged out, which implies that there is a common dependence within separate regions across the subjects. It was also found that for certain regions the influence of delta on alpha oscillations is more pronounced and that oscillations that influence other are more evenly distributed across brain regions than the influenced oscillations. When presenting the information on brain lobes, it was shown that the influence of delta emanating from the brain as a whole is greatest on the alpha oscillations of the cingulate frontal lobe, and at the same time the influence of delta from the cingulate parietal brain lobe is greatest on the alpha oscillations of the whole brain.
... The FFT algorithm was applied to compute the frequency components and their magnitudes. The power spectrum was then calculated by taking the absolute value squared of the FFT output [42], representing the distribution of power across different frequency components. Analysis was performed within each frequency band to quantify the power present in each respective range. ...
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... Instead of directly feeding the raw EEG signal to the next stage for classification, extracting more stable, informative and discriminative statistical features from the signal of interest is crucial. In this work, a fast Fourier Transform (FFT) as one of the established and proven techniques for extracting features from EEG signals [45] is adopted. This technique converts the EEG signal from the time domain into the frequency domain where the hidden features can become visible. ...
Electroencephalogram(EEG)-based authentication has received increasing attention from researchers as they believe it could serve as an alternative to more conventional personal authentication methods. Unfortunately, EEG signals are non-stationary and could be easily contaminated by noise and artifacts. Therefore, further processing of data analysis is needed to retrieve useful information. Various machine learning approaches have been proposed and implemented in the EEG-based domain, with deep learning being the most current trend. However, retaining the performance of a deep learning model requires substantial computational effort and a vast amount of data, especially when the models go deeper to generate consistent results. Deep learning models trained with small data sets from scratch may experience an overfitting issue. Transfer learning becomes an alternative solution. It is a technique to recognize and apply the knowledge and skills learned from the previous tasks to a new domain with limited training data. This study attempts to explore the applicability of transferring various pre-trained models’ knowledge to the EEG-based authentication domain. A self-collected database that consists of 30 subjects was utilized in the analysis. The database enrolment is divided into two sessions, with each session producing two sets of EEG recording data. The frequency spectrums of the preprocessed EEG signals are extracted and fed into the pre-trained models as the input data. Three experimental tests are carried out and the best performance is reported with accuracy in the range of 99.1–99.9%. The acquired results demonstrate the efficiency of transfer learning in authenticating an individual in this domain.
... Nowadays, signal classification methods play an essential role in a wide variety of life. Examples of the implementation of this method are for the classification of music genres and moods [1], the classification of machine conditions [2], and the classification of signals in the health field [3], [4]. One of the signal classification cases in the health sector is the electrocardiogram (ECG) signal classification. ...
The research aimed to compare the classification performance of arrhythmia classification from the ECG signal dataset from the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) database. Shallow learning methods that were used in this study are Support Vector Machine, Naïve Bayes, and Random Forest. 1D Convolutional Neural Network (1D CNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) were deep learning methods that were used for the study. The models were tested on a dataset with 140 samples that were grouped into four class labels, and each sample has 2160 features. Those models were tested for classification performance. This research shows Random Forest and 1D CNN have the best performance.
... Frequency domain fast Fourier Transform (FFT) [120] An effective methodology for stationary signal analysis. It is better suited to narrowband signals like sine waves. ...
Brain-computer interface systems with Electroencephalogram (EEG), especially those
use motor-imagery (MI) signals, have demonstrated the ability to control electromechanical devices
with promising results. EEG being easy to record and non-invasive makes it a good choice for BCI
systems. MI-based BCI systems compute neuronal activity and decipher these electrical impulses
into gestures or effects, aiming to enable the person to communicate with their surroundings. This
study summarises techniques of EEG signal processing used in the recent decade. This research
paper presents an exhaustive survey on four aspects of EEG signals in BCI systems: signal
acquisition, signal pre-processing, feature extraction, and classification. The most prominent timefrequency
technique, wavelet transform (WT), and its updated version, wavelet packet transform
(WPT), is primarily used in EEG-BCI systems for feature extraction. The development of artificial
intelligence technology motivated researchers to classify motor imagery signals for BCI systems
using machine learning (ML) and deep learning (DL) techniques. This literature survey paper
explores more than 220 research papers related to ML and DL approaches to classify EEG signals
for BCI systems. In order to identify prospective research areas for future investigation, present
challenges are carefully considered, and suggestions are also provided for appropriate feature
extraction and classification techniques. The authors expect that the investigation presented in this
paper will help researchers to find accurate feature extraction, ML, and DL methods and these
techniques will be supportive in devising an effective EEG-BCI system.
... However, it excludes the temporal components of the signal. FFT is also used to improve the automated sleep stage classification performance utilizing the extracted features from EEG through machine learning processes [31]. ...
Electroencephalography (EEG) is a mechanism to understand the brain’s functioning by analyzing brain electrical signals. More recently, it has been more commonly used in studies that are focused on the causation and effect of dementia. More tools are now available to gather EEG data. This brings about the challenge of understanding brain signals, which involves signal processing. Professionals with an electrical engineering background are very comfortable analyzing EEG data. Still, scientists in computer science and related fields need a source that can identify all the tools available and the process of analyzing the data. This paper deals specifically with the existing EEG data analysis tools and the processes involved in analyzing the EEG data using these tools. Furthermore, the paper goes in-depth into identifying the tools and the mechanisms of data processing techniques. In addition, it lists a set of definitions required for a better understanding of EEG data analysis, which can be challenging. The purpose of this paper is to serve as a reference for not only scientists that are new to EEG data analysis but also seasoned scientists that are looking for a specific data component in EEG and can go straight to the section of the paper that deals with the tool that they are using.
