Showing 12 results for Electroencephalogram
Mina Mirnaziri, Masoomeh Rahimi, Sepidehsadat Alavikakhaki, Reza Ebrahimpour,
Volume 4, Issue 1 (2-2013)
Abstract
Introduction: In most BCI articles which aim to separate movement imaginations, µ and &beta frequency bands have been used. In this paper, the effect of presence and absence of &gamma band on performance improvement is discussed since movement imaginations affect &gamma frequency band as well.
Methods: In this study we used data set 2a from BCI Competition IV. In this data set, 9 healthy subjects have performed left hand, right hand, foot and tongue movement imaginations. Time and frequency intervals are computed for each subject and then are classi.ed using Common Spatial Pattern (CSP) as a feature extractor. Finally, data is classi.ed by LDA1, RBF2 MLP3, SVM4and KNN 5 methods. In all experiments, accuracy rate of classi.cation is computed using 4 fold validation method.
Results: It is seen that most of the time, combination of &mu,&beta and &gamma bands would have better performance than just using combination of &mu and &beta bands or &gamma band alone. In general, the improvement rate of the average classi.cation accuracy is computed 2.91%.
Discussion: In this study, it is shown that using combination of µ, &beta and &gamma frequency bands provides more information than only using combination of µ and &beta in movement imagination separations.
Seyyed Abed Hosseini,
Volume 8, Issue 6 (11-2017)
Abstract
Introduction: This paper proposes a reliable and efficient technique to recognize different epilepsy states, including healthy, interictal, and ictal states, using Electroencephalogram (EEG) signals.
Methods: The proposed approach consists of pre-processing, feature extraction by higher order spectra, feature normalization, feature selection by genetic algorithm and ranking method, and classification by support vector machine with Gaussian and polynomial radial basis function kernels. The proposed approach is validated on a public benchmark dataset to compare it with previous studies.
Results: The results indicate that the combined use of above elements can effectively decipher the cognitive process of epilepsy and seizure recognition. There are several bispectrum and bicoherence peaks at every bi-frequency plane, which reveal the location of the quadratic phase coupling. The proposed approach can reach, in almost all of the experiments, up to 100% performance in terms of sensitivity, specificity, and accuracy.
Conclusion: Comparing between the obtained results and previous approaches approves the effectiveness of the proposed approach for seizure and epilepsy recognition.
Hamidreza Ghaffari, Ali Yoonessi, Mohammad Javad Darvishi, Akbar Ahmadi,
Volume 9, Issue 2 (3-2018)
Abstract
Introduction: Transcranial Direct Current Stimulation (tDCS) has been used as a non-invasive method to increase the plasticity of brain. Growing evidence has shown several brain disorders such as depression, anxiety disorders, and chronic pain syndrome are improved following tDCS. In patients with Obsessive-Compulsive Disorder (OCD), increased brain rhythm activity particularly in the frontal lobe has been reported in several studies using Eectroencephalogram (EEG). To our knowledge, no research has been done on the effects of electrical stimulation on brain signals of patients with OCD. We measured the electrical activity of the brain using EEG in patients with OCD before and after tDCS and compared it to normal participants.
Methods: Eight patients with OCD (3 males) and 8 matched healthy controls were recruited. A 64-channel EEG was used to record a 5-min resting state before and after application of tDCS in both groups. The intervention of tDCS was applied for 15 minutes with 2 mA amplitude where anode was placed on the left Dorsolateral Prefrontal Cortex (DLPFC) and cathode on the right DLPFC.
Results: In line with previous studies, the results showed that the power of Delta frequency band in OCD patients are significantly higher than the normal group. Following anodal tDCS, hyperactivity in Delta and Theta bands declined in most channels, particularly in DLPFC (F3, F4) and became similar to normal signals pattern. The reduction in Delta band was significantly more than the other bands.
Conclusion: Anodal tDCS over the left DLPFC significantly decreased the power of frequency bands of Delta and Theta in Patients with OCD. The pattern of EEG activity after tDCS became particularly similar to normal, so tDCS may have potential clinical application in these patients.
Mehrnoosh Neghabi, Hamid Reza Marateb, Amin Mahnam,
Volume 10, Issue 3 (5-2019)
Abstract
Introduction: Brain-Computer Interface (BCI) systems provide a communication pathway between users and systems. BCI systems based on Steady-State Visually Evoked Potentials (SSVEP) are widely used in recent decades. Different feature extraction methods have been introduced in the literature to estimate SSVEP responses to BCI applications.
Methods: In this study, the new algorithms, including Canonical Correlation Analysis (CCA), Least Absolute Shrinkage and Selection Operator (LASSO), L1-regularized Multi-way CCA (L1-MCCA), Multi-set CCA (MsetCCA), Common Feature Analysis (CFA), and Multiple Logistic Regression (MLR) are compared using proper statistical methods to determine which one has better performance with the least number of EEG electrodes.
Results: It was found that MLR, MsetCCA, and CFA algorithms provided the highest performances and significantly outperformed CCA, LASSO, and L1-MCCA algorithms when using 8 EEG channels. However, when using only 1 or 2 EEG channels d, CFA method provided the highest F-scores. This algorithm not only outperformed MLR and MsetCCA when applied on different electrode montages but also provided the fastest computation time on the test set.
Conclusion: Although MLR method has already demonstrated to have higher performance in comparison with other frequency recognition algorithms, this study showed that in a practical SSVEP-based BCI system with 1 or 2 EEG channels and short-time windows, CFA method outperforms other algorithms. Therefore, it is proposed that CFA algorithm is a promising choice for the expansion of practical SSVEP-based BCI systems.
Iman M. Mourad, Neveen A. Noor, Haitham S. Mohammed, Heba S. Aboul Ezz, Yasser A. Khadrawy,
Volume 12, Issue 5 (9-2021)
Abstract
Introduction: Caffeine and nicotine are the most widely consumed psychostimulants worldwide. Although the effects of each drug alone on the central nervous system have been studied extensively, the literature on the neurochemical and electrophysiological effects of their combined treatments is scarce. The present study investigated the cortical electrophysiological and neurochemical alterations induced by acute administration of caffeine and nicotine in rats.
Methods: The rats received caffeine and nicotine at a 1-hour interval between the two treatments.
Results: Caffeine and nicotine administration resulted in a significant decrease in the concentrations of cortical amino acid neurotransmitters, namely glutamate, aspartate, glycine, and taurine, while γ-aminobutyric acid (GABA) significantly increased. Increased cortical lipid peroxidation and reduced glutathione and nitric oxide levels and acetylcholinesterase and Na⁺/K⁺-ATPase activities were also observed. The Electroencephalogram (EEG) showed an increase in delta frequency power band, whereas theta, beta-1, and beta-2 decreased after caffeine and nicotine treatment.
Conclusion: These findings suggest that caffeine and nicotine adversely exacerbate their stimulant effects manifested by the EEG changes mediated by increasing cholinergic transmission and disturbing the balance between the excitatory and inhibitory amino acids leading to oxidative stress.
Arash Maghsoudi, Ahmad Shalbaf,
Volume 12, Issue 6 (11-2021)
Abstract
Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signals can help understand disorders, such as attention-deficit hyperactivity, dyscalculia, or autism spectrum disorder where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recognition systems rely on features of a single channel of EEG; however, the relationships between EEG channels in the form of effective brain connectivity analysis can contain valuable information. This study aims to find distinctive, effective brain connectivity features and create a hierarchical feature selection for effectively classifying mental arithmetic and baseline tasks.
Methods: We estimated effective connectivity using Directed Transfer Function (DTF), direct DTF (dDTF) and Generalized Partial Directed Coherence (GPDC) methods. These measures determine the causal relationship between different brain areas. A hierarchical feature subset selection method selects the most significant effective connectivity features. Initially, Kruskal–Wallis test was performed. Consequently, five feature selection algorithms, namely, Support Vector Machine (SVM) method based on Recursive Feature Elimination, Fisher score, mutual information, minimum Redundancy Maximum Relevance (RMR), and concave minimization and SVM are used to select the best discriminative features. Finally, the SVM method was used for classification.
Results: The obtained results indicated that the best EEG classification performance in 29 participants and 60 trials is obtained using GPDC and feature selection via concave minimization method in Beta2 (15-22Hz) frequency band with 89% accuracy.
Conclusion: This new hierarchical automated system could be helpful in the discrimination of mental arithmetic and baseline tasks from EEG signals effectively.
Abdolvahed Narmashiri, Javad Hatami, Reza Khosrowabadi, Ahmad Sohrabi,
Volume 13, Issue 4 (7-2022)
Abstract
Introduction: Paranormal beliefs are defined as the belief in extrasensory perception, precognition, witchcraft, and telekinesis, magical thinking, psychokinesis, superstitions. Previous studies corroborate that executive brain functions underpin paranormal beliefs. To test this hypotheses, neurophysiological studies of brain activity are required.
Methods: A sample of 20 students (10 girls, Mean±SD age: 22.50±4.07 years) were included in the current study. The absolute power of resting-state electroencephalogram (EEG) was analyzed in intra-hemispheric and inter-hemispheric coherence with eyes open. The paranormal beliefs were determined based on the total score of the revised paranormal belief scale (RPBS).
Results: The results of this study demonstrated a significant negative relationship between paranormal beliefs and resting-state EEG in alpha band activity in the frontal lobe (left hemisphere), EEG coherence of alpha and β1, β2, and gamma band activities in the frontal lobe (right hemisphere) and coherence of alpha and β1, β2 and gamma band activities between frontal regions (two hemispheres). In addition, the results showed that coherence of α, α1, β, and β2 band activities between the frontal lobe (right hemispheres) and the EEG coherence of Δ, α1, and beta band activities in the frontal lobe (two hemispheres) predict paranormal beliefs.
Conclusion: This study confirms the connection of executive brain functions to paranormal beliefs and determines that frontal brain function may contribute to paranormal beliefs.
Sara Bagherzadeh, Keivan Maghooli, Ahmad Shalbaf, Arash Maghsoudi,
Volume 14, Issue 1 (1-2023)
Abstract
Introduction: Nowadays, deep learning and convolutional neural networks (CNNs) have become widespread tools in many biomedical engineering studies. CNN is an end-to-end tool, which makes the processing procedure integrated, but in some situations, this processing tool requires to be fused with machine learning methods to be more accurate.
Methods: In this paper, a hybrid approach based on deep features extracted from wavelet CNNs (WCNNs) weighted layers and multiclass support vector machine (MSVM) was proposed to improve the recognition of emotional states from electroencephalogram (EEG) signals. First, EEG signals were preprocessed and converted to Time-Frequency (T-F) color representation or scalogram using the continuous wavelet transform (CWT) method. Then, scalograms were fed into four popular pre-trained CNNs, AlexNet, ResNet-18, VGG-19, and Inception-v3 to fine-tune them. Then, the best feature layer from each one was used as input to the MSVM method to classify four quarters of the valence-arousal model. Finally, the subject-independent leave-one-subject-out criterion was used to evaluate the proposed method on DEAP and MAHNOB-HCI databases.
Results: Results showed that extracting deep features from the earlier convolutional layer of ResNet-18 (Res2a) and classifying using the MSVM increased the average accuracy, precision, and recall by about 20% and 12% for MAHNOB-HCI and DEAP databases, respectively. Also, combining scalograms from four regions of pre-frontal, frontal, parietal, and parietal-occipital and two regions of frontal and parietal achieved the higher average accuracy of 77.47% and 87.45% for MAHNOB-HCI and DEAP databases, respectively.
Conclusion: Combining CNN and MSVM increased the recognition of emotion from EEG signals and the results were comparable to state-of-the art studies.
Erfan Rezaei, Ahmad Shalbaf,
Volume 14, Issue 2 (3-2023)
Abstract
Introduction: The right and left-hand motor imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchical feature selection and classification for discrimination of right and lefthand MI tasks.
Methods: TE is calculated among EEG channels as the distinctive, effective connectivity features. TE is a model-free method that can measure nonlinear effective connectivity and analyze multivariate dependent directed information flow among neural EEG channels. Then four feature subset selection methods namely relief-F, Fisher, Laplacian, and local learningbased clustering (LLCFS) algorithms are used to choose the most significant effective connectivity features and reduce redundant information. Finally, support vector machine (SVM) and linear discriminant analysis (LDA) methods are used for classification.
Results: Results show that the best performance in 29 healthy subjects and 60 trials is achieved using the TE method via the Relief-F algorithm as feature selection and support vector machine (SVM) classification with 91.02% accuracy.
Conclusion: The TE index and a hierarchical feature selection and classification can be useful for the discrimination of right- and left-hand MI tasks from multichannel EEG signals.
Behrouz Nobakhsh, Ahmad Shalbaf, Reza Rostami, Reza Kazemi,
Volume 15, Issue 2 (3-2024)
Abstract
Introduction: Repetitive transcranial magnetic stimulation (rTMS) is a non-pharmacological treatment for drug-resistant major depressive disorder (MDD) patients. Since the success rate of rTMS treatment is about 50%-55%, it is essential to predict the treatment outcome before starting based on electroencephalogram (EEG) signals, leading to identifying effective biomarkers and reducing the burden of health care centers.
Methods: To this end, pretreatment EEG data with 19 channels in the resting state from 34 drug-resistant MDD patients were recorded. Then, all patients received 20 sessions of rTMS treatment, and a reduction of at least 50% in the total beck depression inventory (BDI-II) score before and after the rTMS treatment was defined as a reference. In the current study, effective brain connectivity features were determined by the direct directed transfer function (dDTF) method from patients’ pretreatment EEG data in all frequency bands separately. Then, the brain functional connectivity patterns were modeled as graphs by the dDTF method and examined with the local graph theory indices, including degree, out-degree, in-degree, strength, out-strength, in-strength, and betweenness centrality.
Results: The results indicated that the betweenness centrality index in the Fp2 node and the δ frequency band are the best biomarkers, with the highest area under the receiver operating characteristic curve value of 0.85 for predicting the rTMS treatment outcome in drug-resistant MDD patients.
Conclusion: The proposed method investigated the significant biomarkers that can be used to predict the rTMS treatment outcome in drug-resistant MDD patients and help clinical decisions.
Elnaz Vafaei, Fereidoun Nowshiravan Rahatabad, Seyed Kamaledin Setarehdan, Parviz Azadfallah,
Volume 15, Issue 3 (5-2024)
Abstract
Introduction: Emotion recognition by electroencephalogram (EEG) signals is one of the complex methods because the extraction and recognition of the features hidden in the signal are sophisticated and require a significant number of EEG channels. Presenting a method for feature analysis and an algorithm for reducing the number of EEG channels fulfills the need for research in this field.
Methods: Accordingly, this study investigates the possibility of utilizing deep learning to reduce the number of channels while maintaining the quality of the EEG signal. A stacked autoencoder network extracts optimal features for emotion classification in valence and arousal dimensions. Autoencoder networks can extract complex features to provide linear and non- linear features which are a good representative of the signal.
Results: The accuracy of a conventional emotion recognition classifier (support vector machine) using features extracted from SAEs was obtained at 75.7% for valence and 74.4% for arousal dimensions, respectively.
Conclusion: Further analysis also illustrates that valence dimension detection with reduced EEG channels has a different composition of EEG channels compared to the arousal dimension. In addition, the number of channels is reduced from 32 to 12, which is an excellent development for designing a small-size EEG device by applying these optimal features.
Mr Seyed Morteza Mirjebreili, Dr Reza Shalbaf, Dr Ahmad Shalbaf,
Volume 15, Issue 6 (11-2024)
Abstract
Introduction: A major challenge today is personalizing the treatment for patients with major depressive disorder (MDD) to make it more efficient. To address this issue, we have proposed a novel approach based on machine learning (ML) models that utilize neural activity flow prior to treatment with selective serotonin reuptake inhibitor (SSRI) medication.
Methods: The electroencephalogram signals of 30 patients were used to calculate the neural activity flow of each patient using the direct directed transfer function (dDTF). Then, based on the area under the curve (AUC) values, 30 important connections were identified for the delta, theta, alpha, beta, and gamma bands. To select the most critical neural activity flow, these neural activity flows were combined, and forward features, mRMR, and ReliefF methods were applied. Support vector machines (SVMs), decision tree, and random forest models are trained using selected neural activity flows.
Results: Results showed that most connections originated from F8, Pz, T5, and P4, mainly from the frontal and parietal lobes. In addition, the SVM model showed 98% accuracy in classification using forward feature selection, where most of the neural activity flows were selected from alpha and beta. Finally, results indicate that patients who responded to treatment differed in their patterns of frontoparietal neural activity flows, implying that the frontoparietal network (FPN) is primarily involved in treatment response at alpha and beta frequencies.
Conclusion: Therefore, the proposed method can accurately detect responders in MDD patients. It can reduce costs for both patients and medical facilities.
