The Effective Brain Areas in Recognition of Dyslexia

Background: The brain has four lobes consist of frontal, parietal, occipital, and temporal. Most researchers have reported that the left occipitotemporal region of the brain, which is the combined region of the occipital and temporal lobes, is less active in children with dyslexia like Sklar, Glaburda, Ashkenazi and Leisman.Methods: There are different methods and tools to investigate how the brain works, such as magnetic resonance imaging (MRI), positron emission tomography (PET), magneto-encephalography (MEG) and electroencephalography (EEG). Among these, EEG determines the electrical activity of the brain with the electrodes placed on the special areas on the scalp. In this research, we processed the EEG signals of dyslexic children and healthy ones to determine what the areas of the brain are most likely to cause the disease.Results: For this purpose, we extracted 43 features, including relative spectral power (RSP) features, mean, standard deviation, skewness, kurtosis, Hjorth, and AR parameters. Then an SVM classifier is used to separate two classes. Finally, we show the particular brain activation pattern by calculating the correlation coefficients and co-occurrence matrices, which suggests the activation of the working memory region as an active area.Conclusion: By identifying the brain areas involved in reading activity, it has expected that psychologists and physicians will be able to design the therapeutic exercises to activate this part of the brain

A Review on EEG Signals Based Emotion Recognition

Emotion recognition has become a very controversial issue in brain-computer interfaces (BCIs). Moreover, numerous studies have been conducted in order to recognize emotions. Also, there are several important definitions and theories about human emotions. In this paper we try to cover important topics related to the field of emotion recognition. We review several studies which are based on analyzing electroencephalogram (EEG) signals as a biological marker in emotion changes. Considering low cost, good time and spatial resolution, EEG has become very common and is widely used in most BCI applications and studies. First, we state some theories and basic definitions related to emotions. Then some important steps of an emotion recognition system like different kinds of biologic measurements (EEG, electrocardiogram [EEG], respiration rate, etc), offline vs online recognition methods, emotion stimulation types and common emotion models are described. Finally, the recent and most important studies are reviewed

Emotion Classification through Nonlinear EEG Analysis Using Machine Learning Methods

Background: Emotion recognition, as a subset of affective computing, has received considerable attention in recent years. Emotions are key to human-computer interactions. Electroencephalogram (EEG) is considered a valuable physiological source of information for classifying emotions. However, it has complex and chaotic behavior.Methods: In this study, an attempt is made to extract important nonlinear features from EEGs with the aim of emotion recognition. We also take advantage of machine learning methods such as evolutionary feature selection methods and committee machines to enhance the classification performance. Classification performed concerning both arousal and valence factors.Results: Results suggest that the proposed method is successful and comparable to the previous works. A recognition rate equal to 90% achieved, and the most significant features reported. We apply the final classification scheme to 2 different databases including our recorded EEGs and a benchmark dataset to evaluate the suggested approach.Conclusion: Our findings approve of the effectiveness of using nonlinear features and a combination of classifiers. Results are also discussed from different points of view to understand brain dynamics better while emotion changes. This study reveals useful insights about emotion classification and brain-behavior related to emotion elicitation

Performance assessment of high-density diffuse optical topography regarding source-detector array topology.

Recent advances in optical neuroimaging systems as a functional interface enhance our understanding of neuronal activity in the brain. High density diffuse optical topography (HD-DOT) uses multi-distance overlapped channels to improve the spatial resolution of images comparable to functional magnetic resonance imaging (fMRI). The topology of the source and detector (SD) array directly impacts the quality of the hemodynamic reconstruction in HD-DOT imaging modality. In this work, the effect of different SD configurations on the quality of cerebral hemodynamic recovery is investigated by presenting a simulation setup based on the analytical approach. Given that the SD arrangement determines the elements of the Jacobian matrix, we conclude that the more individual components in this matrix, the better the retrieval quality. The results demonstrate that the multi-distance multi-directional (MDMD) arrangement produces more unique elements in the Jacobian array. Consequently, the inverse problem can accurately retrieve the brain activity of diffuse optical topography data

Comparative analysis of the discriminative capacity of EEG, two ECG-derived and respiratory signals in automatic sleep staging

Highly accurate classification of sleep stages is possible based on EEG signals alone. However, reliable and high quality acquisition of these signals in the home environment is difficult. Instead, electrocardiogram (ECG) and Respiratory (Res) signals are easier to record and may offer a practical alternative for home monitoring of sleep. Therefore, automatic sleep staging was performed using ECG, Res (thoracic excursion) and EEG signals from 31 nocturnal recordings of the Sleep Heart Health Study (SHHS) polysomnography Database. Feature vectors were extracted from 0.5 min (standard) epochs of sleep data by time-domain, frequency domain, time-frequency and nonlinear methods and optimized by using the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) method. These features were then classified by using a SVM. Classification based upon EEG features produced a Correct Classification Ratio CCR=0.92. In comparison, features derived from ECG signals alone, that is the combination of Heart Rate Variability (HRV), and ECG-Derived Respiration (EDR) signals produced a CCR=0.54, while those features based on the combination of HRV and (thoracic) Res signals resulted in a CCR=0.57. Overall comparison of the results based on standard epochs of EEG signals with those obtained from 5-minute (long) epochs of cardiorespiratory signals, revealed that acceptable CCR=0.81 and discriminative capacity (Accuracy=89.32%, Specificity=92.88% and Sensitivity=78.64%) were also achievable when using optimal feature sets derived from long epochs of the latter signals in sleep staging. In addition, it was observed that the presence of some artifacts (like bigeminy) in the cardiorespiratory signals reduced the accuracy of automatic sleep staging more than the artifacts that contaminated the EEG signals

A predictive model of death from cerebrovascular diseases in intensive care units

Objective: This study aimed to explore the mortality prediction of patients with cerebrovascular diseases in the intensive care unit (ICU) by examining the important signals during different periods of admission in the ICU, which is considered one of the new topics in the medical field. Several approaches have been proposed for prediction in this area. Each of these methods has been able to predict mortality somewhat, but many of these techniques require recording a large amount of data from the patients, where recording all data is not possible in most cases; at the same time, this study focused only on heart rate variability (HRV) and systolic and diastolic blood pressure. Methods: The ICU data used for the challenge were extracted from the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) Clinical Database. The proposed algorithm was evaluated using data from 88 cerebrovascular ICU patients, 48 men and 40 women, during their first 48 hours of ICU stay. The electrocardiogram (ECG) signals are related to lead II, and the sampling frequency is 125 Hz. The time of admission and time of death are labeled in all data. In this study, the mortality prediction in patients with cerebral ischemia is evaluated using the features extracted from the return map generated by the signal of HRV and blood pressure. To predict the patient's future condition, the combination of features extracted from the return mapping generated by the HRV signal, such as angle (α), area (A), and various parameters generated by systolic and diastolic blood pressure, including DBPMax−Min SBPSD have been used. Also, to select the best feature combination, the genetic algorithm (GA) and mutual information (MI) methods were used. Paired sample t-test statistical analysis was used to compare the results of two episodes (death and non-death episodes). The P-value for detecting the significance level was considered less than 0.005. Results: The results indicate that the new approach presented in this paper can be compared with other methods or leads to better results. The best combination of features based on GA to achieve maximum predictive accuracy was m (mean), LMean, A, SBPSVMax, DBPMax-Min. The accuracy, specificity, and sensitivity based on the best features obtained from GA were 97.7%, 98.9%, and 95.4% for cerebral ischemia disease with a prediction horizon of 0.5–1 hour before death. The d-factor for the best feature combination based on the GA model is less than 1 (d-factor = 0.95). Also, the bracketed by 95 percent prediction uncertainty (95PPU) (%) was obtained at 98.6. Conclusion: The combination of HRV and blood pressure signals might increase the accuracy of the prediction of the death episode and reduce the minimum hospitalization time of the patient with cerebrovascular diseases to determine the future status

Orchestration of saccadic eye-movements by brain rhythms in macaque Frontal Eye Field

Abstract Visual perception has been suggested to operate on temporal ‘chunks’ of sensory input, rather than on a continuous stream of visual information. Saccadic eye movements impose a natural rhythm on the sensory input, as periods of steady fixation between these rapid eye movements provide distinct temporal segments of information. Ideally, the timing of saccades should be precisely locked to the brain’s rhythms of information processing. Here, we investigated such locking of saccades to rhythmic neural activity in rhesus monkeys performing a visual foraging task. We found that saccades are phase-locked to local field potential oscillations (especially, 9–22 Hz) in the Frontal Eye Field, with the phase of oscillations predictive of the saccade onset as early as 100 ms prior to these movements. Our data also indicate a functional role of this phase-locking in determining the direction of saccades. These findings show a tight—and likely important—link between oscillatory brain activity and rhythmic behavior that imposes a rhythmic temporal structure on sensory input, such as saccadic eye movements