Classification of cognitive and resting states of the brain using EEG features

2016 ◽  
Author(s):  
Rana Fayyaz Ahmad ◽  
Aamir Saeed Malik ◽  
Hafeez Ullah Amin ◽  
Nidal Kamel ◽  
Faruque Reza
Keyword(s):  
The Brain ◽  
Eeg Features

scholarly journals An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network

2021 ◽  
Author(s):  
Muhammad Irfan Sharif ◽  
Jian Ping Li ◽  
Javeria Amin ◽  
Abida Sharif
Keyword(s):  
Brain Tumor ◽  
Tumor Detection ◽  
Entropy Method ◽  
Feature Extraction And Selection ◽  
Tumor Region ◽  
Selection For ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain ◽  
Complicated Task

AbstractBrain tumor is a group of anomalous cells. The brain is enclosed in a more rigid skull. The abnormal cell grows and initiates a tumor. Detection of tumor is a complicated task due to irregular tumor shape. The proposed technique contains four phases, which are lesion enhancement, feature extraction and selection for classification, localization, and segmentation. The magnetic resonance imaging (MRI) images are noisy due to certain factors, such as image acquisition, and fluctuation in magnetic field coil. Therefore, a homomorphic wavelet filer is used for noise reduction. Later, extracted features from inceptionv3 pre-trained model and informative features are selected using a non-dominated sorted genetic algorithm (NSGA). The optimized features are forwarded for classification after which tumor slices are passed to YOLOv2-inceptionv3 model designed for the localization of tumor region such that features are extracted from depth-concatenation (mixed-4) layer of inceptionv3 model and supplied to YOLOv2. The localized images are passed toMcCulloch'sKapur entropy method to segment actual tumor region. Finally, the proposed technique is validated on three benchmark databases BRATS 2018, BRATS 2019, and BRATS 2020 for tumor detection. The proposed method achieved greater than 0.90 prediction scores in localization, segmentation and classification of brain lesions. Moreover, classification and segmentation outcomes are superior as compared to existing methods.

scholarly journals fNIRS Signal Classification Based on Deep Learning in Rock-Paper-Scissors Imagery Task

2021 ◽  
Vol 11 (11) ◽  
pp. 4922
Author(s):  
Tengfei Ma ◽  
Wentian Chen ◽  
Xin Li ◽  
Yuting Xia ◽  
Xinhua Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Total Duration ◽  
Signal Classification ◽  
Imagery Task ◽  
Neural Activities ◽  
Computer Interfaces ◽  
Acquisition Device ◽  
The Time Domain ◽  
The Brain

To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI).

Classification of EEG Mental Patterns by Using Two Scalp Electrodes and Mahalanobis Distance-Based Classifiers

2002 ◽  
Vol 41 (04) ◽  
pp. 337-341 ◽  
Author(s):  
F. Cincotti ◽  
D. Mattia ◽  
C. Babiloni ◽  
F. Carducci ◽  
L. Bianchi ◽  
...  
Keyword(s):  
Mahalanobis Distance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Correct Classification ◽  
Interface Area ◽  
System A ◽  
The Brain ◽  
Full Covariance Matrix ◽  
Recording Electrodes

Summary Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes. Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used. Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes. Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

scholarly journals Application of MRI for the Diagnosis of Neoplasms

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Ewa Bejer-Oleńska ◽  
Michael Thoene ◽  
Andrzej Włodarczyk ◽  
Joanna Wojtkiewicz
Keyword(s):  
Benign Neoplasms ◽  
Young Males ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Mri Scans ◽  
International Statistical Classification ◽  
The Central Nervous System ◽  
Spss Software ◽  
The Brain

Aim. The aim of the study was to determine the most commonly diagnosed neoplasms in the MRI scanned patient population and indicate correlations based on the descriptive variables. Methods. The SPSS software was used to determine the incidence of neoplasms within the specific diagnoses based on the descriptive variables of the studied population. Over a five year period, 791 patients and 839 MRI scans were identified in neoplasm category (C00-D48 according to the International Statistical Classification of Diseases and Related Health Problems ICD-10). Results. More women (56%) than men (44%) represented C00-D48. Three categories of neoplasms were recorded. Furthermore, benign neoplasms were the most numerous, diagnosed mainly in patients in the fifth decade of life, and included benign neoplasms of the brain and other parts of the central nervous system. Conclusions. Males ≤ 30 years of age with neoplasms had three times higher MRI scans rate than females of the same age group; even though females had much higher scans rate in every other category. The young males are more often selected for these scans if a neoplasm is suspected. Finally, the number of MRI-diagnosed neoplasms showed a linear annual increase.

Coding of Temporally Varying Signals in Networks of Spiking Neurons with Global Delayed Feedback

2005 ◽  
Vol 17 (10) ◽  
pp. 2139-2175 ◽  
Author(s):  
Naoki Masuda ◽  
Brent Doiron ◽  
André Longtin ◽  
Kazuyuki Aihara
Keyword(s):  
Information Processing ◽  
Stimulus Frequency ◽  
Spiking Neurons ◽  
Feedforward Networks ◽  
Dynamical Characteristics ◽  
Neural Activities ◽  
Inherent Frequency ◽  
Types Of Information ◽  
The Brain

Oscillatory and synchronized neural activities are commonly found in the brain, and evidence suggests that many of them are caused by global feedback. Their mechanisms and roles in information processing have been discussed often using purely feedforward networks or recurrent networks with constant inputs. On the other hand, real recurrent neural networks are abundant and continually receive information-rich inputs from the outside environment or other parts of the brain. We examine how feedforward networks of spiking neurons with delayed global feedback process information about temporally changing inputs. We show that the network behavior is more synchronous as well as more correlated with and phase-locked to the stimulus when the stimulus frequency is resonant with the inherent frequency of the neuron or that of the network oscillation generated by the feedback architecture. The two eigenmodes have distinct dynamical characteristics, which are supported by numerical simulations and by analytical arguments based on frequency response and bifurcation theory. This distinction is similar to the class I versus class II classification of single neurons according to the bifurcation from quiescence to periodic firing, and the two modes depend differently on system parameters. These two mechanisms may be associated with different types of information processing.

Clinical and neuroimaging features of chronic cerebral ischemia in polycythemia vera

2021 ◽  
pp. 58-62
Author(s):  
G. V. Zyrina ◽  
T. A. Slyusa
Keyword(s):  
Cerebral Ischemia ◽  
Polycythemia Vera ◽  
Mini Mental State Examination ◽  
Comparison Group ◽  
Cognitive Disorders ◽  
Chronic Cerebral Ischemia ◽  
State Examination ◽  
The Brain

The purpose of the study. To study clinical and neuroimaging features of chronic cerebral ischemia (CCI) in polycythemia vera (PV).Materials and methods. 66 patients with PV were examined – the main group (43 men, 23 women; mean age 62.0 ± 3.4 years), of which 64 (97.0%) patients were diagnosed with CCI. The comparison group consisted of 85 patients with CCI (34 men, 51 women; mean age 67.7 ± 4.6 years), who developed against the background of cerebral vascular atherosclerosis and arterial hypertension. To identify cognitive disorders, we used Mini Mental State Examination (MMSE). Insomnia was studied in accordance with the criteria of the International Classification of Sleep ICDS‑22005. The quality of sleep was determined using a questionnaire from the Federal Somnological Center. Neuroimaging (MRI of the brain) was performed on Siemens Symphony 1.5 T and GE Signa 1.5 T tomographs.Results. Subjective symptoms CCI are characterized by a greater representation of asthenic and insomniac disorders. Transient ischemic attacks in patients with PV are significantly more common than in the comparison group, their frequency depends on the duration of PV. The revealed changes in MRI of the brain in the majority of PV patients with CCI are characteristic of multiinfarction vascular encephalopathy; in the comparison group, changes that characteristic for subcortical arteriosclerotic encephalopathy were more often recorded.

NEUROECONOMICS, NEUROPHYSIOLOGY AND THE COMMON CURRENCY HYPOTHESIS

2008 ◽  
Vol 24 (3) ◽  
pp. 419-429 ◽  
Author(s):  
Anthony Landreth ◽  
John Bickle
Keyword(s):  
Rational Choice ◽  
Dopamine Release ◽  
Research Programme ◽  
Neural Mechanism ◽  
The Other ◽  
Common Currency ◽  
The Common ◽  
The Brain ◽  
Electrophysiological Methods

We briefly describe ways in which neuroeconomics has made contributions to its contributing disciplines, especially neuroscience, and a specific way in which it could make future contributions to both. The contributions of a scientific research programme can be categorized in terms of (1) description and classification of phenomena, (2) the discovery of causal relationships among those phenomena, and (3) the development of tools to facilitate (1) and (2). We consider ways in which neuroeconomics has advanced neuroscience and economics along each line. Then, focusing on electrophysiological methods, we consider a puzzle within neuroeconomics whose solution we believe could facilitate contributions to both neuroscience and economics, in line with category (2). This puzzle concerns how the brain assigns reward values to otherwise incomparable stimuli. According to the common currency hypothesis, dopamine release is a component of a neural mechanism that solves comparability problems. We review two versions of the common currency hypothesis, one proposed by Read Montague and colleagues, the other by William Newsome and colleagues, and fit these hypotheses into considerations of rational choice.

scholarly journals Analytical Comparison of Two Emotion Classification Models Based on Convolutional Neural Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Huiping Jiang ◽  
Demeng Wu ◽  
Rui Jiao ◽  
Zongnan Wang
Keyword(s):  
Wavelet Transform ◽  
Classification Accuracy ◽  
Eeg Signal ◽  
Classification Models ◽  
Emotion Classification ◽  
Common Space ◽  
Average Classification Accuracy ◽  
The Common ◽  
The Brain

Electroencephalography (EEG) is the measurement of neuronal activity in different areas of the brain through the use of electrodes. As EEG signal technology has matured over the years, it has been applied in various methods to EEG emotion recognition, most significantly including the use of convolutional neural network (CNN). However, these methods are still not ideal, and shortcomings have been found in the results of some models of EEG feature extraction and classification. In this study, two CNN models were selected for the extraction and classification of preprocessed data, namely, common spatial patterns- (CSP-) CNN and wavelet transform- (WT-) CNN. Using the CSP-CNN, we first used the common space model to reduce dimensionality and then applied the CNN directly to extract and classify the features of the EEG; while, with the WT-CNN model, we used the wavelet transform to extract EEG features, thereafter applying the CNN for classification. The EEG classification results of these two classification models were subsequently analyzed and compared, with the average classification accuracy of the CSP-CNN model found to be 80.56%, and the average classification accuracy of the WT-CNN model measured to 86.90%. Thus, the findings of this study show that the average classification accuracy of the WT-CNN model was 6.34% higher than that of the CSP-CNN.

scholarly journals A Review on Epilepsy and its Management

2020 ◽  
Vol 10 (3) ◽  
pp. 273-279
Author(s):  
V.V. Potnis ◽  
Ketan G. Albhar ◽  
Pritamsinh Arjun Nanaware ◽  
Vishal S. Pote
Keyword(s):  
Developing Countries ◽  
Neurological Disorders ◽  
Genetic Factors ◽  
Future Trends ◽  
Brain Infection ◽  
The World ◽  
Different Types ◽  
Patients With Epilepsy ◽  
The Brain

Today, people face various types of stress in everyday fast life and most people in the world suffer from various neurological disorder. Epilepsy is one of the most common neurological disorders of the brain, affecting about 50 million people around the world, and 90% of them are coming from developing countries. Genetic factors and brain infection, stroke, tumors and epilepsy cause high fever. It imposes a great economic burden on the health systems of countries associated with stigma and discrimination against the patient and also his family in the community, in the workplace, school and home. Many patients with epilepsy suffer from severe emotional stress, behavioral disorders and extreme social isolation. There are many different types of seizure and mechanisms by which the brain generates seizures. The two features of generating seizures are hyperexcitability of neurons and a hyper synchronousneural circuits. A variety of mechanisms alters the balance between excitation and inhibition in predisposing brain local or generalized hyperexcitability region and a hypersynchronia. Purpose of the review is to discuss the history, epidemiology, etiology, pathophysiology, classification of epilepsy, symtomps, diagnosis, management of epilepsy and future trends. Keywords: Anti-epileptic drugs, pathophysiology, seizures, epidemiology, hypersynchrony

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