Rana Fayyaz Ahmad

Simultaneous EEG-fMRI for working memory of the human brain

Memory plays an important role in human life. Memory can be divided into two categories, i.e., long term memory and short term memory (STM). STM or working memory (WM) stores information for a short span of time and it is used for information manipulations and fast response activities. WM is generally involved in the higher cognitive functions of the brain. Different studies have been carried out by researchers to understand the WM process. Most of these studies were based on neuroimaging modalities like fMRI, EEG, MEG etc., which use standalone processes. Each neuroimaging modality has some pros and cons. For example, EEG gives high temporal resolution but poor spatial resolution. On the other hand, the fMRI results have a high spatial resolution but poor temporal resolution. For a more in depth understanding and insight of what is happening inside the human brain during the WM process or during cognitive tasks, high spatial as well as high temporal resolution is desirable. Over the past decade, researchers have been working to combine different modalities to achieve a high spatial and temporal resolution at the same time. Developments of MRI compatible EEG equipment in recent times have enabled researchers to combine EEG-fMRI successfully. The research publications in simultaneous EEG-fMRI have been increasing tremendously. This review is focused on the WM research involving simultaneous EEG-fMRI data acquisition and analysis. We have covered the simultaneous EEG-fMRI application in WM and data processing. Also, it adds to potential fusion methods which can be used for simultaneous EEG-fMRI for WM and cognitive tasks.

Optimization and development of concurrent EEG-fMRI data acquisition setup for understanding neural mechanisms of brain

Electroencephalography (EEG) and functional magnetic resonance (fMRI) both are considered as non-invasive neuroimaging modalities. Both are used for understanding brain functionalities in cognitive neuroscience as well as in clinical applications. EEG gives high temporal resolution and it has poor spatial resolution. On the other hand, fMRI has very high spatial resolution and poor temporal resolution. For deep understanding of neural mechanisms inside human brain, it is desirable to get the higher spatiotemporal resolution of human brain at the same time. Concurrent EEG-fMRI data recording solve the problem of higher spatiotemporal resolution. It can be also helpful to understand the neural mechanism inside human brain effectively. The concurrent EEG-fMRI recording requires MRI compatible EEG equipment which can be placed inside the higher magnetic field of MRI scanner and also synchronization is required to make setup concurrent. To get higher signal to noise ratio (SNR), optimization of data acquisition parameters plays a significant role. In this paper, we discussed the some real issues during data acquisition and their optimization. We have developed the concurrent EEG-fMRI setup and also successfully recorded the EEG-fMRI data concurrently by optimizing the data acquisition parameters involved. Artifacts have been removed from the data and further, data fusion framework is proposed for combine analysis of EEG and fMRI data.

A proposed frame work for real time epileptic seizure prediction using scalp EEG

Epilepsy is the brain disorder disease having more than 50 million people worldwide. The treatment for epilepsy is medication and surgery. Some patients are not cured with medicine and surgery. One third of the patients still remain with uncontrolled epilepsy. They need constant monitoring for epileptic seizures. Better treatment can be provided by the doctors or precautionary measures can be taken by the patients themselves if any abnormal brain activity or seizure is predicted before its occurrence. The pre-ictal period has some information about the occurrence of epileptic seizure in EEG signals. The brain behaves normal in inter-ictal and postictal periods. For epilepsy, long duration EEG recording are required from days to week. This keeps the patients to stay in the hospital for many days. Our proposed methodology is to predict the epileptic seizure and monitor the brain abnormality in real time. Still there is no epileptic seizure prediction algorithm using EEG available for clinical applications. Our aim is to study and develop a good epileptic seizure prediction algorithm/method with high value of sensitivity and specificity using scalp EEG i-e noninvasive approach. Also a comprehensive survey is done to find the limitations and research issues related to this. The proposed pattern recognition approach has great potential to be used in real time monitoring for epileptic patients and it can be helpful in improving the quality of life of the patients.

Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

EEG spectral analysis during complex cognitive task at occipital

Normal oscillations in different frequency bands have an important role in cognitive processing in the frontal region. The key issue is whether frequency oscillations of Electroencephalography (EEG) are related to cognitive task or not in occipital region. All frequency bands delta (δ), theta (θ), alpha (α), beta (β) and gamma (γ) are involved in brain tasks. This study is conducted to investigate the functional relationship between EEG frequency bands and the cognitive task. In various studies theta, alpha and beta bands are discussed for cognitive tasks; however there are few studies which have been focused on delta band for cognitive tasks. In this paper, behavior of θ, α, β and γ is described but the primary focus is on delta (δ) band during cognitive task in occipital region as it had been ignored in the literature. In conclusion, this study explains how different frequencies change during cognitive task as compared to base line (eyes open) in occipital region and the results show that there is an increase in power during the task in delta and theta band.

Discriminating the different human brain states with EEG signals using Fractal dimension: A nonlinear approach

EEG signals are measured on scalp of the human brain and are widely used to address the clinical as well as in modern application like brain computer interfacing (BCI) and gaming. Feature extraction plays a fundamental role for good classification purposes. EEG features commonly extracted are linear as well as nonlinear. Nonlinear approaches are used when the complexity of EEG signals increases. Nonlinear features like correlation dimension (CD), Lyapunov exponents, approximate entropy requires higher computational complexity. On other hand Fractal dimension (FD) requires less computations. Therefore, Fractal dimension are widely used in engineering and biological sciences. In our paper, Fractal dimension has been selected to discriminate the different brain states. EEG data from 08 healthy participants have been acquired during eyes open, eyes close and during IQ task. Fractal dimensions have been computed on the EEG data acquired. Using Fractal dimension, we have successfully discriminated the different brain conditions/states like eyes open, eyes close and IQ task. Results have shown better discrimination between mental task and active brain conditions with 91.66 % accuracy using SVM classifier as compared to other classifiers. This approach can be used for fast decision making and pattern matching based on the selected epoch of the EEG signal using nonlinear approach.

Prediction of Human Brain Activity Using Likelihood Ratio Based Score Fusion

Human brain has a complex structure with the billions of neurons, so it is a difficult and challenging task to predict the behavior of human brain. Different methods and classifiers are used to measure and classify the brain activities with higher accuracy and reliability. In this paper, instead of using mostly used classifier (support vector machine), prediction of the brain activity is done by estimating the match score densities. This method is based on likelihood ratio test which helps in finding the optimal combination of match scores. The distributions of match scores are modeled for different classes based on density score fusion in which the densities of different classes are estimated from the training data set and match scores are found by fusing the estimated densities with the testing data. The fusion is done with the data extracted from distributed activation patterns using multivariate pattern analysis (MVPA) against a visual task. MVPA is an intense strategy which helps in better understanding of the human brain. The match score-based technique is used in different biometric systems but never been used for the prediction of brain activity. In order to test the performance of proposed method, prediction accuracy is compared with the support vector machine using two data sets of different modalities, one is electroencephalography (EEG) and the other is functional magnetic resonance imaging (fMRI). The results show that the proposed method predicts the novel data with improved accuracy of 66.1% and 69.3% compared with support vector machine which have 64.15% and 65.7% for fMRI and EEG data sets, respectively.

Simultaneous EEG-fMRI data acquisition during cognitive task

Functional Magnetic Resonance Imaging (fMRI) has very high spatial resolution as compared to the Electroencephalography (EEG) which on other hand has very high temporal resolution. The pros and cons of the EEG and fMRI are complementary to each other. Simultaneous EEG-fMRI data recording solve the problem to get high spatial and temporal resolution at the same time to study the brain dynamics in efficient manner. EEG-fMRI integration is a new approach to study human brain activity. Recent developments in MRI compatible EEG equipment made this integration more easy and attractive in cognitive neuroscience. The simultaneous EEG-fMRI data acquisition gives us the better information for all activated areas of the brain to understand the cognitive processes. We developed data acquisition setup for simultaneous EEG-fMRI for cognitive tasks. Also data recording has been done for two healthy participants as a pilot study which will be further continued on other healthy participants as well as on patients. The EEG and fMRI data is pre-processed and artefacts are removed. This combined EEG-fMRI data can be further used in multimodal data integration or data fusion which can give the better results and understanding of the cognitive processes of human brain as compared to analysing EEG and fMRI data separately.

Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach

BACKGROUND Classification of the visual information from the brain activity data is a challenging task. Many studies reported in the literature are based on the brain activity patterns using either fMRI or EEG/MEG only. EEG and fMRI considered as two complementary neuroimaging modalities in terms of their temporal and spatial resolution to map the brain activity. For getting a high spatial and temporal resolution of the brain at the same time, simultaneous EEG-fMRI seems to be fruitful. METHODS In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes. RESULTS Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature. CONCLUSIONS The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns.

Disaster monitoring in urban and remote areas using satellite stereo images: A depth estimation approach

Disaster can occur any time either it is natural or human made. Urban areas are congested and highly populated in big cities. Any kind of natural disaster like earthquake or flood can damage the whole city or partially, depending upon the intensity. To enhance the rescue activities and saving lives; disaster monitoring and management plays a critical role. Many techniques are used these days to monitor and identify the intensity of damage e.g., aerial view of city, SAR, LiDAR, site survey or acquiring the satellite images. Remote sensing to monitor natural disasters has been proven useful for detection of earthquakes, land sliding, flooding, wildfire and volcanic activity. In our paper, we have proposed a method to monitor the disaster using satellite stereo images which can detect and measure the intensity of damage. For preliminary study, we have acquired the satellite images from Quick bird. Depth estimation algorithm on stereo images produces good results to monitor the urban and remote areas infrastructure as compared to the traditional methods like video surveillance.