Rana Fayyaz Ahmad

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This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted(More)
Modern wireless communication demands reliable data communication at high throughput in severe channel conditions like narrowband interference, frequency selective fading due to multipath and attenuation of high frequencies. Traditional single carrier systems address this set of problems by the use of complex, computationally intensive equalization filters.(More)
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(More)
Any kind of visual information is encoded in terms of patterns of neural activity occurring inside the brain. Decoding neural patterns or its classification is a challenging task. Functional magnetic resonance imaging (fMRI) and Electroencephalography (EEG) are non-invasive neuroimaging modalities to capture the brain activity pattern in term of images and(More)
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.(More)
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(More)
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(More)
Cognitive state classification is a challenging task. Many studies were reported using different neuroimaging modalities for classification of the cognitive states of the human brain e.g., EEG, fMRI, MEG etc. However, functional MRI seems to be appropriate for these papers as due to its good spatial resolution and localizing the brain activated regions. In(More)
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(More)
Human brain is considered as complex system having different mental states e.g., rest, active or cognitive states. It is well understood fact that brain activity increases with the cognitive load. This paper describes the cognitive and resting state classification based on EEG features. Previously, most of the studies used linear features. EEG signals are(More)