Rana Fayyaz Ahmad

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—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(More)
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)
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)
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)