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Electroencephalography (EEG) captures electrophysiological signatures of cortical events from the scalp with high-dimensional electrode montages. Usually, excessive sources produce outliers and potentially affect the actual event related sources. Besides, EEG manifests inherent inter-subject variability of the brain dynamics, at the resting state and/or under the performance of task(s), caused probably...
Localizing event-related cortical sources is a key factor while developing a computationally efficient Brain Computer Interface (BCI). This paper proposes a unified application of wavelet-based Maximum Entropy on the Mean (wMEM), as a channel selection method, for classifying two motor imagery (MI) tasks using optimal electroencephalography (EEG) sources. The EEG data, which are collected from publicly...
Multi-channel electroencephalography (EEG) recordings require excessive computation and sometimes engender outliers, which make brain computer interface (BCI) systems inefficient. Thus, optimal channel selection becomes a key factor for developing a more comfortable BCI. This study emphasized on a time-frequency (T-F) coherence method, called as Wavelet Coherence (WC), for selecting lesser number...
Diabetes mellitus is a chronic disease and its prolonged existence may cause proliferation of diverse abnormalities in human physiological system. Maintaining a healthy lifestyle can improve the condition of a diabetic patient. However, continuous monitoring of diabetes level is necessary for adapting diets and others for healthy life, which requires clinical settings or medical consultation. Thus,...
Unification of spatial brain dynamics in multiclass brain computer interface (BCI) paradigm reduces computational latencies by using lesser number of electrodes from the sensorimotor regions of the brain. We employ reduced number of channels without compromising performance notably. We apply three spatial filtering methods, i.e., Common Spatial Pattern (CSP), Regularized Common Spatial Pattern (RCSP)...
This paper features the spatial characteristics of the brain towards brain-computer interface (BCI) research. A study on motor imagery (MI) based BCI has been carried out and important implications are identified. Common Spatial Pattern (CSP) is applied to the EEG signals before proceeding to the classification. The primary focus of this research is to utilize the spatial dynamics of the brain to...
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