Neethu Robinson

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OBJECTIVE Studies have shown that low frequency components of brain recordings provide information on voluntary hand movement directions. However, non-invasive techniques face more challenges compared to invasive techniques. APPROACH This study presents a novel signal processing technique to extract features from non-invasive electroencephalography (EEG)(More)
A brain-computer interface (BCI) acquires brain signals, extracts informative features, and translates these features to commands to control an external device. This paper investigates the application of a noninvasive electroencephalography (EEG)-based BCI to identify brain signal features in regard to actual hand movement speed. This provides a more(More)
The Electroencephalogram (EEG) based Brain Computer Interface (BCI) is a non-invasive system to acquire, decode and convert brain signals into control signals for an external device. The Motor-Imagery based BCI (MI-BCI) efficiently decodes the brain signals from the imagination of movement but the performance is limited by the number of commands such as(More)
OBJECTIVE The various parameters that define a hand movement such as its trajectory, speed, etc, are encoded in distinct brain activities. Decoding this information from neurophysiological recordings is a less explored area of brain-computer interface (BCI) research. Applying non-invasive recordings such as electroencephalography (EEG) for decoding makes(More)
The decoding of hand movement kinematics using non-invasive data acquisition techniques is a recent area of research in Brain Computer Interface (BCI). In this work, we use an Electroencephalography (EEG) based BCI to decode directional information from the brain data collected during an actual hand movement experiment. The objective is to find the(More)
A brain-computer interface (BCI) based on near-infrared spectroscopy (NIRS) could act as a tool for rehabilitation of stroke patients due to the neural activity induced by motor imagery aided by real-time feedback of hemodynamic activation. When combined with functional electrical stimulation (FES) of the affected limb, BCI is expected to have an even(More)
Several studies have reported on the feasibility of using NIRS for developing brain-computer interface (BCI) devices as an alternate mode of communication and environmental control for the disabled, including its application in neurofeedback training. In the present study, we report the development of a real-time SVM based pattern classification and(More)
Biometric recognition of persons based on unique features extracted from brain signals is an emerging area of research nowadays, on account of the subject-specificity of human neural activity. This paper proposes an online Electroencephalogram (EEG) based biometric authentication system using band power features extracted from alpha, beta and gamma bands,(More)