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)
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)
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)
Recently, studies have reported the use of Near Infrared Spectroscopy (NIRS) for developing Brain-Computer Interface (BCI) by applying online pattern classification of brain states from subject-specific fNIRS signals. The purpose of the present study was to develop and test a real-time method for subject-specific and subject-independent classification of(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 neuro engineering research over the past decades has established Electroencephalography based Brain Computer Interface (EEG-BCI) systems as an efficient means of decoding brain activity. Motor control BCI is a category of BCI that analyzes neural activity recorded over sensory motor area to classify or decode intended motor tasks. For a BCI system, it(More)