Dheeraj Rathee

The objective is to evaluate the impact of EEG referencing schemes and spherical surface Laplacian (SSL) methods on the classification performance of motor-imagery (MI) related brain-computer interface systems. Two EEG referencing schemes: common referencing, common average referencing (CAR) and three surface Laplacian methods: current source density (CSD), finite difference method, and SSL using realistic head model, were implemented separately for pre-processing of the EEG signals recorded at… Show more

The objective is to evaluate the impact of EEG referencing schemes and spherical surface Laplacian (SSL) methods on the classification performance of motor-imagery (MI) related brain-computer interface systems. Two EEG referencing schemes: common referencing, common average referencing (CAR) and three surface Laplacian methods: current source density (CSD), finite difference method, and SSL using realistic head model, were implemented separately for pre-processing of the EEG signals recorded at the scalp. A combination of filter bank common spatial filter for features extraction and support vector machine for classification was used for both pairwise binary classifications and four-class classification of MI tasks. The study provides three major outcomes: (i) the CSD method performs better than CR providing a significant improvement of 3.02 % and 5.59 % across six binary classification tasks and four-class classification task, respectively; (ii) the combination of a greater number of channels at the pre-processing stage as compared to the feature extraction stage yields better classification accuracies for all the Laplacian methods; (iii) the efficiency of all the surface Laplacian methods reduced significantly in the case of a fewer number of channels considered during the pre-processing. Show less

The level of conscious experience can be effectively and reversibly altered by the administration of sedative agents. Several studies attempted to explore the variations in frontal-parietal network during propofol-induced sedation. However, contradictory outcomes warrant further investigations. In this study, we implemented the Neural Gas algorithm-based delay symbolic transfer entropy (NG-dSTE) for investigation of frontal-parietal-occipital (F-P-O) network using scalp EEG signals recorded… Show more

The level of conscious experience can be effectively and reversibly altered by the administration of sedative agents. Several studies attempted to explore the variations in frontal-parietal network during propofol-induced sedation. However, contradictory outcomes warrant further investigations. In this study, we implemented the Neural Gas algorithm-based delay symbolic transfer entropy (NG-dSTE) for investigation of frontal-parietal-occipital (F-P-O) network using scalp EEG signals recorded during altered levels of consciousness. Our results show significant disruption of the F-P-O network during mild and moderate levels of propofol sedation. In particular, the interaction between frontal and parietal-occipital region is highly disturbed. Moreover, we found measurable effect of sedation on local interactions in the frontal network whereas parietal-occipital network experienced least variations. The results support the conclusion that the connectivity based features can be utilized as reliable biomarker for assessment of sedation levels effectively. Show less

Objective: The majority of the current approaches of connectivity based BCI systems focus on distinguishing between different motor imagery (MI) tasks. Brain regions associated with MI are anatomically close to each other, hence these BCI systems suffer from low performances. Our objective is to introduce single-trial connectivity feature based BCI system for cognition imagery (CI) based tasks
wherein the associated brain regions are located relatively far away as compared to those for… Show more

Objective: The majority of the current approaches of connectivity based BCI systems focus on distinguishing between different motor imagery (MI) tasks. Brain regions associated with MI are anatomically close to each other, hence these BCI systems suffer from low performances. Our objective is to introduce single-trial connectivity feature based BCI system for cognition imagery (CI) based tasks
wherein the associated brain regions are located relatively far away as compared to those for MI.
Approach: We implemented time-domain partial granger causality (PGC) for the estimation of the connectivity features in a BCI setting. The proposed hypothesis has been verified with two publically available datasets involving MI and CI tasks.
Main results: The results support the conclusion that connectivity based features can provide a better performance than a classical signal processing framework based on bandpower features coupled with spatial filtering for CI tasks, including word generation, subtraction, and spatial navigation. These results show for the first time that connectivity features can provide a reliable performance for imagery-based BCI system.
Significance: We show that single-trial connectivity features for mixed imagery tasks (i.e. combination of CI and MI) can outperform the features obtained by current state-of-the-art method and hence can be successfully applied for BCI applications. Show less

Connectivity analysis has become an essential tool for the evaluation of functional brain dynamics. The functional connectivity between different parts of the brain, or between different sensors, is assumed to provide key information for the discrimination of brain responses. In this study, we propose an estimation of effective cortical connectivity measures in frontal and parietal areas of human brain during four different Motor Imagery (MI) tasks. Feedback based brain-computer
interface… Show more

Connectivity analysis has become an essential tool for the evaluation of functional brain dynamics. The functional connectivity between different parts of the brain, or between different sensors, is assumed to provide key information for the discrimination of brain responses. In this study, we propose an estimation of effective cortical connectivity measures in frontal and parietal areas of human brain during four different Motor Imagery (MI) tasks. Feedback based brain-computer
interface (BCI) technology has been successfully implemented for recovery of stroke patients as it can enhance the neural plasticity in brain areas associated with motor execution. However, it is still challenging to obtain reliable information regarding improvement in neural functioning during rehabilitation and its neuro-physiological dynamics. Brain connectivity is a reliable biomarker associated with brain functionality. Here, we evaluate to what extent partial granger causality can
provide information in form of effective neural connectivity that can differentiate motor imagery tasks. Our results on nine subjects using the EEG dataset (BCI competition 2008 dataset 2A) show distinct connectivity patterns for all four MI classes, and higher information flow in the fronto-parietal
network during task phase as compared to non-task phase. The results support the conclusion that effective connectivity analysis through partial granger causality can provide key information about neural interactions specific to different MI tasks. Moreover these interactions can be utilized as reliable biomarkers for assessment of motor recovery during stroke rehabilitation. Show less

Administration of sedation is a common practice in most of the clinical and/or surgical interventions, especially in critical care environment. Although it is necessary to relieve critically ill patients from unpleasant physical and psychological experiences, continuous assessment of sedation is highly crucial as improper sedation practice may lead to serious complications such as delirium. To deal with these concerns, bio-markers for sedative-induced unconsciousness must be… Show more

Administration of sedation is a common practice in most of the clinical and/or surgical interventions, especially in critical care environment. Although it is necessary to relieve critically ill patients from unpleasant physical and psychological experiences, continuous assessment of sedation is highly crucial as improper sedation practice may lead to serious complications such as delirium. To deal with these concerns, bio-markers for sedative-induced unconsciousness must be manifested
through objective brain signal analyses. A balanced amalgamation of cortical integration and diferentiation has been proposed by several theoretical developments in the ield of consciousness, which involve substantial variations in information transfer networks across multiple scales (Tononi, 2004). These indings motivate further research to correlate these variations with altered states of consciousness and quantify them. In this work, Partial Granger Causality (PGC) (Guo et al., 2008) has been utilised to assess the efect of sedation on the Fronto-Centro-Parietal cortical network. Show less

There has been a tremendous growth in the field of neural controlled robotic systems having utility limited not only to physically challenged but also for improving performance of healthy users. In this work, real time EEG signal is acquired for deliberate eye blink action in MATLAB and Independent Component Analysis decomposition is done and corresponding epochs are extracted. The deliberate action shows significant rise in the event related potential from 0μV to 175.1μV at frontal channel… Show more

There has been a tremendous growth in the field of neural controlled robotic systems having utility limited not only to physically challenged but also for improving performance of healthy users. In this work, real time EEG signal is acquired for deliberate eye blink action in MATLAB and Independent Component Analysis decomposition is done and corresponding epochs are extracted. The deliberate action shows significant rise in the event related potential from 0μV to 175.1μV at frontal channel AF3. The average ERP of all dataset epoch with ERP scalp maps at selected latencies are plotted. The traces show the distribution of average potential across cerebral cortex regions at the latency of 547ms corresponding to maximum ERP data variance during forced eye blink. Scalp maps also show that frontal regions are activated at 3Hz and 10Hz thus, representing the activation of delta band power (0-4Hz) and alpha band power (8-13Hz). The component activation is used to generate an ‘active high’ output during deliberate eye blinking to play an audio file. This reflects the use of EEG as a trigger in developing more intuitive Brain Computer Interfaces in real time scenario. Show less

Non-invasive Brain Computer Interface like Electroencephalography (EEG) which directly taps neurological signals, is being widely explored these days to connect paralytic patients / elderly with the external environment. However, in India the research is confined to laboratory settings and is not reaching the mass for rehabilitation purposes. An attempt has been made in this paper to analyze real time acquired EEG signal using cost effective and portable headset unit EMOTIV. Signal processing… Show more

Non-invasive Brain Computer Interface like Electroencephalography (EEG) which directly taps neurological signals, is being widely explored these days to connect paralytic patients / elderly with the external environment. However, in India the research is confined to laboratory settings and is not reaching the mass for rehabilitation purposes. An attempt has been made in this paper to analyze real time acquired EEG signal using cost effective and portable headset unit EMOTIV. Signal processing of real time acquired EEG is done using eeglab in MATLAB and EDF Browser application software platforms. Independent Component Analysis algorithm of eeglab is explored to identify deliberate eye blink in the attained neural signal. Time Frequency transforms and Data statistics obtained using eeglab along with component activation results of edf browser clearly indicate voluntary eye blink in AF3 channel. The spectral analysis indicates dominant frequency component at 1.536000Hz representing the delta wave component of EEG during voluntary eye blink action. An algorithm is further designed to generate an active high signal based on thoughtful eye blink that can be used for plethora of control applications for rehabilitation. Show less

In healthcare domain, Wireless Body Area Network (WBAN) has transpired as a prominent technology which is capable of providing better methods of real time patient health monitoring at hospitals, asylums and even at their homes. In recent times, WBAN has gained great interest and proved one of the most explored technologies by health care facilities because of its vital role and wide range of application in clinical sciences. WBAN involve communication between very small sensor nodes with… Show more

In healthcare domain, Wireless Body Area Network (WBAN) has transpired as a prominent technology which is capable of providing better methods of real time patient health monitoring at hospitals, asylums and even at their homes. In recent times, WBAN has gained great interest and proved one of the most explored technologies by health care facilities because of its vital role and wide range of application in clinical sciences. WBAN involve communication between very small sensor nodes with frequently changing environment, hence lots of issues still need to be addressed. Some of the major issues are Physical layer issues, interoperability & mobility issue, reliability, resource management, usability, Energy consumption and QoS issues. This research paper includes a comprehensive survey of recent trends in WBAN research, provides prospective solutions to some major issues using cognitive approach and a proposed concept of Cognitive Radio based WBAN architecture. Thus a conventional WBAN architecture can be improvised to an adaptive, more reliable and efficient WBAN system using Cognitive based approach. Show less

The ability of EEG signals to portray thoughts, feelings and unspoken words is being widely explored these days and has become a potential area of research. This is based upon the fact that electrical impulse is generated when a precise word is thought at the brain even before it reaches the vocal cords. These unspoken speech impulses can be analyzed and translated into distinct words which may enable a locked-in patient to communicate with the world. Based on the extensive survey on brain… Show more

The ability of EEG signals to portray thoughts, feelings and unspoken words is being widely explored these days and has become a potential area of research. This is based upon the fact that electrical impulse is generated when a precise word is thought at the brain even before it reaches the vocal cords. These unspoken speech impulses can be analyzed and translated into distinct words which may enable a locked-in patient to communicate with the world. Based on the extensive survey on brain mapping techniques, EEG database shall be comprehended to design a proposal to achieve the target. Neurological advancements have led to the development of Emotiv unit which is a personal interface for human computer interaction, which shall be used to acquire and store EEG database. An algorithm shall then be developed for artefact removal and brain mapping so that word specific neural signals can be characterized before being vocalized. Mathematical tools like Fuzzy logic or Artificial Neural Network shall be explored for mapping the brain. Instrumentation shall be developed to convert electrical impulse of these unspoken words generated at the brain into digital form so that it can be converted into synthesized speech. The design has the potential to use Brain computer interface to assist locked-in patients to translate their thoughts into speech in real time applications. Show less

A novel compact CPW-fed Sierpinski fractal antenna is presented. A Fractal radiating of the Sierpinski Gasket with Varying scale factor d = 1.5 (for first and second iteration) and d = 2 (for third iteration). The antenna was designed by changing various triangular widths to accommodate various bands of interest A quasi log periodic performance is achieved for the operating bands of varying scale factor. The antenna is printed on a single layer dielectric substrate and fed by 50 W CPW… Show more

A novel compact CPW-fed Sierpinski fractal antenna is presented. A Fractal radiating of the Sierpinski Gasket with Varying scale factor d = 1.5 (for first and second iteration) and d = 2 (for third iteration). The antenna was designed by changing various triangular widths to accommodate various bands of interest A quasi log periodic performance is achieved for the operating bands of varying scale factor. The antenna is printed on a single layer dielectric substrate and fed by 50 W CPW transmission line. Experimental results i.e., Measured & Simulated Antenna Performance and radiation patterns show that there is strong similarity between patterns through four bands. It was specifically stated that these similarities are specifically remarkable at 2.9, 4.5, 6.9 and 13.8 GHz. Show less