Saravanakumar D

Bio: Saravanakumar D is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Eye tracking & Vog. The author has an hindex of 3, co-authored 4 publications receiving 20 citations.

A Novel Visual Keyboard System for Disabled People/Individuals Using Hybrid SSVEP Based Brain Computer Interface

Abstract: This paper aims to design a new stimulus paradigm for SSVEP based keyboard system. The proposed paradigm was implemented using black and white checkerboard flickering visual stimuli along with the integration of videooculography (VOG). The on-screen speller was designed using three frequencies. The goal of this study is how to increase more number of targets using less number of stimulus frequencies. It is achieved by the use of VOG data. The study was carried out using 36 selected characters. A webcam is integrated along with the system to obtain VOG data. The webcam captures the images of the eyes, which in turn is used to detect the eye gaze direction. This additional information from VOG overcomes the limitations of SSVEP based spelling system. The extended multivariate synchronization index (EMSI) method is used for SSVEP frequency recognition. Offline and online analysis of the experiment were conducted and the duration of recognition of each character required by the participant was calculated based on the classification accuracy. Online experiment was conducted on 10 subjects to validate the accuracy and information transfer rate (ITR) of the system. An average online detection accuracy of 90.46 % was obtained with the ITR of 65.98 bits/minutes.

A Visual Keyboard System Using Hybrid Dual Frequency SSVEP Based Brain Computer Interface with VOG Integration

Abstract: The focus of this paper is to increase the number of targets and classification rate in the SSVEP-BCI visual keyboard system. The dual frequency steady state visual evoked potential (SSVEP) and video-oculography (VOG) based hybrid system has been developed in this study. The visual stimuli (targets) were designed using dual frequency SSVEP method. This method could create more targets through a suitable combination of limited number of frequencies. The keyboard screen was divided into three sections (left, middle and right), and each section visual stimuli/keys were designed with a unique set of frequencies. The webcam based video-oculography was used to detect the direction of the eye gaze. This selection reduces the issue of misclassification of SSVEP frequencies. Extended multivariate synchronization index (EMSI) method is used for SSVEP frequency recognition. Both online and offline experiments were conducted on 10 subjects and an average online detection accuracy of 94.987% was obtained with the information transfer rate (ITR) of 82.786 bits/minutes.

Sensors and Systems for Physical Rehabilitation and Health Monitoring-A Review.

Abstract: The use of wearable equipment and sensing devices to monitor physical activities, whether for well-being, sports monitoring, or medical rehabilitation, has expanded rapidly due to the evolution of sensing techniques, cheaper integrated circuits, and the development of connectivity technologies. In this scenario, this paper presents a state-of-the-art review of sensors and systems for rehabilitation and health monitoring. Although we know the increasing importance of data processing techniques, our focus was on analyzing the implementation of sensors and biomedical applications. Although many themes overlap, we organized this review based on three groups: Sensors in Healthcare, Home Medical Assistance, and Continuous Health Monitoring; Systems and Sensors in Physical Rehabilitation; and Assistive Systems.

Summary of over Fifty Years with Brain-Computer Interfaces-A Review.

Abstract: Over the last few decades, the Brain-Computer Interfaces have been gradually making their way to the epicenter of scientific interest. Many scientists from all around the world have contributed to the state of the art in this scientific domain by developing numerous tools and methods for brain signal acquisition and processing. Such a spectacular progress would not be achievable without accompanying technological development to equip the researchers with the proper devices providing what is absolutely necessary for any kind of discovery as the core of every analysis: the data reflecting the brain activity. The common effort has resulted in pushing the whole domain to the point where the communication between a human being and the external world through BCI interfaces is no longer science fiction but nowadays reality. In this work we present the most relevant aspects of the BCIs and all the milestones that have been made over nearly 50-year history of this research domain. We mention people who were pioneers in this area as well as we highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many. Aiming to fully understand how the human brain works is a very ambitious goal and it will surely take time to succeed. However, even that fraction of what has already been determined is sufficient e.g., to allow impaired people to regain control on their lives and significantly improve its quality. The more is discovered in this domain, the more benefit for all of us this can potentially bring.

Brain-Computer Interface Speller Based on Steady-State Visual Evoked Potential: A Review Focusing on the Stimulus Paradigm and Performance.

Abstract: The steady-state visual evoked potential (SSVEP), measured by the electroencephalograph (EEG), has high rates of information transfer and signal-to-noise ratio, and has been used to construct brain–computer interface (BCI) spellers. In BCI spellers, the targets of alphanumeric characters are assigned different visual stimuli and the fixation of each target generates a unique SSVEP. Matching the SSVEP to the stimulus allows users to select target letters and numbers. Many BCI spellers that harness the SSVEP have been proposed over the past two decades. Various paradigms of visual stimuli, including the procedure of target selection, layout of targets, stimulus encoding, and the combination with other triggering methods are used and considered to influence on the BCI speller performance significantly. This paper reviews these stimulus paradigms and analyzes factors influencing their performance. The fundamentals of BCI spellers are first briefly described. SSVEP-based BCI spellers, where only the SSVEP is used, are classified by stimulus paradigms and described in chronological order. Furthermore, hybrid spellers that involve the use of the SSVEP are presented in parallel. Factors influencing the performance and visual fatigue of BCI spellers are provided. Finally, prevailing challenges and prospective research directions are discussed to promote the development of BCI spellers.

EOG-Based HCI System for Quadcopter Navigation

Abstract: Human–computer interface (HCI) systems are extending their boundaries in our daily life and becoming an important subject in biomedical engineering. Electrooculogram (EOG) signal as an input for such systems stems from the corneoretinal standing potential, which can be used for monitoring human eye rotation. Higher amplitude, better signal-to-noise ratio, and much easier recording conditions compared with electroencephalography, make it an important input modality for HCI systems. In this article, real-time processing and cost-effective (< 100$) HCI system was designed and developed based on the EOG signals. The required electrodes were embedded in updated eyeglasses for easy electrode placement over the subject’s face. EOG signals were acquired by the subject’s eye movement toward the four middle parts of the screen edges of a laptop placed in front of them. The system training for each subject ameliorated system withstanding against the blink and wrinkle artifact. Finally, the required commands for quadcopter navigation (up, down, left, and right) generated with 0.6-s total delay and 94.8% of system accuracy in detecting the correct eye movements. A real quadcopter navigation experiment based on the standard navigation interface and the developed HCI system represented the system accuracy, robustness, and usefulness.