D. Saravanakumar

Bio: D. Saravanakumar is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Eye tracking & Asynchronous communication. The author has an hindex of 3, co-authored 5 publications receiving 19 citations.

A Novel EOG based Synchronous and Asynchronous Visual Keyboard System

Abstract: In this study, a high performance electrooculogram (EOG) based synchronous and asynchronous visual keyboard system is designed with large number of targets. The proposed system overcomes the limitations in the conventional EEG based visual keyboard system which has to compromise with either accuracy or typing speed of the system. In this study, we first proposed a synchronous visual keyboard system with 36 targets which includes alphabets, numbers and space. Later, we proposed an asynchronous visual keyboard system where the user can have control on the system. Ten subjects were taken for evaluating the performance of the proposed keyboard systems. A cue guided online experimental procedure was conducted on all the subjects in synchronous and asynchronous case. The average classification accuracy of 94.2% and 98.79% were obtained in synchronous and asynchronous cases respectively. Four subjects show 100% accuracy in the asynchronous case with average information transfer rate (ITR) of 66.71 bits/minute. This result shows that the developed system outperforms conventional keyboard system.

A Visual Spelling System Using SSVEP Based Hybrid Brain Computer Interface with Video-Oculography

Abstract: A hybrid Brain Computer Interface (BCI) system is developed using steady state visual evoked potential (SSVEP) along with the video-oculogram (VOG). The keyboard layout is designed with 23 characters flickering at selected frequencies. The template matched webcam images provide the direction of eye gaze information to localize the user gazing space on the visual keyboard/display. This spatial localization helps to use/make multiple stimuli of the same frequency. The canonical correlation analysis (CCA) is used for SSVEP frequency recognition. The experiments were conducted on 8 subjects for both online and offline analysis. Based on the classification accuracy from offline analysis, the subject specific SSVEP stimulus duration and the optimal number of EEG channels were selected for online analysis. An average online classification accuracy of 93.5% was obtained with the information transfer rate (ITR) of 96.54 bits/min without inter character identifying delay. When a delay of 0.5 s is introduced between stimulus window the ITR of 80.17 bits/min is realized.

A Brain Computer Interface Based Visual Keyboard System Using SSVEP and Electrooculogram

Abstract: This study aims to design a steady-state visual evoked potential (SSVEP) based, on-screen keyboard/speller system along with the integration of electrooculogram (EOG). The characters/targets were designed using the pattern reversal square checkerboard flickering visual stimuli. In this study, twenty-three characters were randomly selected and their corresponding visual stimuli were designed using five frequencies (6, 6.667, 7.5, 8.57 and 10 Hz). The keyboard layout was divided into nine regions and each region was identified by using the subject’s eye gaze information with the help of EOG data. The information from the EOG was used to locate the area on the visual keyboard/display, where the subject is looking. The region identification helps to use the same frequency valued visual stimuli more than once on the keyboard layout. In this proposed study, more targets were designed using less number of visual stimulus frequencies by integrating EOG with the SSVEP keyboard system. The multi-threshold algorithm and extended multivariate synchronization index (EMSI) method were used for eye gaze detection and SSVEP frequency recognition respectively. Ten healthy subjects were recruited for validating the proposed visual keyboard system.

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.

Feature Extraction Method Based on Filter Banks and Riemannian Tangent Space in Motor-Imagery BCI

Abstract: Optimal feature extraction for multi-category motor imagery brain-computer interfaces (MI-BCIs) is a research hotspot. The common spatial pattern (CSP) algorithm is one of the most widely used methods in MI-BCIs. However, its performance is adversely affected by variance in the operational frequency band and noise interference. Furthermore, the performance of CSP is not satisfactory when addressing multi-category classification problems. In this work, we propose a fusion method combining Filter Banks and Riemannian Tangent Space (FBRTS) in multiple time windows. FBRTS uses multiple filter banks to overcome the problem of variance in the operational frequency band. It also applies the Riemannian method to the covariance matrix extracted by the spatial filter to obtain more robust features in order to overcome the problem of noise interference. In addition, we use a One-Versus-Rest support vector machine (OVR-SVM) model to classify multi-category features. We evaluate our FBRTS method using BCI competition IV dataset 2a and 2b. The experimental results show that the average classification accuracy of our FBRTS method is 77.7% and 86.9% in datasets 2a and 2b respectively. By analyzing the influence of the different numbers of filter banks and time windows on the performance of our FBRTS method, we can identify the optimal number of filter banks and time windows. Additionally, our FBRTS method can obtain more distinctive features than the filter banks common spatial pattern (FBCSP) method in two-dimensional embedding space. These results show that our proposed method can improve the performance of MI-BCIs.

Feature Extraction Method Based on Filter Banks and Riemannian Tangent Space in Motor-Imagery BCI

Abstract: Optimal feature extraction for multi-category motor imagery brain-computer interfaces (MI-BCIs) is a research hotspot. The common spatial pattern (CSP) algorithm is one of the most widely used methods in MI-BCIs. However, its performance is adversely affected by variance in the operational frequency band and noise interference. Furthermore, the performance of CSP is not satisfactory when addressing multi-category classification problems. In this work, we propose a fusion method combining Filter Banks and Riemannian Tangent Space (FBRTS) in multiple time windows. FBRTS uses multiple filter banks to overcome the problem of variance in the operational frequency band. It also applies the Riemannian method to the covariance matrix extracted by the spatial filter to obtain more robust features in order to overcome the problem of noise interference. In addition, we use a One-Versus-Rest support vector machine (OVR-SVM) model to classify multi-category features. We evaluate our FBRTS method using BCI competition IV dataset 2a and 2b. The experimental results show that the average classification accuracy of our FBRTS method is 77.7% and 86.9% in datasets 2a and 2b respectively. By analyzing the influence of the different numbers of filter banks and time windows on the performance of our FBRTS method, we can identify the optimal number of filter banks and time windows. Additionally, our FBRTS method can obtain more distinctive features than the filter banks common spatial pattern (FBCSP) method in two-dimensional embedding space. These results show that our proposed method can improve the performance of MI-BCIs.

Novel Hybrid Brain-Computer Interface for Virtual Reality Applications Using Steady-State Visual-Evoked Potential-Based Brain–Computer Interface and Electrooculogram-Based Eye Tracking for Increased Information Transfer Rate

Abstract: Brain–computer interfaces (BCIs) based on electroencephalogram (EEG) have recently attracted increasing attention in virtual reality (VR) applications as a promising tool for controlling virtual objects or generating commands in a “hands-free” manner. Video-oculography (VOG) has been frequently used as a tool to improve BCI performance by identifying the gaze location on the screen, however, current VOG devices are generally too expensive to be embedded in practical low-cost VR head-mounted display (HMD) systems. In this study, we proposed a novel calibration-free hybrid BCI system combining steady-state visual-evoked potential (SSVEP)-based BCI and electrooculogram (EOG)-based eye tracking to increase the information transfer rate (ITR) of a nine-target SSVEP-based BCI in VR environment. Experiments were repeated on three different frequency configurations of pattern-reversal checkerboard stimuli arranged in a 3 × 3 matrix. When a user was staring at one of the nine visual stimuli, the column containing the target stimulus was first identified based on the user’s horizontal eye movement direction (left, middle, or right) classified using horizontal EOG recorded from a pair of electrodes that can be readily incorporated with any existing VR-HMD systems. Note that the EOG can be recorded using the same amplifier for recording SSVEP, unlike the VOG system. Then, the target visual stimulus was identified among the three visual stimuli vertically arranged in the selected column using the extension of multivariate synchronization index (EMSI) algorithm, one of the widely used SSVEP detection algorithms. In our experiments with 20 participants wearing a commercial VR-HMD system, it was shown that both the accuracy and ITR of the proposed hybrid BCI were significantly increased compared to those of the traditional SSVEP-based BCI in VR environment.