Rajesh Singla

EOG and EMG based virtual keyboard: A brain-computer interface

This paper discusses a brain-computer interface through electrooculogram (EOG) and electromyogram (EMG) signals. In situations of disease or trauma, there may be inability to communicate with others through means such as speech or typing. Eye movement tends to be one of the last remaining active muscle capabilities for people with neurodegenerative disorders, such as amyotrophic lateral sclerosis (ALS) also known as Lou Gehrigs disease. Thus, there is a need for eye movement based systems to enable communication. To meet this need, we proposed a system to accept eye-gaze controlled navigation of a particular letter and EMG based click to enter the letter. Eye -gaze direction (angle) is obtained from EOG signals and EMG signal is recorded from eyebrow muscle activity. A virtual screen keyboard may be used to examine the usability of the proposed system.

Virtual keyboard BCI using Eye blinks in EEG

A Brain Computer Interface (BCI) provides a new communication channel between human brain and the computer. This paper is concentrated on developing a BCI system, a Virtual Keyboard using the LabVIEW platform. The Electroencephalogram (EEG) signal contains the technical artifacts (noise from the electric power source, amplitude artifact, etc.) and biological artifacts (eye artifacts, ECG and EMG artifacts). Eye blink is one of the main artifacts in the EEG signal. But in this context the Eye blinks are not artifacts and are control signals to select the blocks/characters in the Virtual Keyboard. The kurtosis coefficient and amplitude characteristics of the eye blink signals are used to detect the control signals.

Influence of stimuli colour in SSVEP-based BCI wheelchair control using support vector machines.

Abstract This study aims to develop a Steady State Visual Evoked Potential (SSVEP)-based Brain Computer Interface (BCI) system to control a wheelchair, with improving accuracy as the major goal. The developed wheelchair can move in forward, backward, left, right and stop positions. Four different flickering frequencies in the low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using LabVIEW. Four colours (green, red, blue and violet) were included in the study to investigate the colour influence in SSVEPs. The Electroencephalogram (EEG) signals recorded from the occipital region were first segmented into 1 s windows and features were extracted by using Fast Fourier Transform (FFT). Three different classifiers, two based on Artificial Neural Network (ANN) and one based on Support Vector Machine (SVM), were compared to yield better accuracy. Twenty subjects participated in the experiment and the accuracy was calculated by considering the number of correct detections produced while performing a pre-defined movement sequence. SSVEP with violet colour showed higher performance than green and red. The One-Against-All (OAA) based multi-class SVM classifier showed better accuracy than the ANN classifiers. The classification accuracy over 20 subjects varies between 75–100%, while information transfer rates (ITR) varies from 12.13–27 bpm for BCI wheelchair control with SSVEPs elicited by violet colour stimuli and classified using OAA-SVM.

Comparison of SSVEP Signal Classification Techniques Using SVM and ANN Models for BCI Applications

In recent years, Brain Computer Interface (BCI) systems based on Steady-State Visual Evoked Potential (SSVEP) have received much attentions. This study tries to develop a classifier, which can provide higher classification accuracy for multiclass SSVEP data. Four different flickering frequencies in low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using LabVIEW. The Electroencephalogram (EEG) signals recorded from the occipital region were first segmented into 1 second window and features were extracted using Fast Fourier Transform (FFT). One-Against-All (OAA), a popular strategy for multiclass Support Vector Machines (SVM) is compared with Artificial Neural Network (ANN) models on the basis of SSVEP classifier accuracies. OAA SVM classifier had got an average accuracy of 88.55% for SSVEP classification over 10 subjects. Based on this study, it is found that for SSVEP classification OAA -SVM classifier can provide better results

Application of Fingerprint Recognition in Process Control

HMI (Human Machine Interface) is where human and technology meets. The system may expose several user interfaces to serve different kinds of users. In computer science and human-computer interaction, the user interface (of a computer program) refers to the graphical, textual and auditory information the program presents to the user, and the control sequences the user employs to control the program. The aim of this paper is to present a HMI which can be widely in industries or can be used for commercial purposes. We have taken Biometrics i.e. fingerprint for this purpose which is the most reliable security tool for security of the system Fingerprint recognition has long been favored among many biometric identification technologies due to its uniqueness and permanence. Nowadays, fingerprint recognition is considered to be the best choice for most applications from network security systems to compact devices, due to its accuracy, speed, reliability, non- intrusive interfaces, and cost-effectiveness. We have made a fingerprint matching system using MATLAB. After collecting the

The Effect of Mental States on Blood Pressure and Electrocardiogram

This paper describes the effect of mental states on blood pressure and electrocardiogram (ECG). Blood pressure and ECG were recorded before and during four types of mental task. The mental task involved the four tasks that encompassed Motor Action (MA), Thoughts (TH), Memory Related (MR) and Emotions (EM). Blood pressure and ECG were recorded using BIOPAC system. These variables were analyzed using BIOPAC Acknowledge software. In MA and TH tasks, significant changes were observed in blood pressure, although during MR and EM tasks there were no significant changes in it. The present study demonstrated the changes in ECG during MR and EM tasks. But there were no significant changes in ECG during MA and TH tasks.

BCI: an optimised speller using SSVEP

The proposed work is done in order to develop an optimised Brain-Computer Interface (BCI) system (speller) for people with severe motor impairments using SSVEP (Steady-State Visual Evoked Potentials). To make the system fast yet error-free, the optimisation of speller is divided into three domains: one is the design of smart encoding method for the selection of appeared characters on interface, second one is the optimal frequency choice and the last one is design of optimal feature classification algorithm. Three classification methods: threshold method, Artificial Neural Network (ANN) and Support Vector Machine (SVM) are evaluated. An optimal user window is also carefully selected after many trails in order to maintain a decent communication rate. The optimised BCI system provides an average accuracy of 96% with character per minute (CPM) of 13 ± 2. Speller performs almost similar with new users too because inter-subject variability is tackle by SVM classifier.

Moving an object using EEG: an art of BCI

Brain-Computer Interface (BCI) provides a communication channel that allows individuals with neuromuscular disabilities to communicate with the environment. In this paper a simple model of an object movement is developed. The movement of the object is controlled by using the P300 signals generated in response to visual stimuli. The effectiveness of this model is examined online.

Brain Actuated Interaction: An Application of BCI

Brain-Computer Interfaces (BCI) is a system that allows individual with severe neuromuscular disabilities to communicate or perform ordinary tasks exclusively via brain waves. BCI shows promise in allowing these individuals to interact with a computer using EEG. Brain Computer Interface mainly facilitates or formed a communication channel between human brain and machine or device application. A simple model of bed movement i.e. bed upward and downward movement through eye blinks signal of the patients is proposed and its effectiveness is examined online and offline

The types of hybrid modalities in brain-computer interface systems: a review

Brain-Computer Interface (BCI) is a young research area for researchers. Increasing number of research activities improves several areas such as signal acquisition techniques, hardware development, machine learning, and signal processing and system integration. However, there are many disadvantages of conventional BCI approaches. For example, Motor-imagery based BCIs requires extensive training of the subjects, P-300 based BCIs still requires several stimulus repetitions to obtain reliable accuracy and in SSVEP stimulus; number of commands is limited by the number of stimulus frequencies and many more. To overcome these disadvantages and further improvement in performance of the system, an increasing number of researchers have begun to explore hybrid BCI approaches, in which multiple BCI approaches are incorporated in a single BCI system. The purpose of this paper is to give a brief introduction to the different types of hybrid BCI techniques. There are many different types of hybrid BCI that can be used in a wide range of applications. The paradigm design plays a very important role in the performance of hybrid BCIs.