G. Reshmi

Bio: G. Reshmi is an academic researcher from Anna University. The author has contributed to research in topics: Brain–computer interface & ST segment. The author has an hindex of 2, co-authored 2 publications receiving 22 citations.

Design of a BCI System for Piloting a Wheelchair Using Five Class MI Based EEG

Abstract: Motor imagery is the recreation of a motor activity which can be used to design Brain Computer Interfaces (BCI). A BCI bypasses the neuromuscular system and provides a communication link, which directly connects the human brain to an external device. Each individual is able to control his EEG through imaginary motor act it supports to control devices. In this work for designing a BCI system five class motor imagery EEG is used. EEG recorded from the sensory motor cortex is analyzed using wavelet transform. Features extracted from the wavelet coefficients are classified using Support Vector Machine. In this work the motor imagery EEG signal is translated into control signal using a five class BCI to control the directional movement of a wheelchair.

Cardiac Ischemia Diagnosis Using Stress ECG Analysis

Abstract: Myocardial Ischemia is the most common heart disease. Stress ECG has been effectively used for analysis of the myocardial ischemia than normal ECG because of the reason that ischemic conditions will be dominated in stress conditions. According to the clinically proven facts, after taking a series of exercise, the chance of finding ischemia can rise up to 80%-90%. In ECG, the ST segment detection has close relationship with myocardial ischemia and myocardial infarction. Denoising of the stress ECG has done using filters. The key points of ECG signal like Q, R, S are found out using Pan Tompkins algorithm. Other key points like P, T, Ton, Toff, J, Iso-electric point are also found using window method. The feature of interest is ST segment. Based on R-R interval, heart rate was found out. By considering the age of the individual sub-maximal heart rate is fixed and let the patient to do exercise stress test which lasts until sub-maximal heart rate was reached. The ST segment analysis had been done in time domain as well as in frequency domain. ST trend was analyzed on and after the sub-maximal heart rate. According to the clinically proven facts, for a person having ischemia the ST level shows a depression for about two minutes or more during relaxing stage. The signals from MIT-BIH ST Change Database had been used to verify the algorithm in MATLAB software.

A Prototype SSVEP Based Real Time BCI Gaming System

Abstract: Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.

A Review on Electroencephalogram Based Brain Computer Interface for Elderly Disabled

Abstract: Lack of communication causes problems for patients with neurodegenerative diseases, so the need for alternative methods is required to convey their thoughts with caretakers, friends, and family members. Brain–computer interface (BCI) is a device to control external devices by using mental thoughts without any other muscle movements to improve the communication quality for the disabled individual without any other help. The techniques of measuring electrical signal around the scalp during some activities by using electrodes are called electroencephalogram (EEG). By combining these two technologies together to form a brain–computer interaction, this helps the paralyzed individual to communicate with others to share the thoughts. In this paper, we deal with the history of EEG, electrode placements with measurements, and signal ranges, and also further discussed some of the prominent studies completed in designing BCI using EEG. This helps the new researchers to know the EEG measurement and position completely and paved the new way to create EEG-based interface research.

Brain Computer Interface for Neurodegenerative Person Using Electroencephalogram

Abstract: Brain–computer interface (BCI) connects the outside world, in real time and in a natural way, like biological communication system. It facilitates the communication link from the brain to the external world by converting brain thoughts in to control commands to control the external devices, such as wheelchair, keyboard mouse, and other home appliances. Measuring the electrical brain activity by placing electrodes over scalp is called electroencephalogram (EEG). By combining these two techniques, we are able to create EEG-based BCI. In this paper, we use band power and radial basis function to analyze the signal for four mentally composed tasks to design four states BCI for a neurodegenerative person using EEG. Online study was conducted to analyze the performance of the wheelchair for a neurodegenerative person. The result shows that an overall average classification accuracy of 92.50% and individual tasks with an average classification of 95%, 87.50%, 92.50%, and 95.00% were achieved for the four tasks. The result proves that control commands generated from the EEG signal have the bcapacity to control the intelligent systems.

Development of an Autonomous BCI Wheelchair

Abstract: Restoration of mobility for the movement impaired is one of the important goals for numerous Brain Computer Interface (BCI) systems. In this study, subjects used a steady state visual evoked potential (SSVEP) based BCI to select a desired destination. The selected destination was communicated to the wheelchair navigation system that controlled the wheelchair autonomously avoiding obstacles on the way to the destination. By transferring the responsibility of controlling the wheel chair from the subject to the navigation software, the number of BCI decisions needed to be completed by the subject to move to the desired destination is greatly reduced.