BRAIN MACHINE INTERFACE (original) (raw)
Related papers
2015
A brain-machine interface (BMI), sometimes called a mind-machine interface (MMI), or sometimes called a direct neural interface (DNI), synthetic telepathy interface (STI) or a brain-machine interface (BMI), is a direct communication pathway between the brain and an external device. BCIs are often directed at assisting, augmenting, or repairing human cognitive or sensory-motor functions. The field of BCI research and development has since focused primarily on neuroprosthetics applications that aim at restoring damaged hearing, sight and movement. Thanks to the remarkable cortic al plasticity y of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels.
Implementation of Brain Machine Interface using Mind wave Sensor
Procedia Computer Science, 2020
The most interesting thing in this world of science is to monitor human brain activity and its great potential in helping us to understand and function accordingly, which make human different from other species. Electric discharge is created when thousands of neurons interact with each other and radiate signals. Activity created by these thousands of concurrent electric discharges aggregates into waves having certain frequencies and amplitude variations, by monitoring and measuring these variations and frequencies one can predict the state of mind of an individual. This is when BMI came into picture which allow us to Establish interface between a machine and human being by capturing the particular range of frequencies from human brain generated by discharging of neurons. And study of these waves and electrical signals leads to neuroscience and neurotechnology. In this paper, we have presented the analysis of Electroencephalography and brain waves. We also highlight the interfacing of Brain and Machine using brain wave sensor.
IRJET-A Survey: Usage of Brain- Machine Interface in Various Applications
A brain and computer interface (BCI), also called a Mind and machine interface (MMI) is a direct communication pathway between the external device and the human brain. BMI is an association between a brain and a device that authorizes brain signals to direct some external action, such as controlling of a wheelchair. For instance in the case of cursor control, the signal is imparted directly from the brain to the process directing the cursor instead of taking the normal path through the neuromuscular system of a person to the finger on a mouse from the brain. Here we have taken measures to find the people who have worked on different applications on BCI to be accommodated in a single paper.
A control method with brain machine interface for man-machine systems
In this paper, the control aspects of man-machine systems with BMI (Brain machine interface), for example, car cruising systems and so on, are discussed. In norml circumstances, the system is controlled automatically and the BMI is not worked. The BMI signals are used for the emergency situation. It works as a trigger for switching of control laws. On the assumption of combining with the EEG (Electroencephalogram) based BMI, new Receding Horizon Control (RHC) approach with the adaptive DA converter is proposed. Some numerical examples are included to demonstrate the effectiveness of the proposed method.
Oxford Scholarship Online, 2018
A brain–machine interface (BMI) is a biohybrid system intended as an alternative communication channel for people suffering from severe motor impairments. A BMI can involve either invasively implanted electrodes or non-invasive imaging systems. The focus in this chapter is on non-invasive approaches; EEG-based BMI is the most widely investigated. Event-related de-synchronization/ synchronization (ERD/ERS) of sensorimotor rhythms (SMRs), P300, and steady-state visual evoked potential (SSVEP) are the three main cortical activation patterns used for designing an EEG-based BMI. A BMI involves multiple stages: brain data acquisition, pre-processing, feature extraction, and feature classification, along with a device to communicate or control with or without neurofeedback. Despite extensive research worldwide, there are still several challenges to be overcome in making BMI practical for daily use. One such is to account for non-stationary brainwaves dynamics. Also, some people may initial...
Review of the State-of-the-art on Bio-signal-based Brain-controlled Vehicles
arXiv: Signal Processing, 2020
Brain-controlled vehicle (BCV) is an already established technology usually designed for disabled patients. This review focuses on the most relevant topics on the brain controlling vehicles, especially considering terrestrial BCV (e.g., mobile car, car simulators, real car, graphical and gaming cars) and aerial BCV, also named BCAV (e.g., real quadcopter, drone, fixed wings, graphical helicopter and aircraft) controlled using bio-signals such as electroencephalogram (EEG), electrooculogram and electromyogram. For instance, EEG-based algorithms detect patterns from motor imaginary cortex area of the brain for intention detection, patterns like event related desynchronization/event related synchronization, state visually evoked potentials, P300, and generated local evoked potential patterns. We have identified that the reported best performing approaches employ machine learning and artificial intelligence optimization methods, namely support vector machine, neural network, linear disc...
Proposal of a Brain Computer Interface to command an autonomous car
5th ISSNIP-IEEE Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC), 2014
This paper presents a proposal of Brain Computer Interface (BCI) to command an autonomous car. This BCI is based on the paradigm of visual evoked potentials (VEP) and event-related desynchronization (ERD). A menu interface is presented to the user with disabilities in order he/she can choose a destination for the autonomous car. The selection of the final destination is performed using visual stimuli flickering at different frequencies, analyzed through the analysis of brain signals present at the occipital region of the user's scalp. The power spectrum of these signals is then obtained, in order to get the frequency of the visual stimulation. Preliminary tests were performed with healthy users and people with disabilities, which reached an average success rate above 90%. The proposed system is also capable of turning on/off the stimuli, thus reducing the fatigue associated with particular visual stimuli.
Brain Machine Interface—IEETA case study
The goal of the present paper is to report the recent advances in Electroencephalogram (EEG)-based Brain Machine Interface (BMI) developed at the Institute of Electrical Engineering and Telematics of Aveiro (IEETA). First, a short overview of the most successful BMI technologies is presented and then our ongoing research and protocol for motor imagery noninvasive BMI for a mobile robot control is discussed. The main EEG signal processing challenges as filtering, feature extraction and classification are also considered.
Brain–Machine Interface Engineering
Synthesis Lectures on Biomedical Engineering, 2007
What has the past decade taught us about mankind's ability to interface with and read information from the brain? Looking back on our experiences, the salient recollection is how ill-prepared the present theories of microelectronic circuit design and signal processing are for building interfaces and interpreting brain's activity. Although there is plenty of room for future improvement, the combination of critical evaluation of current approaches and a vision of nueroengineering are helping us develop an understanding on how to read the intent of motion in brains. The flow of ideas and discovery conveyed in this book is quite chronological, starting back in 2001 with a multi-university research project lead by Dr. Miguel Nicolelis of Duke University to develop the next-generation BMIs. The series of engineering developments explained in this book were made possible by the collaboration with Miguel, his contagious enthusiasm, vision, and brilliant experimentalism, that have led us in a journey of discovery in new theories for interfacing with the brain. Part of the results presented here also utilize data collected in his laboratory at Duke University. It was also a journey of innovation shared with colleagues in ECE. Dr. John Harris was instrumental in designing the chips and proposing new devices and principles to improve the performance of current devices. Dr. Karl Gugel helped develop the DSP hardware and firmware to create the new generation of portable systems. We were fortunate to count with the intelligence, dedication, and hard work of many students. Dr. Justin Sanchez came on board to link his biomedical knowledge with signal processing, and his stay at University of Florida has expanded our ability to conduct research here. Dr. Sung-Phil Kim painstakingly developed and evaluated the BMI algorithms. Drs. Deniz Erdogmus and Yadu Rao helped with the theory and their insights. Scott Morrison, Shalom Darmanjian, and Greg Cieslewski developed and programmed the first portable systems for online learning of neural data. Later on, our colleagues Dr. Toshi Nishida and Dr. Rizwan Bashirullah open up the scope of the work with electrodes and wireless systems. Now, a second generation of students is leading the push forward; Yiwen Wang, Aysegul Gunduz, Jack DiGiovanna, Antonio Paiva, and Il Park are advancing the scope of the work with spike train Foreword v modeling. This current research taking us to yet another unexplored direction, which is perhaps the best indication of the strong foundations of the early collaboration with Duke. This book is only possible because of the collective effort of all these individuals. To acknowledge appropriately their contributions, each chapter will name the most important players. Jose C. Principe and Justin C. Sanchez vi BRaIN-MaChINE INTERFaCE ENgINEERINg 1 The study of repair and regeneration of the central nervous system is quite broad and includes contributions from molecular/cellular neuroscience, tissue engineering, and materials science. For a comprehensive review of the application of each of these to the repair of the nervous system, see References [1-4].