82
ABSTRACT
Abstract:
The objective is to control the direction of electric devices using EEG signals. In other words, this is an attempt to use brain signals to control mechanical devices such as wheelchairs / robot
1.SYNOPSIS
A Brain-Computer Interface (BCI) application focused on the control of electronic devices must consider the danger which a wrong command would involve in a real situation. Effective reality is a suitable tool to provide subjects with the opportunity to train and test the application before using it under real conditions. Recent studies aimed at such control let the subject decide the timing of the interaction; those are the so-called asynchronous BCI.
In brain–computer interface (BCI) systems is the low bandwidth of the communication channel, especially while communicating and controlling assistive devices, such as a smart wheelchair or a tele presence mobile robot, which requires multiple motion command options in the form of forward, left, right, backward, and start/stop.
To address this, an adaptive user-centric graphical user interface referred to as the intelligent adaptive user interface (iAUI) based on an adaptive shared control mechanism is proposed. The iAUI offers multiple degrees-of-freedom control of a robotic device by providing a continuously updated prioritized list of all the options for selection to the BCI user, thereby improving the information transfer rate. Results have been verified with
multiple participants controlling a simulated as well as physical establish robot.
2.EXISTING SYSTEM
A Brain Computer Interface (BCI) is any system which can derive meaningful information directly from the user's brain activity in real time. The most important applications of the technology are mainly meant for the paralyzed people who are suffering from severe neuromuscular disorders. Most BCIs use information obtained from the user's encephalogram (EEG), though BCIs based on other brain imaging methods are possible.
The information transfer rate, given in bits per trial, is used as an evaluation measurement in a brain–computer interface (BCI). Three subjects performed four motor-imagery (left hand, right hand, foot, and tongue) and one mental-calculation task. Classification of the electroencephalogram (EEG) patterns is based on band power estimates and hidden Markov models (HMMs). The proposed method that combines the EEG patterns based on reparability into subsets of two, three, four, and five mental tasks.
Based on the information transfer, a method is proposed to determine the types of mental tasks and their combination in order to achieve the maximum information transfer rate.
AN OPTIMIZED METHOD FOR EEG-BASED MOBILE ROBOT CONTROL
WITH ADAPTIVE BRAIN–ROBOT INTERFACE
Dr.C.Kumar Charliepaul Principal
83 LIMITATIONS / DISADVANTAGES
A common feature between all BCIs is that, since the recorded brain signal is very noisy and has a large variability, either the uncertainty on the command will be high,or the time between consecutive commands will be long, in the order of seconds.
Brain Computer Interface is typically considered by a very low information transfer rate.
Brain–computer interface (BCI) systems is the low bandwidth of the communication channel, especially while communicating and controlling assistive devices, such as a smart wheelchair or a telepresence mobile robot, which requires multiple motion command
3. INFORMATION TRANSFER RATE IN FIVE-CLASSES
BRAIN–COMPUTER INTERFACE
Bernhard Obermaier, Christa Neuper, Christoph Guger, Associate Member, IEEE, and
Gert Pfurtscheller, Member, IEEE
The information transfer rate, given in bits per trial, is used as an evaluation measurement in a brain–computer interface (BCI). Three subjects performed four motor-imagery (left hand, right hand, foot, and tongue) and one mental-calculation task. Classification of the electroencephalogram (EEG) patterns
is based on band power estimates and hidden Markov models (HMMs). We propose a method that combines the EEG patterns based on separability into subsets of two, three, four, and five mental tasks. The information transfer rates of the BCI systems comprised of these subsets are reported. The achieved information transfer rates vary from 0.42 to 0.81 bits per trial and reveal that the upper limit of different mental tasks for a BCI system is three. In each
subject, different combinations of three tasks resulted in the best performance.
3.1.A P300-based EEG-BCI for Spatial Navigation Control
Adrian Curtin, Hasan Ayaz, Yichuan Liu, Patricia A. Shewokis, Banu Onaral
A Brain Computer Interface (BCI) Based on the P300 oddball paradigm has been developed for Spatial navigation control in virtual environments. Functionality and efficacy of the system were analyzed with Results from nine healthy volunteers. Each participant was Asked to gaze at an individual target in a 3x3 P300 matrix Containing different symbolic navigational icons while EEG
Signals were collected. Resulting erps were processed online And classification commands were executed to control spatial Movements within the mazesuite virtual environment and Presented to the user online during an experiment. Subjects Demonstrated on average, ~89% online accuracy for simple Mazes and ~82% online accuracy in longer more complex Mazes. Results suggest that this BCI setup enables guided freeform Navigation in virtual 3D environments.
3.2.Brain–Computer Interfaces: A Gentle Introduction
Any natural form of communication or control requires peripheral nerves and muscles. The process begins with the user’s intent. This intent triggers a complex process in which certain brain areas are activated, and hence
signals are sent via Brain–Computer
Interfaces: A Gentle Introduction 3 the peripheral nervous system (specifically, the
motor pathways) to the corresponding
84 necessary for the communication or control task. The activity resulting from this process is often called motor output or efferent output. Efferent means conveying impulses from the central to the peripheral nervous system and further to an effector (muscle). Afferent, in contrast, describes communication in the other direction, from the sensory receptors to the central nervous system. For motion control, the motor (efferent) pathway is essential. The sensory (afferent) pathway is particularly important for learning motor skills and dexterous tasks, such as typing or playing a musical instrument. A BCI offers an alternative to natural communication and control. A BCI is an artificial system that bypasses the body’s normal efferent pathways, which are the neuromuscular output channels illustrates this functionality. Instead of depending on peripheral nerves and muscles, a
BCI directly measures brain activity
associated with the user’s intent and translates the recorded brain activity into corresponding control signals for BCI applications. This translation involves signal processing and pattern recognition, which is typically done by a computer. Since the measured activity originates directly from the brain and not from the peripheral systems or muscles, the system is called a Brain–Computer Interface.
A BCI must have four components. It must record activity directly from the brain (invasively or non-invasively). It must provide feedback to the user, and must do so in realtime. Finally, the system must rely on intentional control. That is, the user must choose to perform a mental task whenever s/he wants to accomplish a goal with the BCI. Devices that only passively detect changes in brain activity that occur without any intent, such as EEG activity associated with workload, arousal, or sleep, are not BCIs. Although most researchers accept the term
“BCI” and its definition, other terms has been used to describe this special form of human– machine interface. Here are some definitions of BCIs found in BCI literature:
4. PROPOSED SYSTEM
A Brain-Computer Interface (BCI) is a system that enables a communication that is not based on muscular movements but during brain activity. This activity can be measured through electroencephalographic (EEG) signals. Several EEG signals canbe detected, resulting in different types of BCI.
Simple two-class BCI system, there are normally only two output commands e.g., a left hand motor imagery (MI) or a right hand/foot MI, for every trial rendering control of assistive devices such as a smart wheelchair or a telepresence mobile robot, which requiresmultiple motion commands, a significant challenge.
MI is used in this paper to control the proposed interface using the synchronous mode of BCI operation (cue based and computer driven). t involves selecting the movement tasks, left, right,
forward, backward, halt, or transferring control to another GUI via main using just the two-class MI, i.e., left hand or right hand MI. The class information (left hand or right hand
movement imagery) from the features of the filtered EEG signal is sent to the iAUI. The iAUI is composed of four main modules namely the communication module (CM), the information refresh module (IRM), the adaptation module(AM) and the MM
4.1. ADVANTAGES
85
The iAUI is the user-centric design that presents all the control options to the BCI user at all times.
Better control as shown with the BCI accuracy assumption can be achieved with more training on the paradigm.
The proposed interface designs have the potential to provide true independence to the BCI user with a combination of autonomous and adaptive designs while not compromising much on the overall cost for the device control task.
Scope:A major challenge in two-class brain– computer interface (BCI) systems is the low bandwidth of the communication channel, especially while communicating and controlling assistive devices, such as a smart wheelchair or a telepresence mobile robot, which requires multiple motion command options in the form of forward, left, right, backward, and start/stop.
Project Perspective:This paper proposes to devise a consistently extendable GUI to use a two-class MI BCI to perform a multitask robotic control problem.
Constraints :Microcontroller Kit,Modern Browser.
Existing Algorithm:
EEG:electroencephalogram
Proposed Algorithm:
EEG:electroencephalogram
Modules:
1) Measurement of EEG 2) Preprocessing
3) Feature extraction 4) Classification 5) Device control
1)Measurement of EEG
This is done by using the electrodes. Many BCIs use a special
electrode cap, in which the electrodes are
already in the right places, typically according to the international 10-20 system. It saves time because the electrodes do not have to be attached one by one. Typically, less than 10 electrodes are used in online BCIs with sampling rates of 100-400 Hz.
2) Preprocessing
This includes amplification, initial filtering of EEG signal and possible
artifact removal. Also A/D conversion is made, i.e. the analog EEG signal is digitized.
3) Feature extraction
In this stage, certain features are extracted from the preprocessed
and digitized EEG signal. In the simplest form a certain frequency range is selected and the amplitude relative to some reference level measured .Typically the features are
certain frequency bands of a power spectrum. The power spectrum can be calculated using, for example, Fast Fourier Transform (FFT), the transfer function of an autoregressive (AR) model or wavelet transform No matter what features are used, the goal is to form distinct set of features
for each mental task. If the feature sets representing mental tasks overlap each other too
much, it is very difficult to classify mental tasks, no matter how good a classifier is used. On the other hand, if the feature sets are distinct enough, any classifier can classify them.
4) Classification
86 Different BCIs can classify different number of classes, typically 2 to 5 classes. The classifier can be anything from a simple linear model to a complex nonlinear neural network that can be trained to recognize different mental tasks. With the exception of a simple threshold detection the classifier can calculate the probabilities for the input belonging to each class. Usually the class with the highest probability is chosen. However, in some BCI protocols none of the classes may be chosen, if the classification probability does not exceed some predefined level. This kind of classification result can be called “nothing” or “reject”.
5) Device control
The classifier’s output is the input for the device control. The device control simply transforms the classification to a particular action. The action can be, e.g.,an up or down movement of a cursor on the feedback screen or a selection of a letter in awriting application. However, if the classification was “nothing” or “reject”, no action isperformed, although the user may be informed about the rejection.
5.CONCLUSION:
Real-time implementation of a novel iaui design for a mobile robot control task. The major advantage with the iaui is the user-centric design that presents all the control options to the BCI user at all times. The complete BCI system, including the RQNN technique (for EEG filtering) and the user-centric iAUI (for enhancing the bandwidth) were implemented for the robot control task in the physical environment. Most of the subjects reached the targets on the first or second attempt and were easily acquainted with the adaptive interface as the sessions progressed. However, better control as shown with the 100% BCI accuracy assumption can be achieved with more training on the paradigm. The proposed interface designs have the potential to provide true independence to the BCI user
with a combination of autonomous and adaptive designs while not
compromising much on the overall cost for the device control task. The simple multicircle design of the presented GUI can consistently and seamlessly be used for control through hybrid BCIs involving multimodalities, such as eye-tracker and ERP-based BCIs, and will be explored further in future
6.FUTURE SCOPE:
The simple multicircle design of the presented GUI can consistently and seamlessly be used for control through hybrid BCIs involving multimodalities, such as eye-tracker and ERP-based BCIs, and will be explored further in future.
