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CHAPTER 1

INTRODUCTION

What is BCI?

Brain-computer interface (BCI) is a fast-growing emergent technology, in which

researchers aim to build a direct channel between the human brain and the computer.• A

Brain Computer Interface (BCI) is a collaboration in which a brain accepts and controls a

mechanical device as a natural part of its representation of the body. It is also sometimes

referred as BMI (Brain Machine interface). Computer-brain interfaces are designed to

restore sensory function, transmit sensory information to the brain, or stimulate the brain

through artificially generated electrical signals.

A brain–computer interface (BCI), often called a mind-machine interface (MMI), or

sometimes called a direct neural interface 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.

Research on BCIs began in the 1970s at the University of California Los Angeles (UCLA)

under a grant from the National Science Foundation, followed by a contract from DARPA.

The papers published after this research also mark the first appearance of the expression

brain–computer interface in scientific literature.

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 cortical plasticity of the brain, signals from implanted prostheses can, after

adaptation, be handled by the brain like natural sensor or effector channels. Following years

of animal experimentation, the first neuroprosthetic devices implanted in humans appeared

in the mid-1990s.

The history of brain–computer interfaces (BCIs) starts with Hans Berger's discovery

of the electrical activity of the human brain and the development of electroencephalography

(EEG). In 1924 Berger was the first to record human brain activity by means of EEG. By

analysing EEG traces, Berger was able to identify oscillatory activity in the brain, such as

the alpha wave (8–12 Hz), also known as Berger's wave.

Berger's first recording device was very rudimentary. He inserted silver wires under the

scalps of his patients. These were later replaced by silver foils attached to the patients' head

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by rubber bandages. Berger connected these sensors to a Lippmann capillary electrometer,

with disappointing results. More sophisticated measuring devices, such as the Siemens

double-coil recording galvanometer, which displayed electric voltages as small as one ten

thousandth of a volt, led to success.

Berger analyzed the interrelation of alternations in his EEG wave diagrams with

brain diseases. EEGs permitted completely new possibilities for the research of human brain

activities.

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CHAPTER 2

LITERATURE SURVEY

In 1848, Duboi-Reymond reported the presence of electrical signals in the human

brain. Research on this led to Caton’s discovery in 1875 that “feeble” currents can be

measured on the scalp. In 1924, Mr Hans Berger discovered the EEG, a device that

theoretically measured electrical signals. However, this wasn’t proven until 1929 by Berger.

He analysed the interrelation of EEG and brain diseases

1. 1930-50s EEG used in psychiatric and neurological sciences relying on visual inspection of

EEG patterns

2. 1960s-70s witness emergence of Quantitative EEG and confirmation of hemispheric

specialization, e.g., left brain verbal and right brain spatial.

3. 1980s+ observation of biofeedback

4. 1970: First developments to use brain waves as input

5. ARPA has vision of enhanced human

6. First step in the right direction

7. 1990: First successful experiments with monkeys

8. Implanting electrode arrays into monkey brains

9. Recording of monkeys‘ brain waves

10. 2000: Monkeys control robots by thoughts

11. More non-invasive than invasive approaches

12. Brain reading by eg. EEG, MEG or MRI

13. 2004: First human benefits from research

 

In May 2008 photographs that showed a monkey operating a robotic arm with its mind at

the Pittsburgh University Medical Centre were published in a number of well known science

journals and magazines.

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Figure 2.1 Mind Control - Child of the 70's

The brain-computer interface (BCI), (a.k.a. neural interface or brain-machine

interface), is a direct communication link between a brain and an external electronic device.

BCIs are most commonly intended to assist, augment or fully repair cognitive or sensory-

motor functionality often sustained after injury or from birth defect.

Research on Neural Interface Technology started in the 70s at UCLA and was first

funded by a National Science Foundation (NSF) grant and after showing some promise,

garnered a contract from DARPA (the real inventors of the net). It was during this research

that the phrase "brain-computer interface" first made its appearance in published scientific

literature.

BCI technology has developed exponentially since the early days, with the most growth in

the area of neuroprosthetics. After many years of laboratory research on animals, the first

human neuroprosthetic implants were done in the mid 90's.Early BCI research was

encouraging because it showed that various animals, from Mice to Monkeys, could perform

simple tasks using only Mind Control, such as moving a cursor on a monitor, or controlling

a robotic arm. Further research conducted at Johns Hopkins University discovered a key

mathematical correlation between the electrical responses of individual motor-cortex

neurons in monkeys and the direction that they moved their arms.

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Figure. 2.2: Schematic diagram of a BCI system

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CHAPTER 3

BCI TYPES AND COMPONENTS

3.1 TYPES OF BCI

Invasive BCIs

Jens Naumann, a man with acquired blindness, being interviewed about his vision

BCI on CBS's The Early Show

Invasive BCI research has targeted repairing damaged sight and providing new functionality

for people with paralysis. Invasive BCIs are implanted directly into the grey matter of the

brain during neurosurgery. Because they lie in the grey matter, invasive devices produce the

highest quality signals of BCI devices but are prone to scar-tissue build-up, causing the

signal to become weaker, or even non-existent, as the body reacts to a foreign object in the

brain.

Figure 3.1 Jens

In vision science, direct brain implants have been used to treat non-

congenital (acquired) blindness. One of the first scientists to produce a working brain

interface to restore sight was private researcher William Dobelle.

Dobelle's first prototype was implanted into "Jerry", a man blinded in adulthood, in 1978. A

single-array BCI containing 68 electrodes was implanted onto Jerry’s visual cortex and

succeeded in producing phosphenes, the sensation of seeing light. The system included

cameras mounted on glasses to send signals to the implant. Initially, the implant allowed

Jerry to see shades of grey in a limited field of vision at a low frame-rate. This also required

him to be hooked up to a mainframe computer, but shrinking electronics and faster

computers made his artificial eye more portable and now enable him to perform simple tasks

unassisted.

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Figure 3.2 Design of a Brain Gate

Dummy unit illustrating the design of a Brain Gate interface In 2002, Jens Naumann, also

blinded in adulthood, became the first in a series of 16 paying patients to receive Dobelle’s

second generation implant, marking one of the earliest commercial uses of BCIs. The

second generation device used a more sophisticated implant enabling better mapping of

phosphenes into coherent vision. Phosphenes are spread out across the visual field in what

researchers call "the starry-night effect". Immediately after his implant, Jens was able to use

his imperfectly restored vision to drive an automobile slowly around the parking area of the

research institute.

Movement

BCIs focusing on motor neuroprosthetics aim to either restore movement in individuals with

paralysis or provide devices to assist them, such as interfaces with computers or robot arms.

Researchers at Emory University in Atlanta, led by Philip Kennedy and Roy Bakay, were

first to install a brain implant in a human that produced signals of high enough quality to

simulate movement. Their patient, Johnny Ray (1944–2002), suffered from ‘locked-in

syndrome’ after suffering a brain-stem stroke in 1997. Ray’s implant was installed in 1998

and he lived long enough to start working with the implant, eventually learning to control a

computer cursor; he died in 2002 of a brain aneurysm.

Tetraplegic Matt Nagle became the first person to control an artificial hand using a BCI in

2005 as part of the first nine-month human trial of Cyberkinetics’s BrainGate chip-implant.

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Implanted in Nagle’s right precentral gyrus (area of the motor cortex for arm movement),

the 96-electrode BrainGate implant allowed Nagle to control a robotic arm by thinking

about moving his hand as well as a computer cursor, lights and TV.One year later, professor

Jonathan Wolpaw  received the prize of the Altran Foundation for Innovation to develop a

Brain Computer Interface with electrodes located on the surface of the skull, instead of

directly in the brain.

More recently, research teams led by the Braingate group at Brown University  and a group

led by University of Pittsburgh Medical Center, both in collaborations with the United

States Department of Veterans Affairs, have demonstrated further success in direct control

of robotic prosthetic limbs with many degrees of freedom using direct connections to arrays

of neurons in the motor cortex of patients with tetraplegia.

Partially invasive BCIs

Partially invasive BCI devices are implanted inside the skull but rest outside the brain rather

than within the grey matter. They produce better resolution signals than non-invasive BCIs

where the bone tissue of the cranium deflects and deforms signals and have a lower risk of

forming scar-tissue in the brain than fully invasive BCIs.

Electrocorticography (ECoG) measures the electrical activity of the brain taken from

beneath the skull in a similar way to non-invasive electroencephalography (see below), but

the electrodes are embedded in a thin plastic pad that is placed above the cortex, beneath

the dura mater. ECoG technologies were first trialed in humans in 2004 by Eric Leuthardt

and Daniel Moran from Washington University in St Louis. In a later trial, the researchers

enabled a teenage boy to play Space Invaders using his ECoG implant. This research

indicates that control is rapid, requires minimal training, and may be an ideal trade off with

regards to signal fidelity and level of invasiveness.

(Note: these electrodes had not been implanted in the patient with the intention of

developing a BCI. The patient had been suffering from severe epilepsy and the electrodes

were temporarily implanted to help his physicians localize seizure foci; the BCI researchers

simply took advantage of this.)

Signals can be either subdural or epidural, but are not taken from within the

brain parenchyma itself. It has not been studied extensively until recently due to the limited

access of subjects. Currently, the only manner to acquire the signal for study is through the

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use of patients requiring invasive monitoring for localization and resection of an

epileptogenic focus.

ECoG is a very promising intermediate BCI modality because it has higher spatial

resolution, better signal-to-noise ratio, wider frequency range, and less training requirements

than scalp-recorded EEG, and at the same time has lower technical difficulty, lower clinical

risk, and probably superior long-term stability than intracortical single-neuron recording.

This feature profile and recent evidence of the high level of control with minimal training

requirements shows potential for real world application for people with motor disabilities.

Light Reactive Imaging BCI devices are still in the realm of theory. These would involve

implanting a laser inside the skull. The laser would be trained on a single neuron and the

neuron's reflectance measured by a separate sensor. When the neuron fires, the laser light

pattern and wavelengths it reflects would change slightly. This would allow researchers to

monitor single neurons but require less contact with tissue and reduce the risk of scar-tissue

build-up.

Non-invasive BCIs

As well as invasive experiments, there have also been experiments in humans using non-

invasive neuroimaging technologies as interfaces. Signals recorded in this way have been

used to power muscle implants and restore partial movement in an experimental volunteer.

Although they are easy to wear, non-invasive implants produce poor signal resolution

because the skull dampens signals, dispersing and blurring the electromagnetic waves

created by the neurons. Although the waves can still be detected it is more difficult to

determine the area of the brain that created them or the actions of individual neurons.

3.2 BMI components

A brain-machine interface (BMI) in its scientific interpretation is a combination of several

hardware and software components trying to enable its user to communicate with a

computer by intentionally altering his or her brain waves. The task of the hardware part is to

record the brainwaves– in the form of the EEG signal – of a human subject, and the software

has to analyze that data. In other words, the hardware consists of an EEG machine and a

number of electrodes scattered over the subject’s skull. The EEG machine, which is

connected to the electrodes via thin wires, records the brain-electrical activity of the subject,

yielding a multi-dimensional (analog or digital) output. The values in each dimension (also Progressive Education society’s

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called channel) represent the relative differences in the voltage potential measured at two

electrode sites.

The software system has to read, digitize (in the case of an analog EEG machine), and

preprocess the EEG data (separately for each channel), “understand” the subject’s

intentions, and generate appropriate output. To interpret the data, the stream of EEG values

is cut into successive segments, transformed into a standardized representation, and

processed with the help of a classifier. There are several different possibilities for the

realization of a classifier; one approach – involving the use of an artificial neural network

(ANN) – has become the method of choice in recent years.

Figure 3.3 Mental Task

The user is thinking task number 2 and the BCI classifies it correctly and provides feedback

in the form of cursor movement.

I. IMPLANT DEVICE

The EEG is recorded with electrodes, which are placed on the scalp. Electrodes are small

plates, which conduct electricity. They provide the electrical contact between the skin and

the EEG recording apparatus by transforming the ionic current on the skin to the electrical

current in the wires. To improve the stability of the signal, the outer layer of the skin called

stratum corneum should be at least partly removed under the electrode. Electrolyte gel is

applied between the electrode and the skin in order to provide good electrical contact.

Usually small metal-plate electrodes are used in the EEG recording. Neural implants can be

used to regulate electric signals in the brain and restore it to equilibrium. The implants must

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be monitored closely because there is a potential for almost anything when introducing

foreign signals into the brain.

There are a few major problems that must be addressed when developing neural implants.

These must be made out of biocompatible material or insulated with biocompatible material

that the body won’t reject and isolate. They must be able to move inside the skull with the

brain without causing any damage to the brain. The implant must be chemically inert so that

it doesn’t interact with the hostile environment inside the human body. All these factors

must be addressed in the case of neural implants; otherwise it will stop sending useful

information after a short period of time.

There are simple single wire electrodes with a number of different coatings to complex

three-dimensional arrays of electrodes, which are encased in insulating biomaterials.

Implant rejection and isolation is a problem that is being addressed by developing

biocompatible materials to coat or in case the implant.

One option among the biocompatible materials is Teflon coating that protects the implant

from the body. Another option is a cell resistant synthetic polymer like polyvinyl alcohol.

To keep the implant from moving in the brain it is necessary to have a flexible electrode that

will move with the brain inside the skull. This can make it difficult to implant the electrode.

Dipping the micro device in polyethylene glycol, which causes the device to become less

flexible, can solve this problem. Once in contact with the tissue this coating quickly

dissolves. This allows easy implantation of a very flexible implant.

Three-dimensional arrays of electrodes are also under development. These devices are

constructed as two-dimensional sheet and then bent to form 3D array. These can be

constructed using a polymer substrate that is then fitted with metal leads. They are difficult

to implement, but give a much great range of stimulation or sensing than simple ones.

A microscopic glass cone contains a neurotrophic factor that induces neuritis to grow into

the cone, where they contact one of several gold recording wires. Neuritis that is induced to

grow into the glass cone makes highly stable contacts with recording wires. Signal

conditioning and telemetric electronics are fully implanted under the skin of the scalp. An

implanted transmitter (TX) sends signals to an external receiver (RX), which is connected to

a computer.

3.3 METHODS OF BCI

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A.MULTICHANNEL ACQUISITION SYSTEM

Electrodes interface directly to the non-inverting opamp inputs on each channel. At this

section amplification, initial filtering of EEG signal and possible artifact removal takes

place. Also A/D conversion is made, i.e. the analog EEG signal is digitized. The voltage

gain improves the signal-to-noise ratio (SNR) by reducing the relevance of electrical noise

incurred in later stages. Processed signals are time-division multiplexed and sampled.

Figure 3.4 A BMI under design.

B.SPIKE DETECTION

Real time spike detection is an important requirement for developing brain machine

interfaces. Incorporating spike detection will allow the BMI to transmit only the action

potential waveforms and their respective arrival times instead of the sparse, raw signal in its

entirety. This compression reduces the transmitted data rate per channel, thus increasing the

number of channels that may be monitored simultaneously. Spike detection can further

Reduce the data rate if spike counts are transmitted instead of spike waveforms. Spike

detection will also be a necessary first step for any future hardware implementation of an

autonomous spike sorter. Figure 6 shows its implementation using an application-specific

integrated circuit (ASIC) with limited computational resources. A low power implantable

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ASIC for detecting and transmitting neural spikes will be an important building block for

BMIs.

C. SIGNAL ANALYSIS

Feature extraction and classification of EEG are dealt in this section. 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 frequency content of the EEG signal) can be calculated

using, for example, Fast Fourier Transform (FFT function). 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. The features extracted in the previous stage

are the input for the classifier.

.

Figure 3.5 signal analysis

D. EXTERNAL DEVICE

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 a writing

application. However, if the classification was “nothing” or “reject”, no action is performed,

although the user may be informed about the rejection. It is the device that subject produce

and control motion. Examples are robotic arm, thought controlled wheel chair etc

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E. FEEDBACK

Real-time feedback can dramatically improve the performance of a brain–machine interface.

Feedback is needed for learning and for control. Real-time feedback can dramatically

improve the performance of a brain–machine interface. In the brain, feedback normally

allows for two corrective mechanisms. One is the ‘online’ control and correction of errors

during the execution of a movement. The other is learning: the gradual adaptation of motor

commands, which takes place after the execution of one or more movements.

In the BMIs based on the operant conditioning approach, feedback training is essential for

the user to acquire the control of his or her EEG response. The BMIs based on the pattern

recognition approach and using mental tasks do not definitely require feedback training.

However, feedback can speed up the learning process and improve performance. Cursor

control has been the most popular type of feedback in BMIs. Feedback can have many

different effects, some often beneficial and some harmful. Feedback used in BMIs has

similarities with biofeedback, especially EEG bio feedback.

CHAPTER 4

BLOCK DIAGRAM AND WORKING PRINCIPLES

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Figure 4.1 Block Diagram

BLOCK DESCRIPTION

The BCI consists of several components: 1.the implant device, or chronic multi-electrode

array, 2.the signal recording and processing section, 3.an external device the subject uses to

produce and control motion and 4.a feedback section to the subject. The first component is an

implanted array of microelectrodes into the frontal and parietal lobes—areas of the brain

involved in producing multiple output commands to control complex muscle movements.

This device record action potentials of individual neurons and then represent the neural signal

using a rate code .The second component consists of spike detection algorithms, neural

encoding and decoding systems, data acquisition and real time processing systems etc .A high

performance dsp architecture is used for this purpose. The external device that the subject

uses may be a robotic arm, a wheel chair etc. depending upon the application. Feedback is an

important factor in BCI’s. In the BCI’s based on the operant conditioning approach, feedback

training is essential for the user to acquire the control of his or her EEG response. However,

feedback can speed up the learning process and improve performance.

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4.2 EEG

Overview

Figure 4.2 Recordings of brainwaves produced by an electroencephalogram

Electroencephalography (EEG) is the most studied potential non-invasive interface,

mainly due to its fine temporal resolution, ease of use, portability and low set-up cost. But

as well as the technology's susceptibility to noise, another substantial barrier to using EEG

as a brain–computer interface is the extensive training required before users can work the

technology. For example, in experiments beginning in the mid-1990s, Niels Birbaumer at

the University in Germany trained severely paralysed people to self-regulate the slow cortical

potentials in their EEG to such an extent that these signals could be used as a binary signal

to control a computer cursor. (Birbaumer had earlier trained epileptics to prevent impending

fits by controlling this low voltage wave.) The experiment saw ten patients trained to move

a computer cursor by controlling their brainwaves. The process was slow, requiring more

than an hour for patients to write 100 characters with the cursor, while training often took

many months.

Another research parameter is the type of oscillatory activity that is measured. Birbaumer's

later research with Jonathan Wolpaw at New York State University has focused on

developing technology that would allow users to choose the brain signals they found easiest

to operate a BCI, including muand beta rhythms.

A further parameter is the method of feedback used and this is shown in studies

of P300 signals. Patterns of P300 waves are generated involuntarily (stimulus-feedback)

when people see something they recognize and may allow BCIs to decode categories of

thoughts without training patients first. By contrast, the biofeedback methods described

above require learning to control brainwaves so the resulting brain activity can be detected.

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Lawrence Farwell and Emanuel Donchin developed an EEG-based brain–computer

interface in the 1980s. Their "mental prosthesis" used the P300 brainwave response to allow

subjects, including one paralyzed Locked-In syndrome patient, to communicate words,

letters and simple commands to a computer and thereby to speak through a speech

synthesizer driven by the computer. A number of similar devices have been developed since

then. In 2000, for example, research by Jessica Bayliss at the University of

Rochester showed that volunteers wearing virtual reality helmets could control elements in a

virtual world using their P300 EEG readings, including turning lights on and off and

bringing a mock-up car to a stop.

While an EEG based brain-computer interface has been pursued extensively by a number of

research labs, recent advancements made by Bin He and his team at the University of

Minnesotasuggest the potential of an EEG based brain-computer interface to accomplish

tasks close to invasive brain-computer interface. Using advanced functional neuroimaging

including BOLD functional MRI and EEG source imaging, Bin He and co-workers

identified the co-variation and co-localization of electrophysiological and hemodynamic

signals induced by motor imagination. Refined by a neuroimaging approach and by a

training protocol, Bin He and co-workers demonstrated the ability of a non-invasive EEG

based brain-computer interface to control the flight of a virtual helicopter in 3-dimensional

space, based upon motor imagination.

In addition to a brain-computer interface based on brain waves, as recorded from scalp EEG

electrodes, Bin He and co-workers explored a virtual EEG signal-based brain-computer

interface by first solving the EEG inverse problem and then used the resulting virtual EEG

for brain-computer interface tasks. Well-controlled studies suggested the merits of such a

source analysis based brain-computer interface.

4.3 INPUT METHODS

The EPOC has 14 electrodes (compared to the 19 electrodes of a standard medical

EEG, and the 3 of OCZ's NIA features and a multiple of Neuro Sky's single electrode). It also

has a two-axis gyro for measuring

1. Conscious thoughts (Cognitive suite): Imagining 12 kinds of movement- those were in

the demo application 6 directions (left, right, up, down, forward, and "zoom") and 6

rotations ([anti-]clockwise rotation, turn left and right, and sway backward and forward)-

plus 1 other visualization ("disappear") that can be detected in µ rhythms. While the current

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driver may only be able to listen for any 4 of these at a time, the degrees of freedom are

larger than a joystick's 2 df. Ideomotor responses or the much stronger EMG currents aside,

these thought Because of the complex detection algorithms involved, there is a slight lag in

detecting thoughts. However, the technology may still be useful in a support role like calling

up a minimap or radar in a FPS game.

2. Emotions (Affective suite): "Excitement", "Engagement/Boredom", "Meditation", and

"Frustration" can currently be measured. Emotiv admits that the names may not perfectly

reflect exactly what the emotion is, and says that they may be renamed before market

launch.

3. Facial expressions (Expressive suite): Individual eyelid and eyebrow positions, eye

position in the horizontal plane, smiling, laughing, clenching, and smirking can currently be

detected. Other expressions may be added prior to release.

4. Head rotation: The angular velocity of one's head can be measured in the yaw and pitch

(but not roll) directions. This is detected by gyros, and isn't related to the EEG features.

CHAPTER 5

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APPLICATIONS

Some of the BCI applications are discussed below.

5.1. The Mental Typewriter:

March 14, 2006 Scientists demonstrated a brain-computer interface that translates brain

signals into computer control signals this week at CeBIT in Berlin. The initial project

demonstrates how a paralysed patient could communicate by using a mental typewriter

alone – without touching the keyboard. In the case of serious accident or illness, a patient’s

limbs can be paralyzed, severely restricting communication with the outside world. The

interface is already showing how it can help these patients to write texts and thus

communicate with their environment. There’s also a PONG game (computer tennis) used to

demonstrate how the interface can be used. Brain

Pong involves two BBCI users playing a game of

teletennis in which the “rackets” are controlled by

imagining movements and predictably the general

media has focussed the majority of its attention on

computer gaming applications but BCCI could

equally be used in safety technologies (e.g. in

automobiles for

Figure 5.1 Web Search-

monitoring cognitive driver stress), in controlling prostheses, wheelchairs, instruments and

even Machinery.

5.2 Web Search

For the first test of the sensors, scientists trained the software program to recognize six

words - including "go", "left" and "right" - and 10 numbers. Participants hooked up to the

sensors silently said the words to themselves and the software correctly picked up the

signals 92 per cent of the time.

5.3. BCI offers paralyzed patients improved quality of life:

Tuebingen, Germany. A brain–computer interface installed early enough in patients with

neuron-destroying diseases can enable them to be taught to communicate through an

electronic device and slow destruction of the nervous system.

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Fundamental theories regarding consciousness, emotion and quality of life in sufferers of

paralysis from Amyotrophic Lateral Sclerosis (ALS, also known as 'Lou Gerhig's

The study appears in the latest issue of Psychophysiology. The article reviews the usefulness

of currently available brain-computer –interfaces (BCI), which use brain activity to

communicate through external devices, such as computers.

5.4. Adaptive BMI for Augmented Cognition and Action :

The goal of this project is to demonstrate improved human/computer performance for

specific tasks through detection of task-relevant cognitive events with real-time EEG (Fig.

1). For example, in tasks for which there is a direct trade off between reaction time and error

rate, (such as typing or visual search) it may be beneficial to correct a user’s errors without

interrupting the pace of the primary task.

(1) Error and conflict perception:

Error related negativity (ERN) in EEG has been linked to perceived response errors and

conflicts in decision-making. In this project we have developed single trial ERN detection to

predict task-related errors

(2) Working memory encoding.

Transient modulation of oscillations in the theta (4-8 Hz) and gamma (20-30 Hz) bands,

recorded using EEG and magneto encephalography (MEG), have been implicated in the

encoding and retrieval of semantic information in working memory.

(3) Rapid visual recognition:

We are exploring the signals elicited by visual target detection, which were recently

observed in rapid sequential visual presentation (RSVP) experiments. We have

demonstrated that the detection of these signals on a single trial basis can be used to replace

the slow manual response of a human operator, thereby significantly increasing the

throughput of image search tasks.

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CHAPTER 6

ADVANTAGES

This prototype mind-controlled wheelchair developed from the University of Electro-

Communications in Japan lets you feel like half Professor X and half Stephen Hawking—

except with the theoretical physics skills of the former and the telekinetic skills of the latter.

A little different from the Brain-Computer Typing machine, this thing works by mapping

brain waves when you think about moving left, right, forward or back, and then assigns that

to a wheelchair command of actually moving left, right, forward or back.

The sensors have already been used to do simple web searches and may one day help space-

walking astronauts and people who cannot talk. The system could send commands to rovers

on other planets, help injured astronauts control machines, or aid disabled people.

In everyday life, they could even be used to communicate on the sly - people could use them

on crowded buses without being overheard

The pilot of a high-speed plane or spacecraft, for instance, could simply order by thought

alone some vital flight information for an all-purpose cockpit display. There would be no

need to search for the right dials or switches on a crowded instrument panel.

Depending on how the technology is used, there are good and bad effects

1. In this era where drastic diseases are getting common it is a boon if we can develop it to

its full potential.

2. Also it provides better living, more features, more advancement in technologies etc.

3. Linking people via chip implants to super intelligent machines seems to a natural

progression –creating in effect, super humans.

4. Linking up in this way would allow for computer intelligence to be hooked more directly

into the brain, allowing immediate access to the internet, enabling phenomenal math

capabilities and computer memory.

5. By this humans get gradual co-evolution with computers.

DISADVANTAGES

1. Researchers from the Max Planck Institute for Human Cognitive and Brain Sciences, along

with scientists from London and Tokyo, asked subjects to secretly decide in advance

whether to add or subtract two numbers they would later are shown. Using computer

algorithms and functional magnetic resonance imaging, or fMRI, the scientists were able to

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determine with 70 percent accuracy what the participants' intentions were, even before they

were shown the numbers. The popular press tends to over-dramatize scientific advances in

mind reading. FMRI results have to account for heart rate, respiration, motion and a number

of other factors that might all cause variance in the signal. Also, individual brains differ, so

scientists need to study a subject's patterns before they can train a computer to identify those

patterns or make predictions.

2. While the details of this particular study are not yet published, the subjects' limited options

of either adding or subtracting the numbers means the computer already had a 50/50 chance

of guessing correctly even without fMRI readings. The researchers indisputably made

physiological findings that are significant for future experiments, but we're still a long way

from mind reading.

3. Still, the more we learn about how the brain operates, the more predictable human beings

seem to become. In the Dec. 19, 2006, issue of The Economist, an article questioned the

scientific validity of the notion of free will: Individuals with particular congenital genetic

characteristics are predisposed, if not predestined, to violence.

4. Studies have shown that genes and organic factors like frontal lobe impairments, low

serotonin levels and dopamine receptors are highly correlated with criminal behaviour.

Studies of twins show that heredity is a major factor in criminal conduct. While no one gene

may make you a criminal, a mixture of biological factors.

5. Looking at scientific advances like these, legal scholars are beginning to question the

foundational principles of our criminal justice system.

CHALLENGES

1. Connecting to the nervous system could lead to permanent brain damage, resulting in the

loss of feelings or movement, or continual pain.

2. In the networked brain condition –what will mean to be human?

3. Virus attacks may occur to brain causing ill effects.

LIMITATION OF STUDY

1. The equipments needed are expensive, with the cheapest as expensive as $5,145 (which is

approximately equivalent to N771, 750).

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2. In the case where a device (even one as tiny as a logic gate) is been implanted on the brain,

the medical procedure is very delicate and complications can arise practically from nowhere

in the twinkle of an eye.

3. Lack of awareness and fear of its long-term effects have discouraged a lot of patients from

becoming willing ‘guinea pigs’,

4. Specialists that are experienced enough to carry out the necessary operations are few due to

the fact that this study is still being researched.

5. There are about 100billion neurons in a human brain. Each neuron is constantly sending &

receiving signals through a WEB of connections. This makes designing a device as

seemingly simple as an arm having grippers (in place of fingers) difficult.

6. EEG measure tiny voltage potentials, and sometimes a simple as the blinking eyelids of the

subject can generate much stronger signals than can be read by the EEG.

7. Although we already understand the basic principles behind BCIs, they don't work perfectly.

8. The brain is incredibly complex. To say that all thoughts or actions are the result of simple

electric signals in the brain is a gross understatement. There are about 100 billion neurons in

a human brain. Each neuron is constantly sending and receiving signals through a complex

web of connections.

9. The equipment is less than portable. It's far better than it used to be -- early systems were

hardwired to massive mainframe computers. But some BCIs still require a wired connection

to the equipment, and those that are wireless require the subject to carry a computer that can

weigh around 10 pounds. Like all technology, this will surely become lighter and more

wireless in the future.

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CHAPTER 7

CURRENT PROJECTS

“Begin developing products and applications that interface with the brain, by using

NeuroSky’s MindSet Development Tools. NeuroSky’s Brain-Computer Interface (BCI)

turns the users thoughts into actions, unlocking new worlds of interactivity. By monitoring

your brain waves, the headset can send messages to a device—allowing the user to control

the device with their thoughts. The Mindset Development Tools (MDT) includes everything

necessary to develop applications for the Mindset. The NeuroSky MindSet can be paired

with video games, research devices or a number of other tools for an enhanced user

experience. Upon approval, applications created using the Mindset Development Tools

qualify for entry into the NeuroSky Store.

6.1 Programmable Brainwave-Sensitive Headset

NeuroSky’s Brainwave Device Lets Users Move Objects With Their Thoughts / July 17,

2008

Attention. Meditation. Two default human settings. You pay attention when you’re driving.

You’re in a state of meditation when you watch the ocean, or perhaps a good movie. You

may even meditate on purpose, sitting a pillow with closed eyes. Silicon Valley innovator

NeuroSky has given new meaning to these two states of mind. Attention means you can

move things back and forth. Meditation means you can levitate objects. Sound outlandish,

like a lucid dream, or a hallucinogenic flashback? Actually, all it takes is a headset.

NeuroSky, using a consumer-friendly EEG device, is taking Virtual Reality one step closer

to just plain Real. NeuroSky has created a wearable biosensor allowing users to control

electronic devices with their minds. A red wire feeds brainwaves into a patented device that

turns them into commands. The device uses a series of emotion-based algorithms to allow

users to push and pull objects (virtual or external) based on an attention or meditation rating.

For example, users can levitate virtual objects with a strong meditation rating, or push/pull

objects with a strong attention rating. Their technology allows users to control just about

any mechanical system. They plan to expand into brainwave-read robots for the elderly and

disabled in the future, as well as diversify their command suite to include more emotions

and subtle movements such as eye blinks. Business Pundit interviewed Greg Hyver, VP of

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Marketing at San Jose brainwave device manufacturer, to find out more about NeuroSky’s

fascinating EEG device.

The first challenge was actually non-technical in nature. We had to identify the conditions in

which an “average” consumer would actually purchase a device that reads and translates

their brainwaves to perform a function. NeuroSky identified five major categories that we

had to address to establish our products’ design parameters: (a) price; (b) wearability; (c)

ease-of-use; (d) mobility; (e) utility. It all came down to building a dry (no electrode gel),

single-EEG-sensor solution that met each of our markets’ technical and costing

requirements.

The medical industry already uses multiple sensor headsets and it didn’t seem to make much

sense for us to produce another “medical-like” headset. You may lose a few features with a

single sensor versus multiple sensors, but it’s much more intuitive for a consumer who has

never used this type of technology to understand and control it more quickly and

consistently. We didn’t want to frustrate the consumer by producing a multiple sensor

headset that created head placement problems, lengthy calibration periods, special training

sessions, non-repeatable performances and high-end (and expensive) hardware requirements

that limited a platform’s (mobility) ability to support it.

By making it simple, we created a fully-embedded headset where all of the processing

(reading-filtering-amplifying EEG, translating the mental states) is done on the headset,

itself, without requiring a remote processing device. Since we don’t steal processor

bandwidth from the remote device (for example, a simple toy product), our technology can

effectively communicate with any product that can read our data stream.

6.2 Wheelchair Access

A motorised wheelchair that moves when the operator thinks of particular words has been

demonstrated by a US company. The wheelchair works by intercepting signals sent from

their brain to their voice box, even when no sound is actually produced. The company

behind the chair, Ambient, is developing the technology with the Rehabilitation Institute of

Chicago, in the US. The wheelchair could help people with spinal injuries, or neurological

problems like cerebral palsy or motor neurone disease, operate computers and other

equipment despite serious problems with muscle control. The system will work providing a

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person can still control their larynx, or “voice box”, which may be the case even if the lack

the muscle coordination necessary to produce coherent speech.

The larynx control system, called Audeo, was developed by researchers Michael Callahan

and Thomas Coleman at University of Illinois at Urbana-Champaign, US, who together also

founded Ambient. Restored speech The system works via a sensor-laden neckband which

eavesdrops on electrical impulses sent to larynx muscles. It then relays the signals, via an

encrypted wireless link, to a nearby computer. The computer decodes these signals and

matches them to a series of pre-recorded “words” determined during training exercises.

These “words” can then be used to direct the motorised wheelchair. Callahan and Coleman

say they can also be sent to a speech synthesiser, allowing a paralysed person to “speak” out

loud. Recent refinements to the algorithms used may make it possible to interpret whole

sentences thought out by the user. This could potentially restore near-normal speech to

people who have not spoken for years, the researchers say. “Everyone working on brain-

computer interfaces wants to be able to identify words,” says Niels Birbaumer from

Eberhard Karls University in Tübingen, Germany, who is developing similar systems for

use by stroke victims. “If this works reliably I would be very impressed, it is very hard to

record signals from nerves through the skin.”

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CHAPTER 8

FUTURE EXPANSION

A new thought-communication device might soon help severely disabled people get

their independence by allowing them to steer a wheelchair with their mind. Mind-machine

interfaces will be available in the near future, and several methods hold promise for

implanting information. . Linking people via chip implants to super intelligent machines

seems to a natural progression –creating in effect, super humans. These cyborgs will be one

step ahead of humans. And just as humans have always valued themselves above other

forms of life, it is likely that cyborgs look down on humans who have yet to ‘evolve’.

Will people want to have their heads opened and wired? Technology moves in light speed

now. In that accelerated future, today’s hot neural interface could become tomorrow’s neuro

trash. Will you need to learn any math if you can call up a computer merely by your

thoughts? Thought communication will place telephones firmly in the history books.

One professor used the following example of a real world use: "If it knew which air traffic

controllers were overloaded, the next incoming plane could be assigned to another

controller." 

Hence if we get 100% accuracy these computers may find various applications in

many fields of electronics where we have very less time to react Brain-Computer Interface

(BCI) is a method of communication based on voluntary neural activity generated by the

brain and independent of its normal output pathways of peripheral nerves and muscles. The

neural activity used in BCI can be recorded using invasive or non-invasive techniques. We

can say as detection techniques and experimental designs improve, the BCI will improve as

well and would provide wealth alternatives for individuals to interact with their

environment.

As future research and developments the intention is to do:

Improving physical methods for gathering EEGs

Improving generalization

Improving knowledge of how to interpret waves (not just the “new phrenology”)

Improving the present complex system to SOC by advanced technologies.

These will imply more applications in:

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Professional Gaming.

Robotics in Medical Usage (e.g. Robotic Arm).

Artificial intelligence.

BCI is technology which can bring new revolutionary trade marks in the market if this is

made portable and easy to accessible.

CHAPTER 9

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CONCLUSION

Cultures may have diverse ethics, but regardless, individual liberties and human life

are always valued over and above machines. What happens when humans merge with

machines? The question is not what will the computer be like in the future, but instead, what

will we be like? What kind of people are we becoming?

BMI’s will have the ability to give people back their vision and hearing. They will also

change the way a person looks at the world. Someday these devices might be more common

than keyboards. Is someone with a synthetic eye, less a person than someone without? Shall

we process signals like ultraviolet, X-rays, or ultrasounds as robots do? These questions will

not be answered in the near future, but at some time they will have to be answered. What an

interesting day that will be.

Tufts University researchers have begun a three-year research project which, if successful,

will allow computers to respond to the brain activity of the computer's user. Users wear

futuristic-looking headbands to shine light on their foreheads, and then perform a series of

increasingly difficult tasks while the device reads what parts of the brain are absorbing the

light. That info is then transferred to the computer, and from there the computer can adjust

its interface and functions to each individual. 

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REFERENCES

BOOKS

[1]Handbook of Biomedical Instrumentation, R.S.Khandpur

[2] Biased feedback in brain-computer interfaces, Álvaro Barbero and Moritz Grosse-

Wentrup

JOURNAL ARTICLES

[3] Lebedev, MA; Nicolelis, MA (2006). "Brain-machine interfaces: past, present and

future". Trends in neurosciences ,29 (9)

[4] Vidal, JJ (1973). "Toward direct brain-computer communication". Annual review of

biophysics and bioengineering 2, 157–80.

[5]  D. Plass-Oude Bos, B. Reuderink, B. van de Laar, H. Gürkök, C. Mühl, M. Poel, A.

Nijholt, D. Heylen. "Brain-Computer Interfacing and Games “Brain-Computer Interfaces

2010: 149–178

WEB SITES

[6] blog.marcelotoledo.org/2007/10

[7] www.newscientist.com/article/dn4795-nasa-develops-mindreading-system

[8] http://blogs.vnunet.com/app/trackback/95409

[9] http://www.nicolelislab.net/NLnet_Load.html

[10] http://www.youtube.com/watch?v=7-cpcoIJbOU

[11] http://www.en.wikipedia.com/braincomputerinterface

[12] http://www. Ieeeexplore.com

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