1

CHAPTER-1

INTRODUCTION

The main aim of this project is to control the device based on electrical

signals of brain. The other is to provide a comprehensive review and comparison

of the most important Brain Computer Interface (BCI) systems developed to this

day. Brain-Computer Interface (BCI) is a communication system, which enables

the user to control special computer applications by using only his or her thoughts.

Different research groups have examined and used different methods to achieve

this. Almost all of them are based on electro encaphalo graphy (EEG) recorded

from the scalp. The EEG is measured and sampled while the user imagines

different things (for example, moving the left or the right hand). Depending on the

BCI, particular preprocessing and feature extraction methods are applied to the

EEG sample of certain length.

It is then possible to detect the task-specific EEG signals or patterns from the

EEG samples with a certain level of accuracy. First signs of BCI research can be

dated back to 1960’s, but it was in 1990’s when the BCI research really got started.

Faster computers and better EEG devices offered new possibilities. To date there

have been over 20 BCI research groups. They have taken different approaches to

the subject, some more successful than others. Less than half of the BCI researches

groups have build an online BCI, which can give feedback to the subject. None of

the BCIs have yet become commercial and only a couple has been tested outside

laboratory environments. Despite the technological developments numerous

problems still exists in building efficient BCIs. The biggest challenges are related

to accuracy, speed and usability. Other interfaces are still much more efficient.

2

1.1 OBJECTIVE

Drowsyness plays a major role in most of the car accidents. This type of

accidents can be avoided by sensing the brain wave signals using brainwave

sensor. Meditation level is used to find the drowsyness level of the driver and alert

them if it crosses the threshold level.

1.2 RHYTHMIC BRAIN ACTIVITY

Depending on the level of consciousness, normal people’s brain waves show

different rhythmic activity. For instance, the different sleep stages can be seen in

EEG. Different rhythmic waves also occur during the waking state. These rhythms

are affected by different actions and thoughts, for example the planning of a

movement can block or attenuate a particular rhythm. The fact that mere thoughts

affect the brain rhythms can be used as the basis for the BCI. The various brain

rhythms are.

1.2.1 DELTA RHYTHM

EEG waves below 3.5 Hz (usually 0.1-3.5 Hz) belong to the delta waves.

Infants (around the age of 2 months) show irregular delta activity of 2-3.5 Hz

(amplitudes 50-100 V) in the waking state. In adults delta waves (frequencies

below 3.5 Hz) are only seen in deep sleep and are therefore not useful in BCIs.

Delta rhythm is shown in Figure 1.1.

Figure 1.1 Delta rhythm

3

1.2.2 THETA RHYTHM

Theta waves are between 4 and 7.5 Hz. Theta rhythm plays an important role

in infancy and childhood. In normal adults theta waves are seen mostly in states of

drowsiness and sleep. During waking hours the EEG contains only a small amount

of theta activity and no organized theta rhythm. Niedermayer lists some studies in

which the theta activity of 6-7 Hz over frontal midline region had been correlated

with mental activity such as problem solving. However, he did not find it in his

own studies. Theta rhythm is shown in Figure 1.2.

Figure1.2 Theta rhythm

1.2.3 ALPHA RHYTHM

The International Federation of Societies for Electroencephalography and

Clinical Neurophysiology proposed the following definition of alpha rhythm:

Rhythm at 8-13 Hz occurring during wakefulness over the posterior regions of the

head, generally with higher voltage over the occipital areas. Amplitude is variable

but is mostly below 50 _V in adults. Best seen with eyes closed and under

conditions of physical relaxation and relative mental inactivity. Blocked or

attenuated by attention, especially visual, and mental effort’.

The posterior basic rhythm increases in frequency during the childhood and

reaches the frequency 8 Hz (the limit of the alpha rhythm) at the age of 3 years. At

the age of 10 years the frequency reaches a mean of about 10 Hz, which is typical

mean adult alpha frequency. The frequency tends to decline in elderly individuals

and in dementia. The alpha rhythm is temporarily blocked, i.e, its amplitude

decreased, by eye opening, other afferent stimuli or mental activities. The degree

4

of reactivity varies. Usually, eye opening is the most effective manipulation. Alpha

rhythm is shown in Figure 1.3.

Figure 1.3 Alpha rhythm

1.2.4 BETA RHYTHMS

Any rhythmical activity in the frequency band of 13-30 Hz may be regarded

as a beta rhythm. Beta rhythm amplitudes are seldom larger than 30 V. Beta

rhythms can mainly be found over the frontal and central region. A central beta

rhythm is related to the mu rhythm. It can be blocked by motor activity and tactile

stimulation. Beta rhythm is shown in Figure 1.4.

Figure 1.4 Beta rhythm

1.3 ADVANTAGES

Detects the drowsiness in drivers even if the eye is open.

More accurate.

Faster response.

5

CHAPTER-2

LITERATURE SURVEY

Chris Berka , Daniel Levendowski, J., Philip Westbrook , Gene Davis ,

Michelle Lumicao, N., Richard Olmstead, E., Miodrag Popovic , Vladimir

Zivkovic, T., Caitlin Ramsey, K., (2012) in Implementation of a Closed-Loop

Real-Time EEG-Based Drowsiness Detection System, [1] stated that With the

growing demands of the global economy for round-the-clock operations, fatigue

management is increasingly important, particularly in safety-sensitive

environments such as military operations and commercial transportation. Safety,

efficiency and productivity are all impacted by employee alertness. Fatigue-related

accidents and decreased productivity associated with drowsiness are estimated to

cost the U.S. over $77 billion each year and $377 billion worldwide. It is estimated

that more U.S. freeway fatalities are caused by fatigue than alcohol or drugs, with

10% to 50% of motor vehicle accidents attributed to sleepiness. In addition, over

30 million Americans are believed to suffer from sleep disorders, the majority

undiagnosed and untreated, resulting in dangerous levels of daytime drowsiness.

As more workers are forced into shift work to meet the demands of a 24-hour

society, sleep is often sacrificed for other activities. Although automation is

replacing manual labor, it can have a deleterious effect if it causes the operator to

be disengaged from the controls of machinery. Passive monitoring of automated

equipment can increase the difficulty of maintaining vigilance with performance

decrements increasing with time-on-task.

Several studies revealed that people are not good judges of their own level of

fatigue. The AAA Foundation for Traffic Safety interviewed 467 drivers involved

in police-reported crashes whose physical condition at the time of the crash was

6

identified as either “asleep” or “drowsy”. While most drivers agreed with the

police officer’s assessment of the role of drowsiness in their accident, close to 50%

reported feeling either “slightly” or “not at all” drowsy just prior to the crash.

Similarly, AAA Foundation research found that 50% of people tested during sleep

deprivation were unable to predict whether they would fall asleep within the next

two minutes. The study concluded that a “sleepiness indicator device” should be

developed to inform users prior to sleep onset.

The integration of physiological monitoring into the man-machine interface

offers the possibility of allocating tasks based on real-time assessment of operator

status. Real-time monitoring could drive intelligent feedback or facilitate active

intervention by the operator or through a third party (man or machine), increasing

safety and productivity. The achievement of such a system is particularly relevant

for the development of future military technology where the emphasis is

increasingly on unmanned vehicles and aircraft, maximizing capacity while

limiting the need for additional human resources. This study was designed to

investigate the utility of a method for real-time detection of drowsiness with alarms

delivered directly to the user.

Deepa.T.P, Vandana Reddy (2013) in EEG Based Drowsiness Detection

Using Mobile Device for Intelligent Vehicular System, [2] stated that tiny

electrical signals are produced by brain cells when they pass message to each

other. Electrodes which are placed on brain scalp of subject (person) will pick up

these signals and send them to machine called as Electroencephalograph (EEG).

EEG will record the signals as waves or wavy lines on to display or paper. This

pattern of electric activity produced on EEG can be used for various applications

like sleep detection, drowsiness detection, and sleep disorders like insomnia,

studying brain activities of coma patients and to diagnose many other conditions

7

which affect the brain. This paper discuss about how EEG can be used to

implement drowsiness detection in intelligent transportation system for example,

cars, airplanes, helicopters etc to monitor drowsiness status of the driver /pilot

(called as subject in this paper) and alert them being sleep.

Driver is the main part of vehicle system and driver condition is related to

traffic safety such as driver’s emotion state, fatigue state, drunken state etc. Study

says accuracy of these states will be reflected very efficiently in EEG. So, now a

days in intelligent transportation systems which mainly has network and

information, if driver’s EEG information can be gathered in Real-time and

transportation system is synchronized to this then driver can be alerted and

necessary measures can be taken against accidents due to sleep state of driver. In

this paper, combining the mobile device and EEG measuring instrument, the

experiments of drowsiness driving will be designed. EEG signals were measured

when they were in normal, drowsyness, sleep state.

Drowsiness detection is a challenging task on live signals. Many techniques

have been proposed on this. One of the method is using Mahalanobis Distance

which transforms a given multi normal distribution into the simple standard

(spherical) multi normal distribution. It also helps in studying the distributions and

conditions of independence of quadratic forms in multivariate normal variables.

Frequency bands are separated using DFT or FFT to an EEG Signal. After this, the

magnitude values are stored for each band. The lower cut of frequency and upper

cut of frequency is defined depends upon the frequency range of each band only

the signals between the upper band and lower band of EEG remain as it is and

others magnitudes are zero padded., for plotting graphical results for each band

like Delta, Alpha, Beta and Theta for calculation of percentage power in each

bands. The lower bands and upper bands are defined depend upon the frequency

8

range of each band. This detection system can be fully hardware controlled using

micro controller .

Krishnaveni Yendrapalli, Naga Pavan Kumar Tammana, S. S., (2014) in

The Brain Signal Detection for Controlling the Robot, [6] stated that Human

brain consists of millions of interconnected neurons. The patterns of interaction

between these neurons are represented as thoughts and emotional states. According

to the human thoughts, this pattern will be changing which in turn produce

different electrical waves. A muscle contraction will also generate a unique

electrical signal. All these electrical waves will be sensed by the brain wave sensor

and it will convert the data into packets and transmit through Bluetooth medium.

Level analyzer unit (LAU) will receive the brain wave raw data and it will

extract and process the signal using MATLAB platform. Then the control

commands will be transmitted to the robotic module which is the vehicle section.

With this entire system, we can move a robot according to the human thoughts and

it can be turned by blink thoughts and it can be turned by blink muscle contraction.

Electroencephalography (EEG) is the measurement of electrical activity in the

living brain. In this project we used a brainwave sensor MW001 to analyze the

EEG signals. This design discuss about processing and recording the raw EEG

signal from the Mind Wave sensor in the MATLAB environment and through

Zigbee transmission control commands will be passed to the Robot section. Mind

wave sensors are not used in clinical use, but are used in the Brain Control

Interface (BCI) and neuro feedback (one of biofeedback types). The BCI is a direct

communication pathway between the brain and an external device to provide direct

communication and control between the human brain and physical devices by

translating different patterns of brain activity into commands in real time.

9

Uma, K.J., Santha Kumar, C., (2014) in Non-Invasive EEG Based

Wireless Brain Computer Interface for Safety Applications Using Embedded

Systems, [11] stated that Drowsiness in drivers has been implicated as a causal

factor in many accidents because of the marked decline in drivers’ perception of

risk and recognition of danger, and diminished vehicle-handling abilities. The

National Sleep Foundation (NSF) reported that 51% of adult drivers had driven a

vehicle while feeling drowsy and 17% had actually fallen asleep. Therefore, real-

time drowsiness monitoring is important to avoid traffic accidents. Previous studies

have proposed a number of methods to detect drowsiness. They can be categorized

into two main approaches. The first approach focuses on physical changes during

fatigue, such as the inclination of the driver’s head, sagging posture, and decline in

gripping force on the steering wheel. The movement of the driver’s body is

detected by direct sensor contacts or video cameras. Since these techniques allow

noncontact detection of drowsiness, they do not give the driver any discomfort.

This will increase the driver’s acceptance of using these techniques to monitor

drowsiness. However, these parameters easily vary in different vehicle types and

driving conditions. The second approach focuses on measuring physiological

changes of drivers, such as eye activity measures, heart beat rate, skin electric

potential, and electro encephalographic (EEG) activities. It is reported that the eye

blink duration and blink rate typically are sensitive to fatigue effects. Further the

eye-activity-based methods are compared with EEG-based methods for alertness

estimates in a compensatory visual tracking task. In this a real-time wireless EEG-

based brain–computer interface (BCI) system for drowsiness detection is proposed.

The proposed BCI system consists of a wireless physiological signal-

acquisition module and an embedded signal processing module. Here, the wireless

physiological signal-acquisition module is used to collect EEG signals and transmit

10

them to the embedded signal-processing module wirelessly. It can be embedded

into a headband as a wearable EEG device for long-term EEG monitoring in daily

life. The embedded signal processing module, which provides powerful

computations and supports various peripheral interfaces, is used to real-time detect

drowsiness and trigger a warning tone to prevent traffic accidents when drowsy

state occurs.

Variability in EEG dynamics relating to drowsiness from alertness is large.

The same detection model may not be effective to accurately predict subjective

changes in the cognitive state. Therefore, subject-dependent models have also been

developed to account for individual variability. Although subject-dependent

models can alleviate the influence of individual variability in EEG spectra, they

still cannot account for the cross-session variability in EEG dynamics due to

various factors, such as electrode displacements, environmental noises, skin-

electrode impedance, and baseline EEG differences.

11

BRAIN COMPUTER INTERFACE SYSTEM

Brain wave signal

Human Brain

Raw brain wave signal transmission

BlueTooth Packets transmision

Raw data transmission

Brain wave sensor

Reference ground

connection

Dry electrode

unit

EEG Power Spectrum Process

CHAPTER-3

PROJECT DESCRIPTION

3.1 BLOCK DIAGRAM

Figure 3.1 Block diagram of the proposed model

DATA PROCESSING UNIT

Level Splitter

Section

Raw data extraction and Processing unit

BlueTooth Reception

serial data transmission transmission

VEHICLE SECTION

Motor

rrr 1 Motor

1

ARM

PWM

GPIO U A R T

Serial data

reception

Alert Display

12

The functional blocks of the block diagram shown in Figure 3.1 are explained as

follows,

3.2 BRAIN COMPUTER INTERFACE SYSTEM

3.2.1 HUMAN BRAIN

The average human brain weights around 1400 grams. The brain can be

divided into four structures: cerebral cortex, cerebellum, brain stem, hypothalamus

and thamalus. The most relevant of them concerning BCIs is the cerebral cortex.

The cerebral cortex can be divided into two hemispheres. The hemispheres are

connected with each other via corpus callosum. Each hemisphere can be divided

into four lobes. They are called frontal, parietal, occipital and temporal lobes.

Cerebral cortex is responsible for many “higher order” functions like problem

solving, language comprehension and processing of complex visual Information.

The cerebral cortex can be divided into several areas, which are responsible of

different functions. These areas can be seen in Figure 3.2. The functions are

described in Table 3.1. These kinds of knowledge have been used when with BCIs

based on the pattern recognition approach. The mental tasks are chosen in such a

way that they activate different parts of the cerebral cortex.

13

Figure 3.2 Functional areas of the brain

14

Table 3.1: Cortical areas of the brain and their function

Type Frequency Location Use

Delta <4 Hz everywhere occur during sleep, coma

Theta 4-7 Hz temporal and

parietal correlated with emotional stress (frustration & disappointment)

Alpha 8-12 Hz occipital and parietal reduce amplitude with sensory

stimulation or mental imagery

Beta 12-36 Hz parietal and frontal can increase amplitude during

intense mental activity

Mu 9-11 Hz frontal (motor

cortex) diminishes with movement or

intention of movement

Lambda sharp, jagged occipital correlated with visual attention

15

3.2.2 BRAIN WAVE SENSOR

Electro encephalography (EEG) is a method used in measuring the electrical

activity of the brain. The electrical activity of a single neuron cannot be measured

with scalp EEG.

Four prerequisites, which must be met for the activity of any network of neurons to

be visible in EEG signal, are

The neurons must generate most of their electrical signals along a specific

axis oriented perpendicular to the scalp.

The neuronal dendrites must be aligned in parallel so that their field

potentials summate to create a signal which is detectable at a distance.

The neurons should fire in near synchrony.

The electrical activity produced by each neuron needs to have the same

electrical sign.

The various components of brain wave sensor are

Electrode records the EEG, 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. The placement of electrode is shown in

the Figure 3.3.

16

Figure 3.3 Placement of electrode on the scalp

EEG power spectrum analyzer Converts analog signal into digital signal.

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 (which describes the frequency

content of the EEG signal) can be calculated using, for example, Fast Fourier

Transform (FFT), the transfer function of an auto regressive (AR) model or

wavelet transform. No matter what features are used, the goal is to form distinct set

of features for each mental task.

17

3.3 DATA PROCESSING UNIT

This session involves detecting the meditation level of a person and to send

the control information to the vehicle unit if it exceeds the threshold value.

Drowsiness, eyes open and eyes closed are closely connected to alpha activity.

Once sleepiness forces the eyes to shut, alpha waves are strongest encephalogram

brain signals have reported that in sleepiness state alpha activity mainly seems in

os space and particularly magnitude of alpha2 wave like a better alpha band

(11~13Hz) increases. However, supposing traditional adults have their eyes open

notwithstanding they drowse, alpha changes of can’t be explain one thing logically.

The various sections of data processing unit are.

Level Splitter Section uses matlab with Think Gear library on receiving the

data packets from the brainwave sensor it checks the meditation level with the

given threshold value. If it exceeds the first threshold limit it sends the command

‘B’ to the vehicle unit through UART. After crossing the second threshold it

sends the command ‘A’ to the vehicle unit.

Bluetooth Section is used to receive the data packets transmitted by the brain

wave sensor. It uses the Bluetooth version 2.0 with a symbol rate of 3mbps.

Serial Data Transmitter (RS232) is used to transfer information between data

processing equipment and peripherals is in the form of digital data which is

transmitted in either a serial or parallel mode. Parallel communications are used

mainly for connections between test instruments or computers and printers, while

serial is often used between computers and other peripherals. Serial transmission

involves the sending of data one bit at a time, over a single communication line.

18

3.4 VEHICLE SECTION

This half consists of ARM core processor as a main unit, Brain wave device

system, Ignition unit, PC, alert section and a show unit. This modules with coming

up with and implementation technique is given below.

ARM processor is employed for dominant the system. Here we have a

tendency to square measure victimization the LPC2148 series, which has 2 UART.

Interrupt routine code is employed to visualize whether or not we have a tendency

to have gotten any serial interrupt. For this project we have a tendency to square

measure having some interrupt checking commands ‘B’ and ‘A’.

Once ARM processor receives a command ‘B’ through UART1, then the

processor can trigger the alarm circuit. Next, if the processor receives a command

‘A’, then the processor can move the motive force circuit. Attributable to this the

engine is going to be move instantly. So, this worth within the information base can

compare mechanically the motive force management unit can stop. This interrupt

routine code is going to be checked by the processor endlessly that will increase

the potency of the project.

During this project the engine unit are going to be controlled by a driver

circuit. The motive force circuit consists of a driver unit, electrical device and a

semiconductor unit. If the automobile is started, the engine are going to be turned

ON which implies ARM processor can offer the bias voltage to the semiconductor

unit to modify on the relay that successively activate the automobile engine.

Meantime the processor can check the interrupt routine.

19

CHAPTER-4

RESULTS

4.1 HARDWARE DESCRIPTION

The Figure 4.1 shows the brain wave sensor which detects brain wave

signals and converts them into digital packets and transmit them to level splitter

section using Bluetooth transmitter.

Figure 4.1 Brain Wave Sensor

20

The Figure 4.2 shows the continuous eye blink of the driver. For every blink

there is a dip in the waveform.

Figure 4.2 Eye Blink Signal

21

The Figure 4.3 shows the brainwave visualizer which indicates the varying

brainwave signals. In the Figure the meditation level of the driver is shown with

three ways of plotting. This visualizer is used to initialize the brain wave sensors.

Figure 4.3 Brain Wave Visualizer

22

The Figure 4.4 shows the blink detection of the driver by MATLAB. Three

blinks are required to activate the drowsyness detection in the program.

Figure 4.4 Blink Detection in MATLAB

23

The Figure 4.5 shows the plot of Meditation values and the blink values of

the driver. The black stared plot indicates the blink values of the driver and the

blue plot indicates the meditation level of the driver.

Figure 4.5 Plot of Blink and Meditation Level

24

The Figure 4.6 shows the buzzer activation. When the meditation crosses the

first threshold value control signal B is sent to the vehicle control unit through

serial data transmission.

Figure 4.6 Buzzer Activation

25

The Figure 4.7 shows the motor speed control. When the meditation level

crosses the second threshold level, control signal A is sent to the vehicle control

unit through serial data transmission.

Figure 4.7 Speed Control Activation

26

The Figure 4.8 shows the vehicle control unit of the proposed model. When

it receives the command B from the Level Splitter Section through UART1 Port, it

turns ON the buzzer. When it receives the command A from the Level Splitter

Section through UART1 port, it controls the speed of the motor.

Figure 4.8 Vehicle Control Unit

27

4.2 SOFTWARE DESCRIPTION

4.2.1 FLOW CHART

Figure 4.9 Flow Chart of Proposed Model

NO

YES

YES

INITALIZATION OF BRAINWAVE SENSOR

START

READ THE MEDITATION LEVEL

ACTIVATE BUZZER

CONTROL THE SPEED OF THE NOTOR

IF

MEDITATION

>90

READ THE MEDITATION LEVEL

STOP

NO

IF

MEDITATION

>80

28

4.2.2 ALGORITHM

Step 1

The brain wave sensor is first initialized by using brain wave visualizer

software.

Step2

After initialization connect the brain wave sensor with the MATLAB tool.

Then read the meditation level value that is transmitted by the brain wave sensor.

Step 3

The meditation level is then compared with two different threshold values.

Step 4

If the meditation level is greater than the first threshold value it triggers the

buzzer, if not continue to read the meditation level.

Step 5

If the meditation level is greater than the second threshold value it controls

the speed of the car, if not continue to read the meditation level.

29

CHAPTER -5

CONCLUSION AND FUTURE SCOPE

In this work six EEG-based brain computer interface systems were reviewed

and compared. Experiments lasting five days with three subjects were done with

the Brain Interface system. The comparison of the BCI systems, especially their

training duration and performance, proved to be difficult. This was because the

results were reported inadequately and differently in most of the papers. Reporting

the experiments and results should be standardized.

Accuracy is the most important and affects greatly on the performance of the

BCI. Many of the BCI systems are operated in a synchronous way, using trials

lasting many seconds each. This means that time required for making one selection

is long. This time should be kept short (below one second). Feedback methods

could be improved, maybe using games like in the EEG biofeedback. Some of the

mental tasks used in the ABI and the experiments in this work are not good.

The relax task is the easiest to classify, but it includes eye opening and

closing, which is not permitted in a BCI by the definition presented in the

beginning of the second chapter. It can be argued if people suffering from locked-

in-syndrome can use the relax task. In addition, it is not good in applications,

because eyes are closed. Subtraction, word association and cube rotation tasks are

not very natural and practical in applications. The left and the right hand

movement are the most natural of the current tasks.

30

In the future, an exhaustive research about the mental tasks should be done.

A study of the left and right hand movements using high-resolution EEG and MEG

is planned. Research topics would include the localization of the brain activity

during the mental tasks and how the EEG changes in process of time. Other

research areas would be feedback methods and online learning. There are many

challenges in the future of the BCI field. Currently none of the BCIs are capable of

proper cursor control, which could be used to control ordinary computer

applications. In the near future it is not possible and special applications must be

developed for BCIs. Today, special writing applications or Internet browser can

provide communication tools for severely disabled people. These applications

could be improved. In the future, BCIs could be used to control a hand prosthesis.

How well that can be achieved with EEG-based BCIs is not yet known. Non-

invasive BCIs recording activity directly from the motor cortex may be used for

this kind of purpose in the future.

31

APPENDIX

MICROCONTROLLER ARM LPC 2148

LPC 2148 BOARD

The LPC2141/2/4/6/8 microcontrollers are based on a 32/16 bit

ARM7TDMI-S CPU with real-time emulation and embedded trace support, that

combines the microcontroller with embedded high speed flash memory ranging

from 32 kB to 512 kB. A 128-bit wide memory interface and a unique accelerator

architecture enable 32-bit code execution at the maximum clock rate. For critical

code size applications, the alternative 16-bit Thumb mode reduces code by more

than 30 % with minimal performance penalty. Due to their tiny size and low power

consumption, LPC2141/2/4/6/8 are ideal for applications where miniaturization is

a key requirement, such as access control and point-of-sale. A blend of serial

communications interfaces ranging from a USB 2.0 Full Speed device, multiple

UARTs, SPI, SSP to I2Cs, and on-chip SRAM of 8 kB up to 40 kB, make these

devices very well suited for communication gateways and protocol converters, soft

modems, voice recognition and low end imaging, providing both large buffer size

and high processing power. Various 32-bit timers, single or dual 10-bit ADC(s),

10-bit DAC, PWM channels and 45 fast GPIO lines with up to nine edge or level

sensitive external interrupt pins make these microcontrollers particularly suitable

for industrial control and medical systems.

32

FEATURES

16/32-bit ARM7TDMI-S microcontroller in a tiny LQFP64 package.

8 to 40 kB of on-chip static RAM and 32 to 512 kB of on-chip flash

program memory. 128 bit wide interface/accelerator enables high speed 60 MHz

operation.

In-System/In-Application Programming (ISP/IAP) via on-chip boot-loader

software. Single flash sector or full chip erase in 400 ms and programming of 256

bytes in 1 ms.

EmbeddedICE RT and Embedded Trace interfaces offer real-time

debugging with the on-chip RealMonitor software and high speed tracing of

instruction execution.

33

USB 2.0 Full Speed compliant Device Controller with 2 kB of endpoint

RAM. In addition, the LPC2146/8 provide 8 kB of on-chip RAM accessible to

USB by DMA.

One or two (LPC2141/2 vs. LPC2144/6/8) 10-bit A/D converters provide a

total of 6/14 analog inputs, with conversion times as low as 2.44 μs per channel.

Single 10-bit D/A converter provides variable analog output.

Two 32-bit timers/external event counters (with four capture and four

compare channels each), PWM unit (six outputs) and watchdog.

Low power real-time clock with independent power and dedicated 32 kHz

clock input.

Multiple serial interfaces including two UARTs (16C550), two Fast I2C-bus

(400 kbit/s), SPI and SSP with buffering and variable data length capabilities.

Vectored interrupt controller with configurable priorities and vector

addresses.

Up to 45 of 5 V tolerant fast general purpose I/O pins in a tiny LQFP64

package.

Up to nine edge or level sensitive external interrupt pins available.

60 MHz maximum CPU clock available from programmable on-chip PLL

with settling time of 100 μs.

On-chip integrated oscillator operates with an external crystal in range from

1 MHz to 30 MHz and with an external oscillator up to 50 MHz.

Power saving modes include Idle and Power-down.

Individual enable/disable of peripheral functions as well as peripheral clock

scaling for additional power optimization.

Processor wake-up from Power-down mode via external interrupt, USB,

Brown-Out Detect (BOD) or Real-Time Clock (RTC).

34

Single power supply chip with Power-On Reset (POR) and BOD circuits: –

CPU operating voltage range of 3.0 V to 3.6 V (3.3 V ± 10 %) with 5 V tolerant

I/O pads.

APPLICATIONS

Industrial control.

Medical systems.

Access control.

Point-of-sale.

Communication gateway.

Embedded soft modem.

General purpose applications.

ARM7TDMI-S PROCESSOR

The ARM7TDMI-S is a general purpose 32-bit microprocessor, which offers

high performance and very low power consumption. The ARM architecture is

based on Reduced Instruction Set Computer (RISC) principles, and the instruction

set and related decode mechanism are much simpler than those of

microprogrammed Complex Instruction Set Computers. This simplicity results in a

high instruction throughput and impressive real-time interrupt response from a

small and cost-effective processor core. Pipeline techniques are employed so that

all parts of the processing and memory systems can operate continuously.

Typically, while one instruction is being executed, its successor is being

decoded, and a third instruction is being fetched from memory. The ARM7TDMI-

S processor also employs a unique architectural strategy known as THUMB, which

35

makes it ideally suited to high-volume applications with memory restrictions, or

applications where code density is an issue.

The key idea behind THUMB is that of a super-reduced instruction set.

Essentially, the ARM7TDMI-S processor has two instruction sets:

The standard 32-bit ARM instruction set.

A 16-bit THUMB instruction set.

The THUMB set’s 16-bit instruction length allows it to approach twice the

density of standard ARM code while retaining most of the ARM’s performance

advantage over a traditional 16-bit processor using 16-bit registers. This is possible

because THUMB code operates on the same 32-bit register set as ARM code.

THUMB code is able to provide up to 65% of the code size of ARM, and 160% of

the performance of an equivalent ARM processor connected to a 16-bit memory

system. The ARM7TDMI-S processor is described in detail in the ARM7TDMI-S

Datasheet that can be found on official ARM website.

ON-CHIP FLASH MEMORY SYSTEM

The LPC2141/2/4/6/8 incorporate a 32 kB, 64 kB, 128 kB, 256 kB, and 512

kB Flash memory system, respectively. This memory may be used for both code

and data storage. Programming of the Flash memory may be accomplished in

several ways: over the serial built-in JTAG interface, using In System

Programming (ISP) and UART0, or by means of In Application Programming

(IAP) capabilities. The application program, using the IAP functions, may also

erase and/or program the Flash while the application is running, allowing a great

degree of flexibility for data storage field firmware upgrades, etc. When the

LPC2141/2/4/6/8 on-chip bootloader is used, 32 kB, 64 kB, 128 kB, 256 kB, add

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500 kB of Flash memory is available for user code. The LPC2141/2/4/6/8 Flash

memory provides minimum of 100,000 erase/write cycles and 20 years of data-

retention.

ON-CHIP STATIC RAM (SRAM)

On-chip Static RAM (SRAM) may be used for code and/or data storage. The

on-chip SRAM may be accessed as 8-bits, 16-bits, and 32-bits. The

LPC2141/2/4/6/8 provide 8/16/32 kB of static RAM, respectively.

The LPC2141/2/4/6/8 SRAM is designed to be accessed as a byte-addressed

memory. Word and halfword accesses to the memory ignore the alignment of the

address and access the naturally-aligned value that is addressed (so a memory

access ignores address bits 0 and 1 for word accesses, and ignores bit 0 for

halfword accesses). Therefore valid reads and writes require data accessed as

halfwords to originate from addresses with address line 0 being 0 (addresses

ending with 0, 2, 4, 6, 8, A, C, and E in hexadecimal notation) and data accessed as

words to originate from addresses with address lines 0 and 1 being 0 (addresses

ending with 0, 4, 8, and C in hexadecimal notation). This rule applies to both off

and on-chip memory usage. The SRAM controller incorporates a write-back buffer

in order to prevent CPU stalls during back-to-back writes. The write-back buffer

always holds the last data sent by software to the SRAM. This data is only written

to the SRAM when another write is requested by software (the data is only written

to the SRAM when software does another write).

If a chip reset occurs, actual SRAM contents will not reflect the most recent

write request. Any software that checks SRAM contents after reset must take this

into account. Two identical writes to a location guarantee that the data will be

present after a Reset. Alternatively, a dummy write operation before entering idle

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or power-down mode will similarly guarantee that the last data written will be

present in SRAM after a subsequent reset.

PIN CONFIGURATION

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DESCRIPTION

The pin connect block allows selected pins of the microcontroller to have

more than one function. Configuration registers control the multiplexers to allow

connection between the pin and the on chip peripherals. Peripherals should be

connected to the appropriate pins prior to being activated, and prior to any related

interrupt(s) being enabled. Selection of a single function on a port pin completely

excludes all other functions otherwise available on the same pin. The only partial

exception from the above rule of exclusion is the case of inputs to the A/D

converter. Regardless of the function that is selected for the port pin that also hosts

the A/D input, this A/D input can be read at any time and variations of the voltage

level on this pin will be reflected in the A/D readings. However, valid analog

reading(s) can be obtained if and only if the function of an analog input is selected.

Only in this case proper interface circuit is active in between the physical pin and

the A/D module.

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