Introduction to Brain Computer Interface

Introduction to
Brain Computer
Interface

Technology beyond the Dreams™ Copyright © 2006 Pantech Solutions Pvt Ltd.
Brain Computer Interface
• Communication using Thoughts of Brain (EEG)
without using any Muscle control, Especially for
Severely Paralyzed people

BCI defined
• A BCI generally consists of three main
components:
1. A Signal-acquisition module (Electrode,Filtering and
Amplifier)
2. A Signal-processing module(ASIC ,DSP)
3. A Control module ( Microcontroller &Driver)

BCI Defined (Continued)
BCI Types:
– Invasive
– Involves attaching electrodes directly to the
brain tissue
– The patient’s brain gradually adapts its
signals to be sent through the electrodes
– Non-invasive
Involves putting electrodes on the scalp of the
patient, and taking readings

Three BCI Subtypes
• Active BCI: “An active BCI is a BCI which derives its outputs from brain activity
which is directly consciously controlled by the user, independently from external
events, for controlling an application.”
• Reactive BCI: “A reactive BCI is a BCI which derives its outputs from brain activity
arising in reaction to external stimulation, which is indirectly modulated by the
user for controlling an application.”
• Passive BCI: “A passive BCI is a BCI which derives its outputs from arbitrary brain
activity without the purpose of voluntary control, for enriching a human-computer
interaction with implicit information.”

Introduction to EEG
• Electroencephalography (EEG) is
the recording of electrical activity
along the scalp. EEG measures
voltage fluctuations resulting from
ionic current flows within the neurons
of the brain
• German physiologist and psychiatrist
Hans Berger (1873–1941) recorded
the first human EEG in 1924

Anatomy

Functional Mapping

Volume Conduction

Why is BCI Hard

Why is BCI Hard?
• Signal-to-noise ratio is very challenging, so sensitive measures are hard to
obtain
• –Relevant brain activity is small compared to interfering artifacts and compared
to brain background activity
• Specific measures are even harder to obtain (with coarse-grained sensing)
• Large collections of neurons are involved in many different activities, not just
one
• Underlying phenomena are also highly diverse and rich and derived measures
are still poorly understood – not always clear what to look for

Reason for Variability

Consequences
• Sophisticated signal processing is required
• BCI systems must be calibrated before they
can be used
• Calibration should entail as much information as
available, e.g., example data, prior knowledge,
large databases

Underlying Neural Process
• Largest contributors to the EEG are the pyramidal cells
• Radially oriented in the cortex (orthogonal to the surface)
• Electromagnetic fields of co-aligned and co-activated
neurons add up
• EEG, MEG and ECoG can only detect large-scale neural
dynamics , For example, 50.000 neurons firing in near-
synchrony

When would 50,000 neurons fire
near-synchronously?
• –An external event triggers a cascade of related
neural processes (e.g., in perception)
• –An internal event triggers a cascade of related
neural processes (e.g., sudden “aha!”)
• –Neural populations enter a synchronized steady-
state firing pattern (e.g., idle oscillations)
• Event-Related Potentials (ERPs) and Oscillatory
Processes are the two major BCI-detectable EEG

Signal Detectability
• Root cause might not be
directly observable (e.g.,
dopaminergic system, deep
brain structures, few neurons)
• Electromagnetic fields can
cancel each other out

Electrode Placement

Types of Electrodes

EEG Sensor Designs

Electrode Position

10-10 Electrode System

Signal Range

Comparison of EEG Bands

Available Tools
• Opensig
• BCI2000
• EEGLAB
• Open VIBE
• BCILAB

Communication and Control for the
Severely disabled

Neurospeller

Operator Monitoring

Forensics-LIE Detection

Entertainment

Health

NeuroMarketing
• Impact of Advt
,movies is measured
by a sample of
audiences Before the
release of an Advt or
Movie

Neuroscience

Mindwave Mobile vs Mindwave
BRAIN SENSOR
EAR CLIP
BATTERY
TGAM MODULE

HEADSET PLACEMENT

FEATURES
• Single Sensor on FP1
• Can detect multiple mental states simultaneously
• Reference electrode on Ear clip to remove ambient
noises
• IP involves noise cancellation and signal
amplification
• Provides EMG feature for Eye Blink detection

SPECIFICATIONS
• Bluetooth ™ Wireless communication
• Passive Dry Sensor EEG
• Single AAA battery
• 10 Hours Run Time
eSense Brainwave Patterns:
• Attention
• Meditation
• Eye Blink
• Frequency Bands: 0.5 to 50Hz

THINKGEAR
•TGAM MODULE offers e-Sense technology
•Outputs Attention, Meditation and Physical eye blinks from raw
EEG data
•2.79cm x 1.52cm x 0.25cm, weighs 130mg
•Communicates through UART interface
at baudrate 1200, 9600 or 57600

SUPPORTED PLATFORMS

MINDBOT
SPECIFICATIONS:
• Micro Controller : 8051
• ZIGBEE MODULE-Frequency : 2.3 Ghz
• Attention level < 65 ------ STOP
• Attention level > 65 ------ START
• Baud rates available at 1200, 9600 ,57600 or 115200

MINDBOT
BLUETOOTH UART
UART
ZIGBEE MODULE
SENSES ATTENTION
LEVEL
BLUETOOTH RFCOMM
SERIAL PORT
ZIGBEE MODULE
8051 CONTROLLER WHEEL CONTROL
BOT
uses ‘SERIAL’ function
TX COMMAND
HEADSET
RX COMMAND
CONTROLS MOTOR BASED ON COMMAND

For more details
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Introduction to Brain Computer Interface

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