K. S. RANGASAMY COLLEGE OF TECHNOLOGY

BRAIN COMPUTER INTERFACE

Presented by

Jai Dersheni S

AGENDA Introduction to BCI Brain Waves Basics Stages of BCI Data Acquisition methods Pre-processing Techniques Feature extraction and classification Applications

INTRODUCTION OF BCI A communication channel connecting the

brain to a computer or another electronic device.

Two basic requirements areFeatures that are useful to distinguish

several kinds of brain stateMethods for the detection and

classification of such features implemented in real time.

BRAINWAVE FREQUENCY

Frequency band

Frequency Physiological role

Beta ( ) 12-30 Hz Working/alert

Alpha ( ) 8-12 Hz Relaxing

Theta ( ) 4- 8 Hz Ideal , meditation

Delta ( ) 1-4 Hz Deep sleep with dream

sub - Delta < 1Hz Deep sleep without dream

STAGES OF BCI

DATA ACQUISITION METHODS

DATA ACQUISITION METHODS

EEG, ElectroEncephaloGraphy :recording the electrical field generated by

action potentials of neurons using small metal electrodes.

MEG, magneto encephalography :directly measures the cortical magnetic

fields produced by electric current fMRI, functional Magnetic Resonance

Imaging :provides information on brain metabolism

using BOLD (Blood Oxygen Level Dependent).

EEG

•Consists of a electrode cap of simple covering the cortex of the brain on the scalp.•Requires neither professional training nor the personnel to apply it

ARTIFACTS

The signals coming from electrodes connected to the brain range from 0Hz and upwards. Quality vary due to artifacts.Artifacts:

Brain signals are contaminated by artifacts.These artifacts range from bioelectrical potentials produced by movement of body parts like,eyes, tongue, arms,fluctuation in skin resistance (sweating).

PRE-PROCESSING TECHNIQUES(ARTIFACT REMOVAL)

PRE-PROCESSING TECHNIQUES(ARTIFACT REMOVAL)

At first using ICA algorithm extract Independent components (ICs) separated then GA select the best and related ICs among the whole ICs.ICs are represented as a binary string of d elements, in which a 0 in the string indicates that the corresponding IC is to be omitted, and a 1 that it is to be included.

FEATURE EXTRACTION

FEATURE EXTRACTION

• It is the process of selecting appropriate features from the input data

• It can be done using autoregressive moving average (ARMA) model

• The notation ARMA(p, q) refers to the model with p autoregressive terms q moving average terms. This model contains the AR(p) and MA(q) models,

AR frequency analysis which gives higher resolution than fast Fourier transform (FFT)

FEATURE CLASSIFICATION

FEATURE CLASSIFICATION It is the process of identifying the opt input

feature for system command generation It acquiring the commands from the user

thought. It can be achieved by Linear Vector

Quantization (LVQ)• By this the correlation of the extracted

feature to the sample data signal is typically done implicitly by classifying the feature vector using Neural Network

DEVICE CONTROL

APPLICATION The list of possible applications of BCI is practically

endless It range from simple decision programs to

manipulation of the environment,

from spelling programs

to controlling the systems Example of BCI spelling program Other Applications:

Artificial limbs control.Artificial leg control.Artificial vision for blinds.Artificial hear sense for deaf.