The document presents an overview of Brain-Computer Interface (BCI), discussing its definition, brain wave basics, and the various stages of BCI, including data acquisition methods like EEG, MEG, and fMRI. It outlines pre-processing techniques for artifact removal, feature extraction through methods like Autoregressive Moving Average (ARMA), and feature classification for command generation. The applications of BCI are extensive, ranging from basic communication aids to controlling artificial limbs and sensory devices for individuals with disabilities.

1. K. S. RANGASAMY COLLEGE
OF TECHNOLOGY
BRAIN COMPUTER INTERFACE
Presented by
Jai Dersheni S

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

3. INTRODUCTION OF BCI
A communication channel connecting the
brain to a computer or another electronic
device.
Two basic requirements are
 Features that are useful to distinguish
several kinds of brain state
 Methods for the detection and
classification of such features
implemented in real time.

4. 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

5. STAGES OF BCI

6. DATA ACQUISITION METHODS

7. 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).

8. 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

9. 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).

10. PRE-PROCESSING
TECHNIQUES(ARTIFACT REMOVAL)

11. 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.

12. FEATURE EXTRACTION

13. 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)

14. FEATURE CLASSIFICATION

15. 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

16. DEVICE CONTROL

17. 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.