This document discusses brain-computer interfaces (BCIs) for controlling cursor movements. It provides background on BCIs, describing them as direct communication pathways between the brain and external devices that allow manipulating computers with thoughts. It then covers the basic components of a BCI system, including data acquisition, signal processing/classification, and applications. Regarding controlling cursor movements, it proposes a hybrid BCI combining P300 detection for vertical movement and motor imagery signals for horizontal movement. Technical and usability challenges are also discussed.

1. Brain - Computer Interface for Controlling
Cursor Movements -A Review

2. ❖ BCI is a direct communication pathway between an
enhanced or wired brain and external device which
allow you to manipulate computers and machinery
with your thoughts.
❖ BCI has given many names mind machine interface (MMI), a direct neural
interface and brain-machine interface (BMI).
2
What is Brain-Computer interface

3. ❖ BCI measure and use the signals produced by the
central nervous system
❖ Not a voice activated or muscle activated
communication system
❖ BCIs are not mind reading devices
3
What is Brain-Computer Interface

4. ❖ In 1924 Berger was the first to record human brain activity by means of
EEG.
❖ In 1964 Dr. Grey Walter placed electrodes through the human
❖ After years of experiment on animal the first BCI was implemented in
humans in mid 90s.
❖ In December of 2004, Dr Jonathan Wolpaw and his group published a study
that shows the ability to control a computer by using BCI.
❖ In 2013 researchers successfully connected the brains of two rats with
electronic interfaces.
4
What is Brain-Computer Interface

5. 5
Basic Component
BCI is consisting of three phases
❖ Data Acquisition
❖ Signal Processing and Classification.
❖ Application.

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Basic Component

7. Data Acquisition
7
Process of measuring the voltage of electrical signals
that will generate by the brain.
Types of BCI
❖ Invasive
Inside grey matter
❖ Partially invasive
Outside the gray matter
❖ Non-invasive
On the scalp

8. Noninvasive BCI
8
❖ Most popular type
❖ Electroencephalogram (EEG), Magnetoencephalography (MEG),
Magnetic Resonance Imaging (MRI), and Functional Magnetic
Resonance Imaging (fMRI), Position Emission Tomography (PET).

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EEG: A recording of the electrical activity of the brain from the scalp. The
recorded waveforms reflect the cortical electrical activity.
Components of an EEG system.
● Electrodes
● Amplifiers
● Analog to Digital Converter
● Recording Device
EEG

10. Pre-processing
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❖ Aim of enhancing the signal to noise ratio is called as pre-processing
❖ It is an essential step
Techniques
● Independent Component Analysis
● Principle Component analysis
● Adaptive Filtering
● Surface Laplacian
● Common Spatial Patterns

11. Feature Extraction
11
Feature extraction transforms raw signals into more informative data
Techniques
● ICA : Independent Component Analysis
● PCA : Principal Component Analysis
● WT : Wavelet Transform
● AR : Autoregressive Modeling
● WPD : Wavelet packet decomposition
● FFT : Fast Fourier Transform

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Independent Component Analysis
Computational method for separating a multivariate signal into additive
subcomponents.
Assume that the subcomponents are statistically independent from each
other.

13. 13
Principle Component Analysis
Technique used to emphasize variation and bring out strong patterns in a
dataset. It's often used to make data easy to explore and visualize.
Identifying a smaller number of uncorrelated variables, called "principal
components", from a large set of data

14. 14
Classification
● Linear Classifiers
linear discriminant analysis (LDA)
support vector machine (SVM)
● Nonlinear Bayesian
● Neural Network
● K-Nearest Neighbor Classifier

15. BCI Applications
15
❖ Allow paralyzed people to control prosthetic limbs with their minds
❖ Allow gamers to control video games with their minds
❖ Allow a mute person to have their thoughts displayed and spoken by
a computer
❖ Transmit visual images to the mind of a blind person allowing them to
see
❖ Transmit auditory data to the mind of a deaf person, allowing them to
hear.

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BCI Applications

17. 17
BCI For controlling cursor movements
❖ EEG signals in noninvasive method
❖ Low signal to noise ratio
❖ Difficult to identify two independent signals with respect to the
vertical and horizontal

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Hybrid BCI
Specific area of BCIs which has been made by combining different type of
conventional BCI systems.
Two types
● Sequential
● Simultaneous

19. Hybrid BCI for 2D asynchronous cursor control
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● Vertical movement P300 Detection
● Horizontal movement Motor imaginary signals

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Vertical movement
Feature extraction
❖ Filtering between 0.1 and 20Hz.
❖ Single vector which represent flash buttons (Down sampling +
Concatenation).
❖ Obtain trained support vector machine model ( single vector +
feature vector of trained data set).

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Vertical movement
Classification
Eight scores denoted as Dx
Let assume
Highest HD = max {D1, · · ·, D8}
Second Highest SHD = max {{D1, · · ·, D8} {Dx0}}
if 1-SHD/HD> µ0
P300 classifier output = {1,-1,0} 1 ->down , -1 ->UP , 0-> stop
else
No output

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Vertical movement
Calculation
ΔZ(i) = C(i)V
ΔZ(i): Amount that the cursor need to move at ith update
C(i): Result of the P300 classifier
V : Positive speed constant

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Pre-processing
❖ Spatial filtering of common average reference
❖ Brand-pass filtering in between 8-13 Hz
❖ Spatial filtering
Horizontal movement

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Calculation
U(x+1)=U(x)+(c )/3 (p(x-2)+p(x-1)+p(x))+d
U(x) : Horizontal coordinate of the cursor’s current position
p(x) : Support vector machine score
c, d : Constants
calculation of p(x) uses EEG signals preprocessed in most recent 1200ms
Horizontal movement

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Discussion
Technical challenges and Usability challenges
Technical challenges
Strength of the acquired signals
Performance of feature extraction and classification techniques
Usability challenges
Training the user

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❖ Information transfer rate
❖ How accurately target will be detected
❖ The average time that the BCI will take to select the target
Performance measurement

27. Thank You