This document summarizes a presentation on brain-machine interfaces. It begins by defining a brain-machine interface as a direct communication link between the brain and external devices. It then outlines the main components of a BMI system, including neural signal acquisition from the brain, signal processing algorithms to extract commands, and using those commands to control external devices with feedback. Challenges discussed include the complexity of the brain, weak signal strengths, and ethical concerns about thought control and memory modification. The future of BMI is predicted to include thought-based communication devices and advanced cyborg technologies.
4. Brain machine interface has several name like direct
neural interface , brain–computer interface and it is a
direct communication link between a brain and the
outside world.
BMI uses brain activity to command, control, activate
and communicate with the world by using peripheral
devices and systems.
UNDERSTANDING
BMI
5. The field of BMI has emerged in
neuroprosthetics applications that aim at
damaged hearing, sight and physical challenged.
7. • Main principle behind this interface is
the bioelectrical activity of nerves and
muscles.
• Brain is composed of millions of
neurons.
• When the neuron activates, there is a
voltage change across the cell which
generates signals on the surface of the
brain.
• By monitoring and analyzing these
signals we can understand the
working of brain.
8. Neural Interface
Neural Signals
Signal Processing
Algorithms/Command Extraction
Control
Command
Vehicle State Signal
Sensors
Environmental Feedback
Directio
nal
control
9. BLOCK DIAGRAM
IMPLANT DEVICE
SIGNAL PROCESSING SECTION
EXTERNAL DEVICE
FEEDBACK SECTION
Multichannel Acquisition Systems
Spike Detection
Signal Analysis
10. ELECTRO ENCEPHALOGRAPHY (EEG) IS
MEASUREMENT OF ELECTRICAL ACTIVITY
PRODUCED BY BRAIN AS RECORDED FROM
ELECTRODES PLACED ON THE SCALP.
11. IMPLANT DEVICE
• The EEG is measured with electrodes,
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.
12. Multichannel Acquisition Systems
At this section of amplification, initial filtering
of EEG signal and sending these signal from
instrument into a computer.
Spike Detection
Spike detection will allow the BMI to transmit
only the action potential waveforms and their
respective arrival times instead of the low
signal, raw signal .
Signal Analysis
In this stage, digitized EEG signal which are
input to the classifier.
Classifier recognize different mental tasks.
SIGNAL PROCESSING SECTION
13. EXTERNAL DEVICE
The classifier’s output is the input for the device control.
The device control simply transforms the classification to a
particular action.
Examples are robotic arm, thought controlled wheel chair etc.
FEEDBACK DEVICE
Feedback is needed for learning and for control.
Real-time feedback can dramatically improve the performance
of a brain–machine interface.
classifier
14. 1.Auditory and visual prosthetics
2.Functional-neuromuscular stimulation (FNS)
3.Prosthetics limb control
16. CHALLENGES
Permanent damage to brain.
Virus attack on brain
Thought control and prediction of future thoughts.
Deletion or recording of memories.
LIMITATIONS
The brain is incredibly complex.
The signals are weak and interference can happen.
There are chemical processes Involved as well, which
electrodes can’t pick up.
17. Thought-communication device.
Super intelligent machines- Cyborgs.
• New research has demonstrated that it is possible
for communication from person to person through
the power of thought alone.
• A Cyborg is a Cybernetic Organism, human part
machine.
• This will mean that robots, not humans, make all the
important decisions .This may bring serious effects for
humankind.