The document discusses neuro-electric prostheses and brain-machine interfaces (BMIs), detailing various neural interfaces including brain-computer interfaces, peripheral nerve interfaces, and targeted muscle reinnervation to control prosthetic devices. It outlines the signal processing involved in BMIs, such as filtering, amplifying, and digitizing neural signals, and describes advancements in neuro-prostheses that leverage these technologies for improved functionality. Additionally, the document introduces key examples of prosthetic innovations, including robotic arms and the Esper hand, which use advanced algorithms to respond to neural commands.

1. NEURO-ELECTRIC PROSTHESIS
Presented by
FIONA VERMA
Masters in Prosthetics & Orthotics
2023-2024

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CONTENTS
2
S.No Topic Slide no
1. Introduction 3-5
2.
2.1
Brain-machine interface neuro-prosthetic devices
Review of literature
6
7
3.
3.1
Neural signal recordings
Non-invasive recordings
8
9 & 10
4.
4.1
4.2
4.3
Signal processing and prosthetic control
Filtering, amplifying and digitizing
Processing and decoding
Controlling prosthetic actuators
11 & 12
13 &14
15
16
5. Current advance sin Neuro-prostheses 17 & 18
6. References 19
7. Thank you 20

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1. INTRODUCTION
Neural-Machine Control Interfaces:
A neural–machine interface that can provide motor commands to operate the prosthesis and
serve as a conduit to provide sensory feedback that is needed.
Three types of neural interfaces are currently being investigated:
(1) Brain– machine interfaces / Brain-computer interfaces
(2) Peripheral nerve interfaces
(3) Targeted reinnervation (TMR)
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1 . I N T R O D U C T I O N ( C O N T. . )
4
Peripheral nerve interface: The Utah slant array (inset) records
activity from individual nerve fascicles of a peripheral nerve. It is
implanted into the muscle adjacent to the nerves, as shown in the
image in the lower right quadrant of the figure. The slanted tips
ensure that nerve fascicles at different depths of the nerves are
probed. These signals may then be transmitted to a controller,
which in turn controls a prosthesis.
Brain computer interface: brain-computer interface (BCI).
Commonly used non-invasive modality to record brain signals is
electroencephalography (EEG). EEG signals are deciphered to
control commands in order to restore communication between the
brain and the output device when the natural communication
channel i.e., neuronal activity is disrupted.

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1. INTRODUCTION (CONT..)
Targeted muscle reinnervation interfaces:
TMR transfers residual nerves from an
amputated limb onto alternative muscle
groups that are not biomechanically
functional due to the amputation. The target
muscles are denervated prior to the nerve
transfer. The reinnervated muscle then
serves as a biological amplifier of the
amputated nerve motor commands.
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2. BRAIN-MACHINE INTERFACE NEURO-PROSTHETIC
DEVICES
• The advent of digital signal processing and high-speed computation has opened the
possibility of restoring lost upper-extremity function through brain-machine interface
(BMI) neuroprosthetic devices.
• By capturing brain activity, predicting underlying intentions from that activity, and
controlling a prosthetic device accordingly.
• Presently, BMI devices can guide robotic arms and, in some cases, activate paralyzed
extremities with volitional control in a limited way in a laboratory environment.
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Below table show come of the major accomplishments made by groups investigating brain-machine interfaces
across the past 40 years.
7
2.1 REVIEW OF LITERATURE

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3. NEURAL SIGNAL RECORDINGS
Across the past several decades, several recording modalities have been experimented from non-
invasive to invasive deep-brain methods, each has prevalence in the realm of neuroprosthetic devices.
8
Fig 4: Relative invasiveness of various portable neural recording modalities.

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3 . 1 N O N - I N VA S I V E R E C O R D I N G
Noninvasive recording modalities require minimal to no alteration of the person to record brain
activity and can be readily acceptable to any person as the risk of complications are low.
Electroencephalography is a non-invasive recording technique that measures the
electromagnetic byproducts of brain activity instantaneously. As neurons in the brain fire and
signals traverse axons and dendrites, small amplitude electric currents are generated along with
coupled magnetic fields, which are also weak in magnitude
Thousands to millions of neurons become active in synchrony which superimpose larger-
magnitude magnetic fields.
These larger-magnitude magnetic fields generate small electrical currents on the scalp, which is
measured by EEG.
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3.1 NON-INVASIVE R ECORDING (CONT.)
• EEG signals are recorded using metallic electrodes several millimeters to centimeters in
diameter that make electrically conductive connections with the scalp.
• Due to the simplicity of the EEG cap, EEG systems are lightweight, safe to equip, and
portable enough for use with a Neuro-prosthesis.
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4. SIGNAL PROCESSING AND PROSTHETIC CONTROL
Brain-machine interfaces have not yet translated to full-time use because of several inherent issues
with experimental systems. Below, we discuss the minimum required components of a BMI.
Key Components of the system:
 the tissue interface directly acquire electrophysiological signals from the brain.
These voltage signals must be filtered and then amplified so they can be converted into digital
signals.
This allows general computing hardware to perform computations on those measurements
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4. SIGNAL PROCESSING AND PROSTHETIC CONTROL (CONT.)
Further in chain of computations:
1) Feature extraction:
2) Decoder:
3) Actuate prosthesis:
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4.1 FILTERING, AMPLIFYING AND DIGITIZING
Filtering:
• Typically referred to as the analog front-end (AFE) , experimental AFEs first filter and
amplify the incoming neural signals.
• The first filter in the chain is a band-pass filter that eliminates frequencies outside of those
relevant to brain activity.
• Typical BMI, AFEs s inherently filter out frequencies below approximately 0.1Hz and above
approximately 10 kHz.
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4 . 1 FI LT E R I N G , A M PL I FY I N G A N D D I G I T I Z I N G
Amplifying:
• In addition to reducing noise, front-end filters also amplify their neural signal inputs.
• Amplification is necessary to bring the neural signals, which are typically on the order of tens to
hundreds of microvolts in amplitude, to voltages at which the downstream electronic circuits
generally operate, which are on the order of volts
Analog-to-digital converter (ADC):
• The ADC receives the filtered and amplified analog neural signal and periodically converts it to a
digital one for use with computers.
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4.2 PROCESSING AND DECODING
• Once neural signals are digitized, BMIs employ computational algorithms that convert the
recorded brain activity into a person’s intentions.
• These algorithms are called decoders as they decode the encoded neural information into a
more usable control signal.
• The encoded information for intra-cortical recordings is often some representation of each
electrode’s spiking or firing rate, broadly called features in the machine learning
community, which are extracted
• The different effectors that can be controlled by these neural features which include robotic
arms or neuro-prosthesis.
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4.3 CONTROLLING PROSTHETIC ACTUATORS
The final stage of a brain-machine interface is the prosthesis. The prosthesis can take many
forms: a surrogate arm and hand or a tool, such as a computer or a speaker, that provides some
special functionality to the user.
16
Fig 9: A research team from the University of
Houston has created an algorithm that allowed a
man to grasp a bottle and other objects with a
prosthetic hand, powered only by his thoughts

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4.3 CONTROLLING PROSTHETIC ACTUATORS(CONT.)
• There are a handful of robotic hands that could fit brain-machine interface applications:
Modular Prosthetic Limb (Johns Hopkins University)
LUKE Arm , Mobius Bionics
I-Limb, Ossur
TASKA Hand, TASKA Prosthetics
BeBionic Hand, Ottobock Healthcare
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5. ADVANCES IN NEURO-PROSTHESIS
Robotic Prosthetic arm by Johns Hopkins
University Researchers:
First, the patient’s neurosurgeon placed an array of
128 electrode sensors—all on a single rectangular
sheet of film the size of a credit card—on the part
of the man’s brain that normally controls hand and
arm movements.
After the motor and sensory data were collected,
the researchers programmed the prosthetic arm to
move corresponding fingers based on which part of
the brain was active.
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5. ADVANCES IN NEURO-PROSTHESIS (CONT.)
Esper Hand by Esper Bionics:
• New York-based engineering startup Esper
Bionics has developed a prosthetic arm with
intuitive self-learning technology.
• Esper Hand uses an electromyography-based
brain-computer interface (BCI) – a computer-
based technology system that gathers brain
activity or information – to trigger movement.
• Esper Hand has five movable digits and can
rotate and grip in multiple ways.
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THANK YOU