Download Annotated Bibliography Ferdinando A. Mussa

Annotated Bibliography
Ferdinando A. Mussa-Ivaldi and Lee E. Miller. “Brain–Machine Interfaces:
Computational Demands and Clinical Needs Meet Basic Neuroscience.” TRENDS in Neuroscience,
Vol.26 No.6 June 2003, pp. 329-334.
This review focuses on the following challenges: establishing a ‘closed-loop’ interaction between
sensory input and motor output, and controlling neural plasticity to achieve the desired behavior
of the Brain-Machine Interface (BMI) system. The authors discuss many of the different clinical
applications of the BMI system. Many of these applications utilize electroencephalography
(EEG), which is a measurement of electrical activity produced by the brain that is recorded from
electrodes. The electrodes are placed on the subject’s scalp; thus, these applications are noninvasive. One group of researchers used the amplitude of the µ–rhythm, oscillations detected
during actual and imagined movements by the brain, and trained their subjects to control the
position of a cursor on a computer monitor. However, the EEG-based methods are limited by a
low information rate, 20-30 bits per minute. As a result, more invasive methods, based on
extracellular recordings by microelectrodes were pursued by researchers. In one such experiment,
researchers used monkeys as subjects and implanted electrodes into the region of their brain
believed to participate in movement planning, the posterior partial cortex. Then, monkey
subjected to this experiment had to reach towards one of two targets on a touch screen. A
probabilistic algorithm was designed to predict the preferred target based on the neuron signals
discharged during the period of delay before the monkey made any movement. Within 50 trials,
the monkeys were able to regulate this discharge and indicate the intended target without
movement. The authors stress the need for real-time feedback in order to improve the
performance of the BMI. The human brain uses two feedback mechanisms: (1) online control
and correction of errors during the execution of movement, and (2) learning: gradual adaptation
of motor commands, after the execution of one or two movements. The authors discuss how
closed-loop interaction between nerve cells and external devices allow for the possibility of
combining synthetic properties of neural tissue with the adaptive control of neural devices.
Vision is crucial to the guidance of long-term adaptation of movement. Monkeys trained to
control a computer cursor were able to use their visual system to adapt their brain to control the
cursor movement. In addition, a control algorithm was ‘co-adapted’ to accommodate for changes
in directional properties of the recorded neurons. In the course of a session, the directional tuning
of a set of neurons seemed to settle into a stable arrangement, which suggests these changes were
adaptive, occurring as the nervous system switched between direct control(by the control
algorithm) and brain control. The authors also investigated the possibility of using the feedback
from a BMI in order to bring about controlled synthetic changes at specific synapses. In their
experiment, light signals were generated by two optical sensors of the robot. These signals were
translated into electrical stimuli and were applied to netlike neurons in the vestibular pathways of
the brain, those responsible for balance of the body. The neurons reacted to these stimuli and
discharged a frequency, which was used in order to control the movement of the robot’s wheels.
Gerhard M. Friehs, Vasilios A. Zerris, Catherine L. Ojakangas, Mathew R. Fellows and John P.
Donoghue. “Brain-Machine and Brain-Computer Interfaces.” Stroke: Journal of the American Heart
Association, Dallas, Texas, Oct. 14, 2004, pp. 2702-2705.
This literature review gives a summary of the components required for a successful BMI and also
describes the authors’ prototype BMI system. Motor BMIs consist of at least three modules: data
acquisition module, data interpretation module and data output module. In the following we
briefly discuss each module in turn.
The data acquisition module extracts electrical signals from the brain with sufficient bandwidth.
The authors describe how EEG signals can be obtained either non-invasively or invasively.
When obtaining signals non-invasively, electrodes are mounted on the subject’s scalp. The
signals obtained represent only a field of potential rather than specific cellular activity. Noninvasive methods, however, limit the rate at which information, and thus the patient’s intent, is
extractable from the EEG signals. This delay may be sufficient for tasks such as moving a cursor;
however, it is insufficient for controlling artificial limbs, for which detailed movement
trajectories must be supplied. Invasive methods implant electrodes into the brain in order to
obtain signals. Recent research has shown that alternative and multiple locations may produce
the best signals, as opposed to early conjectures that the motor cortex was the optimal location.
Some of the alternative sites include: parietal cortex, premotor cortex, frontoparietal cortices, and
the motor cortex and premotor cortex simultaneously.
The data interpretation module processes the digitized brain signals obtained from the data
acquisition module and translates them into code that best represents the desired output action.
Some of the desired movements for motor prosthetics include: movement of a cursor, clicking a
button, and specification of complex time-varying trajectories, such as reaching for an object.
Continuous movements, such as following an object with a hand, can be represented via a threedimensional coordinate system. This module allows for distinct states of limb movement, such as
reaching and grasping, to be distinguishable. The ability of the system to identify a user’s intent
helps reduce the decoding error. BMIs are now capable of accurately predicting movement for
90% of trials in real time or within only milliseconds delay. In addition, patients are able to
improve the decoding the system’s interpretation of intent through practice. The data output
mode uses the translated output data to run any number of output devices.
Gerwin Schalk, Dennis J. McFarland, Thilo Hinterberger, Niels Birbaumer, and Jonathan R.Wolpaw.
“BCI2000: A General-Purpose Brain-Computer Interface (BCI) System.” IEEE Transactions On
Biomedical Engineering 1, Albany, New York, June 25, 2003, pp. 1-10.
This report gives a general review of current BCI technology. The authors describe how most
existing BCI studies evaluated the possibility of using brain signals to record external devices.
These systems measure the specific features of brain activity, from either brain signals recorded
from the scalp via EEG or from within the brain, and translate them into device control signals.
Current systems however cannot reach a processing rate of more than 25 bits per minute even in
optimal conditions. To address this short coming, the authors developed the BCI2000 system, a
general purpose BCI research and development platform. The BCI2000 system is based on a
model that can describe any BCI system. It has four modules that communicate freely with each
other: source, signal processing, user application, and operator interface. The source module
digitizes and stores brain signals and passes them on to the signal processing module. The signal
processing module extracts signal features off the digitized signals. Then, a translation algorithm
translates these signal features into control signals, which are transferred to the user application
module. The application module uses these control signals to run an application. The BCI2000
platform has been used to create seven applications: four cursor movement applications, one
application for evaluating prospective users, one application that can present user-selectable
auditory and visual stimuli, and one spelling application. This platform can also be used to
control neuroprosthesis that provide hand closure and potentially much more. The operation
module defines the system parameters and the onset and offset operations. It can display
information sent by any other module, thus delivering real-time information about events
occurring online. The BCI2000 supports any programming language, any development
environment, and any operating system. In order to minimize dependencies between modules,
the BCI2000 modules use a generic protocol that can transmit all the information necessary for
any operation. It acquires/processes brain signals and responds in real-time. In addition, the
BCI2000 platform offers offline analyses of data that is gathered by researchers during online
operation. This platform defines three types of system variables: parameters, event markers, and
signals. Parameters do not change throughout a data file during a specified period of online
operation. Event markers record event details that occur during online operation. They can
change from one data sample to the next, unlike parameters. In addition, every module has
access to the event markers and can modify them accordingly. Signal variables are the functions
of the user’s brain signals that are received; signals can be modified by any of the modules.
Jonathan R. Wolpaw, Niels Birbaumer, Dennis J. McFarland, Gert Pfurtscheller, and Theresa M.
Vaughan. “Brain-Computer Interfaces for Communication and Control.” Clinical Neurophysiology 113,
Albany, New York, March 2002, pp. 767-791.
This review defines the BCI and its components and also summarizes the state-of-the-art BCI
systems. Electroencephalographic (EEG) activity provides commands to the BCI and is translated
into output actions. The BCI system is viewed as a communication system in which an
individual’s commands to the external world do not pass through the brain’s normal output
pathways of peripheral nerves and muscles. The authors describe two classes of BCIs:
independent and dependent. An independent BCI does not depend on the brain’s regular output
pathways. The generation of the EEG signal depends mainly on the user’s intent. An independent
BCI provides the brain with new output pathways. On the other hand, a dependent BCI does not
use the brain’s regular output pathways to carry the signals; however, activity in these pathways
is necessary to generate brain activity that will carry these signals. For example, if a user stares at
the letter on the screen that he wishes to select this visual stimulus activates the appropriate
pathways for the signal to travel. A BCI, like the human brain, depends largely on feedback and
on adaptation of the brain’s activity based on the feedback received. The authors highlight five
parts of the BCI: signal acquition, signal processing: feature extraction, signal processing:
translation algorithm, output device, and operation protocol.
Several state-of-the-art BCIs currently in use are described in this paper. One of the BCI systems
made use of visual evoked potentials (VEP) of the brain by means of a VEP recorded from the
scalp in order to determine the visual fixation point. This determined the direction the user
desired to move the cursor on a computer. Another BCI made use of slow cortical potentials
(SCP), slow voltage changes generated in the cortex. Negative SCPs are associated with
movement, while positive SCPs are usually associated with reduced cortical activation. Studies
showed that subjects learned to control SCPs in order to control the movement of objects on a
computer screen. Another BCI used P300 evoked potentials, which refers to the peak of 300 ms
reached in the EEG when infrequent auditory, visual or somatosensory (of or relating to the
perception of sensory stimuli from the skin and internal organs) mixed together with routine
stimuli. The BCI uses the P300 to identify a user’s intent, because the P300 is prominent only in
responses elicited by the desired choice. This method, unlike the previous two, requires non
initial user training. The final BCI method discussed uses µand β rhythms, oscillation
measurements representing 8-12 Hz and 18-26 Hz of EEG activity respectively, of the
sensorimotor cortex. Movements or thoughts of movements cause a decrease in mu and beta
rhythms, named ‘event-related de-synchronization’ (ERD).
Mikhail A. Lebedev, Roy E. Crist, and Miguel A. L. Nicolelis. “Building Brain-Machine Interfaces to
Restore Neurological Functions.” Methods for Neural Ensemble Recordings, Second Edition. CRC Press
LLC, Second Edition, Durham, North Carolina, Dec. 3, 2007, pp. 219-233.
The authors give a general summary of the breakthroughs in BMI technology up to date. The
first breakthrough in using neural activity to control prosthetic devices was made by Chapin and
researchers. They recorded the neural activity of multiple brain areas of rats, which controlled
the one dimensional movement of a robotic arm. The importance of this breakthrough was that
the rats could control the robotic arm without any explicit movement of their own body parts.
Most research with BMI technology has been performed on the rhesus monkey. By implanting
single neuron-recordings into these monkeys researchers were able to have the monkeys
reproduce arm reaching trajectories, such as reaching and grasping. The authors describe the
features that distinguish BMIs, some of which include: the number of electrode arrays implanted,
cortical sites implanted, characteristic of neural signal methods, and sizes of neuronal population
sampled. A BMI processes neural signals in four stages: (1) an electrode implanted in the brain
records high-quality signals emitted by neurons—firing patterns, (2) motor control signals are
extracted from the recorded signals, (3) control signals enact motor behaviors of prosthetic
devices, and (4) feedback is sent back to the subject’s brain so that errors in performance can be
corrected. The authors describe many of the problems facing current BMI technology, including:
stability of neural recordings, development of 3D electrode matrices, working with a large
population of neurons, development of next generation algorithms, and biocompatibility. Their
vision of a functional implantation of a BMI technology is a fully implantable device that will be
able to amplify a large number of neuronal signals, which will be equipped with a wireless link
that enables communication with external actuators.
The authors give a summary of some of the algorithms used for processing neural activity in BMI
systems. They describe how BMI computational algorithms predict motor parameters from the
patterns of neuron firing. Research has shown that BMIs in animals are capable of inducing a
relationship between neuron firing patterns and the movement of an actuator (prosthetic limb).
The authors describe two types of algorithms developed for the BMI: one that decodes motor
parameters (decoding muscle activation signals to directly stimulate the muscles of paralyzed
individuals who cannot activate them voluntarily) and one that decodes cognitive signals
(decoding intended reach direction during delay periods before a subject’s movement). They
present a shared-control model, which makes use of both decoding methods by extracting highorder signals from the brain and sending them to a low-level controller that uses “artificial reflexlike circuits” in order to improve the accuracy of the movement. The paper concludes with a
short description of the concept of brain plasticity involved in BMI operations. This concept
encompasses the idea that long-term usage of a prosthetic limb directly controlled by the brain
will cause remapping within important area of the brain, resulting in areas of the brain
representing prosthetic devices as if they were a natural body appendage.