Advances in Neural Signal Processing
6
Quantitative models of machine learning algorithms provide incredibly powerful
implementations in neuroscience. Some traditional methods such as LDA, PCA, and
support vector machine (SVM) are also regarded as machine learning algorithms. Other
algorithms (neural networks, autoencoders, and logistic regression) train batches of
input data using basis transformation function to match the output adaptively.
4. Applications of neural signal processing
Neuroprostheses, neurostimulators, or human-machine interfaces are devices
that record from or stimulate the brain to help individuals with neurological disorders, restore their lost function, and thereby improve their quality of life. Neural
signal processing methodologies are used extensively in all these applications.
4.1 Neurostimulators
Neurostimulators that have demonstrated decades of success are cochlear
implants that are designed for those who have dysfunctional conduction of sound
waves from the eardrum to the cochlea. These implants can also help elderly
individuals who have age-related hearing loss [4]. There is an external speech
processor to capture and convert the sound from the surrounded environment to
digital signals. The internal implants turn the digital signals into electrical signals
to stimulate the hearing nerve by the electrodes inside the cochlea. Once the brain
receives the signals, one can hear and interpret the sound.
Another successful neurostimulator is the deep brain stimulator (DBS) system used for individuals with Parkinson’s disease. The DBS has been available
as a reliable treatment for decades for individuals with Parkinson’s disease. The
implanted impulse generator placed under the collarbone provides continuous
electrical impulses by giving a certain frequency of stimulation to the subthalamic
nucleus and makes it possible to minimize the uncontrolled tremors. During the
DBS surgery, electrodes are inserted into a targeted area of the brain, and the whole
procedure is monitored and recorded using MRI. After the treatment, symptomatic
improvement was durable for at least 10 years [5].
4.2 Neuroprostheses or human-machine interfaces (HMIs)
Stroke, spinal cord injury, and traumatic brain injury may lead to long-term
disability, and an increased number of individuals are suffering from severe motor
impairments, resulting in loss of independence in their daily life. Recovery of motor
function is crucial in order to perform activities of daily living. Human-machine
interfaces (HMIs) can enable dexterous control of exoskeletons that could be
used as a rehabilitative device or an assistive device to restore lost motor functions
poststroke or spinal cord lesions [6–8], thus promoting long-lasting improvements
in motor function of individuals with movement disorders.
Additionally, significant applications in neural engineering are HMI-based
systems to restore or compensate the lost limb functions for individuals with amputation or paralysis. Cortical control of prosthetics has been studied both in animals
[9] and humans [10, 11]. Movement-related cortical potentials used to assess cortical activation patterns provide interesting information, as they are associated with
the planning and execution of voluntary movements. Recently, HMI-based research
has stressed on the development of algorithms for movement decoding using noninvasive neural recordings [12–14]. In order to understand neural intent before or
during the movement, it is necessary to extract the characteristics accurately using
6
Quantitative models of machine learning algorithms provide incredibly powerful
implementations in neuroscience. Some traditional methods such as LDA, PCA, and
support vector machine (SVM) are also regarded as machine learning algorithms. Other
algorithms (neural networks, autoencoders, and logistic regression) train batches of
input data using basis transformation function to match the output adaptively.
4. Applications of neural signal processing
Neuroprostheses, neurostimulators, or human-machine interfaces are devices
that record from or stimulate the brain to help individuals with neurological disorders, restore their lost function, and thereby improve their quality of life. Neural
signal processing methodologies are used extensively in all these applications.
4.1 Neurostimulators
Neurostimulators that have demonstrated decades of success are cochlear
implants that are designed for those who have dysfunctional conduction of sound
waves from the eardrum to the cochlea. These implants can also help elderly
individuals who have age-related hearing loss [4]. There is an external speech
processor to capture and convert the sound from the surrounded environment to
digital signals. The internal implants turn the digital signals into electrical signals
to stimulate the hearing nerve by the electrodes inside the cochlea. Once the brain
receives the signals, one can hear and interpret the sound.
Another successful neurostimulator is the deep brain stimulator (DBS) system used for individuals with Parkinson’s disease. The DBS has been available
as a reliable treatment for decades for individuals with Parkinson’s disease. The
implanted impulse generator placed under the collarbone provides continuous
electrical impulses by giving a certain frequency of stimulation to the subthalamic
nucleus and makes it possible to minimize the uncontrolled tremors. During the
DBS surgery, electrodes are inserted into a targeted area of the brain, and the whole
procedure is monitored and recorded using MRI. After the treatment, symptomatic
improvement was durable for at least 10 years [5].
4.2 Neuroprostheses or human-machine interfaces (HMIs)
Stroke, spinal cord injury, and traumatic brain injury may lead to long-term
disability, and an increased number of individuals are suffering from severe motor
impairments, resulting in loss of independence in their daily life. Recovery of motor
function is crucial in order to perform activities of daily living. Human-machine
interfaces (HMIs) can enable dexterous control of exoskeletons that could be
used as a rehabilitative device or an assistive device to restore lost motor functions
poststroke or spinal cord lesions [6–8], thus promoting long-lasting improvements
in motor function of individuals with movement disorders.
Additionally, significant applications in neural engineering are HMI-based
systems to restore or compensate the lost limb functions for individuals with amputation or paralysis. Cortical control of prosthetics has been studied both in animals
[9] and humans [10, 11]. Movement-related cortical potentials used to assess cortical activation patterns provide interesting information, as they are associated with
the planning and execution of voluntary movements. Recently, HMI-based research
has stressed on the development of algorithms for movement decoding using noninvasive neural recordings [12–14]. In order to understand neural intent before or
during the movement, it is necessary to extract the characteristics accurately using