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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_145_библиотеки_им_акад_М_И_Перельмана
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Brain Computer Interaction Disease Prediction using Machine Learning 133
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specific aspects, but it is inadequate in more general situations. An algorithm
automatically extracts representative features as the result. Because most machine
learning research focuses on static data, it is unable to reliably classify constantly
evolving brain signals [6, 7]. BCI systems need to use novel learning methods to
deal with dynamic data streams [8]. The benefit of deep learning is
backpropagating. By back-propagating, it learns distinguishable information by
utilizing the raw brain signals without the need to pre-process and engineer
features. Furthermore, deep neural networks may use deep structures to capture
latent dependencies and representative high-level features [9].
COMPONENTS OF BRAIN COMPUTER INTERFACE
The primary objective of a Brain-Computer Interface is to detect and analyze the
signals in the user's brain that indicate user intention and then transmit these
signals to an external computer that executes the user instruction. A BCI-based
system has four components that work together to achieve this objective as shown
in Fig. (1).
Fig. (1). Components of BCI.
Signal Acquisition
It is the first component that detects and analyses brain signals. The purpose of
this component is to receive and register signals produced by neuronal action. It
also transfers the resulting signals to the next part of the BCI component for
signal enhancement and to reduce the noise [10].
134 Disease Prediction using Machine Learning M. Kiruthiga Devi
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Number of BCI related paper
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Number of BCI
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Feature Extraction
It is the first step in the signal processing of the Brain Computer Interface. Digital
signals (such as signals corresponding to user intentions) are processed by
analyzing different characteristics of the signals and translating them into
acceptable commands for user output.
Translation
In this stage of signal processing, the extracted characters of the signal are passed
to a translation algorithm. It converts the characters into valid instructions that are
sent to an external system in order to fulfil the purpose (e.g. the user's intention is
converted into complete instructions).
Application/Device Output
A translation algorithm provides instructions for driving and controlling an output
device. It helps users achieve their objectives such as selecting alphabets,
controlling a mouse, moving a robotic arm, using a wheelchair, moving a
paralyzed limb, etc. [11].
Fig. (2). Number of BCI related paper publication over the year.
BCI CHARACTERISTICS
BCI Systems are Classified according to how they use the Brain: Active BCI
This mechanism derives its effects from the brain's voluntary programmed
movements which are independent of external stimuli. In order to manage an
application such as BCI, it is activated by a person's deliberate motor imagery
[12].
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Reactive BCI: It is a device that uses brain signals which are formed as a result
of response to a stimulant. It is used to monitor an application by the consumer by
P300.
Passive BCI: Detecting drowsiness in drivers and thus preventing road traffic
accidents is possible through methods that utilize unintended emotional/cognitive
brain functioning.
Signal Acquisition Modalities have been used to Classify Structures as
Invasive or Noninvasive BCI
The most important component of a BCI-based device is the ability to measure
brain-generated oscillations. It represents the user's current voluntary neural
behavior and various signal acquisition techniques have been investigated. By
using the BCI application, you can determine which acquisition method and
calculations are appropriate for your signals [13].
Invasive Techniques
Electrocorticography (ECoG) analyzes the neuronal activity of the brain either
intra-cortical from within the nervous system or on the surface of the cortex. The
main benefit is that they have a spatial resolution and high temporal, by which the
signal-to-noise ratio and efficiency of the signal are improved. These methods
have a number of flaws. Aside from concerns regarding the usability of surgical
procedures, an issue with output has also been raised. One of them is the limited
size of the tracked brain regions by such implants. They can't be moved to test
brain activity in another part of the brain once they've been implanted. During a
surgical procedure, electrode strips or grids are implanted over the surface of the
cortex as shown in Fig. (3). ECoG recording falls somewhere in the middle
between non-invasive security and invasiveness. It has a higher spatial resolution
and signal amplitude than non-invasive techniques like EEG because of its
proximity to the signal source. ECoG brain signals were used to discern right vs
left hand and finger vs tongue imagery movement for both non-paralyzed and
paralyzed epileptic patients and were also used to calculate kinematic parameters
for five-finger flexion.
The most invasive approach is the Intracortical acquisition technique. It is
implanted under the brain's cortex surface. It can be done with an array of
electrodes or a single electrode that monitors individual neuron action of the
signal. Since the electrode tips are located close to the signal source, the arrays
must be stable in excess of time. Its use in source localization problems is highly
recommended due to its high spatial resolution. Intracortical acquisition, on the
other hand, can experience signal variability over time. Rats and monkeys have
136 Disease Prediction using Machine Learning M. Kiruthiga Devi
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been heavily involved in BCI research that uses intracortical invasive acquisition
as shown in Fig. (4). Animals with implanted electrodes were used to study
movement. In a study aimed at reducing the number of electrodes used, monkeys
first learned to transfer a cursor into 8 targets positioned at the imaginary cube
corners. Adaptive movement prediction algorithms were trained from the
extracted data to assess movement intentions. In virtual reality, monkeys were
used to manoeuvre a brain-controlled robot arm [14]. Researchers have even
succeeded in using a real robot arm to assist them in eating. Amyotrophic lateral
sclerosis (ALS) is a neurodegenerative condition connected to nerve cells in the
brain and spinal cord. Motor neurons run throughout the body, from the brain to
the spinal cord and from the spinal cord to the muscles [15]. After implanting a
single electrode in the motor cortex, a patient with ALS was able to shift a cursor
on a computer screen to pick presented objects.
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Fig. (3). Electrocorticography.
Output
communication
Visual feedback
Input communication
Signal
processing
Target selection Task
Decode
neural data
Real-time
task control
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Recording
neurons
time
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Sensory
Feedback
Q
KCGY J
SIND
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UO R L
ZBFYPX
Output
Device
Fig. (4). Intracortical.
Non-Invasive Techniques
These methods of recording are based on a process that does not involve the
implantation of foreign items into the subject's brain. As a result, it eliminates the
need for invasive acquisition procedures or permanent implant attachment.
Magnetoencephalography (MEG) is used for detecting magnetic fields generated
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by the electrical current in the brain. The superconducting quantum interference
system is currently used to collect the outside head magnetic signals. A shield and
specialized equipment are necessary for the laboratory to prevent MEG signals
from interfering with other magnetic signals, such as the earth's magnetic field as
shown in Fig. (5). The skull layer is less skewed by MEG signals than electric
fields, despite their portability and cost concerns. As compared to non-invasive
electronic acquiring strategies, this advantage does not result in significant
improvements in efficiency or training times.
Fig. (5). Magnetoencephalography.
Functional Magnetic Resonance Imaging (fMRI)
The fMRI senses changes in the flow of blood that are connected to the neuronal
activities in the brain. So, it aids in mapping behaviors to the corresponding
regions of the brain, a process known as source localization. Any activity
involving the brain causes an increase in blood flow to the area (Fig. 6). The
BOLD contrast method is sensitive to hemodynamic responses. Differences in
deoxyhemoglobin concentration in brain tissue can be seen as BOLD contrast
intensities [16]. It has a low temporal resolution, but it has a high spatial
resolution and can collect data from the deep parts of the brain that magnetic or
electrical methods cannot.
Fig. (6). Functional magnetic resonance imaging (fMRI).
138 Disease Prediction using Machine Learning M. Kiruthiga Devi
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Functional Near-Infrared Spectroscopy (fNIRS)
The fNIRS is a non-invasive method for detecting neuronal activity by measuring
the flow of blood in the brain. It determines the flow of blood using light in the
near-infrared range. It has the advantage of high spatial resolution for a signal.
The fNIRS recording is expected to be less accurate in terms of temporal
resolution than recording based on electromagnetic signals. It has the advantage
of delivering signals with a high spatial resolution. Its benefits make it a viable
option for practical use and clinical trials.
Electroencephalogram (EEG)
Electroencephalography (EEG) monitors voltage changes that follow
neurotransmission activity within the brain to record electrical activity in the
scalp. Commercial use is possible because of its distinct advantages in usability
over other methods of recording brain signals. It is easy to use, compact, and
affordable. High temporal resolution is also provided by EEG recording [16]. The
signal-to-noise ratio of this approach is lower than that of other approaches, and
its spatial resolution is also lower. To improve EEG signal localization and spatial
resolution many solutions have been proposed [17]. It has been proposed that the
number of electrodes is increased to 256. It has been announced that there is a
global electrode positioning scheme. According to the assignment, the distance
between adjacent pairs of electrodes should be ten percent or twenty percent of
the scalp diameter. There are a variety of EEG systems that use sensors like
NeuroSky that are less obtrusive and have a high portability choice for
widespread consumer use. It has been proven feasible when measured against on
scalp EEG. The benefit of this method is that it fixes electrode location, provides
comfort to the user, and is resistant to electromagnetic interference. Research into
BCI-based applications has focused on reducing the number of electrodes used
while maintaining the signal-to-noise ratio.
BCIs convert direct measurements of brain activity into commands and controls.
The most modern BCI uses electroencephalogram (EEG) to record one of three
types of signals: P300, event-related desynchronization/synchronization
(ERD/ERS), Steady-State Visual Evoked Potential (SSVEP).
P300-Based BCI
The assessment of brain responses to particular sensory, cognitive, or motor
events is known as event-dependent potentials (ERPs). The P300 is a significant
peak and one of the most commonly used event-related potential elements. An
optimistic peak in the EEG can be seen 300 milliseconds after the stimulus is
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presented in an oddball paradigm. Visual, auditory and sensory stimulation may
be used. The P300 part of ERP refers to the evoked response in the EEG.
Properties of P300
The occipital area of the brain has the highest spatial amplitude distribution,
which is symmetric around the central location. The 10-20 international method
was used to record Cz. P300 is normally reported as electrodes and the amplitude
allocation of 10-20 international systems.P300 wave amplitude is usually 2 to 5 V
with a period of 150 to 200 msec in terms of temporal pattern. From Fig. (7),
given the low amplitude of P300 in comparison to brain background activities (50
V), it is obvious that P300 finding necessitates unusual signal processing. Various
applications have utilized the P300-based BCI from wheelchair navigation to
spelling words on the virtual keyboard and communicating with machines. BCI
systems like this kind have the ability to change people's lives.
NASION
Fp1
Fp2
Fig. (7). P300.
F7
A1
T3
T5
C3 Cz
P3
O1
INION
FzF3
Pz
F8
F4
A2
T4
C4
P4
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O2
The study of electrophysiology and neurophysiology has shown that neurons
respond to stimuli by increasing activity. Sudden visual stimuli elicit visually
evoked potential, and repeated visual stimuli result in a stable voltage oscillations
pattern in the EEG recognized as Steady-State Visually Evoked Potentials
(SSVEP). SSVEP occurs directly in the primary visual cortex, according to
Ragan. According to Silberstein, the SSVEP can contain indirect cortical
responses from the peripheral retina during cognitive tasks. Continuous visual
areas of the cortex are stimulated by a stimulus pattern that repeats continuously
on the central retina in an SSVEP. It is a sinusoidal oscillating waveform, and its
fundamental frequency is similar to the stimulus. In response to a visual stimulus
offered at a rate between 3.5 Hz and 75 Hz, the brain induces electrical activity at
the different and same frequencies. Using this type of stimulation can help
140 Disease Prediction using Machine Learning M. Kiruthiga Devi
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diagnose visual imperceptions, neurological anomalies in schizophrenia patients,
multifocal sensitivity loss in multiple sclerosis patients, and other psychiatric
diagnoses.
Electroencephalogram (EEG) using Convolutional Classifier
A consistent EEG signal and an effective classifier are more essential in a
simplified BCI system than in a multiple-channel system. Independent component
analysis (ICA) outperforms the conventional band-pass filter in terms of
separating fusion signals into their additive independent components [18]. With
the recent development of the classification methods (e.g., machine learning and
deep learning) in the BCI area, conventional classification methods (i.e., linear
discrimination analysis and support vector machine) explore the good output. In
BCI fields, researchers are attempting to replace conventional classifiers (e.g.
support vector machines) with deep learning methods [19]. We extracted function
and time-domain features via the wavelet transform (WT) algorithm. The CNN
algorithm was used to classify the various mental signals. Researchers used
optimized algorithms (e.g., FastICA, WT, and CNN) to detect hybrid MI and
SSVEP signals from the same source. This system is capable of detecting three
different EEG forms with a single CNN model, in comparison to simple hybrid
simultaneous BCI systems, using a simpler BCI structure (e.g., two channels) for
multiple commands (e.g., ten comments). An easier headset structure could serve
as an everyday control device for people with disabilities using BCI wireless
commands to accomplish everyday tasks. In order to avoid complicated direct
pre-treatment and input of photos, CNN commonly includes many paired
convolution max-pooling layers and a fully connected layer as the output.
There are as many neurons as input data in a general neural network; too much
data would prevent efficient training [20]. The number of linked neurons in a
general neural network is equal to the input data. For CNN to reduce dimensions,
it performs local field and parameter sharing, and two-dimensional filters are
applied to input images in the convolution layer, after which they are re-sampled
to produce a smaller size. In equation (1), consider matrix (x
column is paired with the WT algorithm. W
represents the weight of the mth row
m,n
) with ith row and j
i,j
and the nth column, and Wb represents the weight of the mth row and the nth column
in the filter. The activation function (i.e., f) is the function ReLU). The input is the
time-frequency-based function. The network has four layers, each of which
contains one or more neurons.
(𝒊, 𝒋) = (∑
𝒎=𝟎 ∑𝒏=𝟎
𝑾𝒎, 𝒏 𝑿 𝒊 + 𝒎, 𝒋 + 𝒏) + 𝒘𝒃
(1)
th
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We obtained a 3-dimensional feature matrix as a consequence of WT (time,
frequency and amplitude). Layer 1 of a CNN model will include all data sets with
different labels once all datasets have been gathered. A convolution layer and a
pooling layer are the first layers in the time domain, and the dynamic function is
added to all the data as input.
The convolution filter size is 5*5 with the padding, and each move is equal to 1.
The max-pooling layer is used with a filter size of 2*5. The system uses the maxpooling with a size of the filter 2*5 shown in Fig. (8). As a result, after processing
Layer1, the matrix will be reduced to 4*700/2*700 = 2, 800 or 1, 400 neurons.
The completely linked layer, which is made up of 280 or 140 neurons, is the third
secret layer [21]. The ten neurons that reflect the ten commands make the fourth
secret layer, which is also the completely connected layer. The dataset is further
divided into single features (SSVEP/MI) and hybrid features
(SSVEP/MI+SSVEP) to prove the benefit of the simultaneous hybrid MI +
SSVEP method. The CNN model is trained with different signals and their results
are compared [22]. Based on the results that showed a loss, the hybrid system
performed better than the single system. Accuracy increased to varying degrees as
well. The hybrid system was therefore the best option for a multi-command
system that could be operated simultaneously. Simultaneous hybrid signals could
be applied for more orders in order to overcome the constraint of mental tasks and
time frame issues. A sequential hybrid system requires 2-time windows to execute
one instruction, while the simultaneous hybrid mode reduces the time window by
1/2. In addition, unlike the conventional hybrid system [23]. In addition, the
device could be used to perform synchronized recognition of MI mental tasks,
hybrid MI-SSVEP tasks, and SSVEP mental tasks.
Fig. (8). The four-layer convolution neural network classifier structure.
INPUT
5*5
CONVOLUTION
2*2
SUBSAMPLING
1*10
CONVOLUTION
FEATURE EXTRACTION
2*2
SUBSAMPLING
0
1
CLASSIFICATION
8
9
142 Disease Prediction using Machine Learning M. Kiruthiga Devi
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CHALLENGES
Usability challenges: BCI is described as having difficulties gaining user
acceptance. Class discrimination preparation is an issue that concerns them [24].
System efficiency and acceptance are measured by information transfer rate (ITR)
[25].
Training Process
In terms of training a user, one has to direct the user through the processor or
record multiple sessions. It occurs either during the introduction process or during
the calibration phase of the classifier. In the preliminary phase, the individual is
taught how to use the device and manipulate brain input signals, while in the
calibration phase, the signal of a trained subject is used to learn the used classifier.
One of the most widely studied solutions to this time-consuming problem is to use
single-trial analysis instead of multi-trial analysis, which is used to improve
signal-to-noise ratio and to place the burden of small training size on subsequent
BCI device components to manage. As alternatives, a zero-training classifier and
a variety of adaptive have been investigated.
Information Transfer Rate
Command-based BCI systems are evaluated using Information transfer rate.
Based on the number of options available, target detection accuracy, and the
average amount of time necessary to make a decision, this statistic is determined.
Because there are so many options available, selective attention techniques have a
higher ITR than imagery-based BCI.
Technical Challenges
There are problems with the electrophysiological data collected. Nonlinearity, for
example, is a property of brain signals.
Non-Linearity
Dynamic nonlinear behavior of groups of neurons is observed in the brain, which
is a highly dynamic system. Nonlinear dynamic methods can, therefore, better
describe EEG signals than linear methods.
Non-Stationary and Noise
A BCI system faces the problem of non-stationary electrophysiological brain
signals. The results in a continuous shift in the signals used over time, either
between or inside recording sessions. EEG signal variability may be influenced by
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