121 research outputs found
Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications
This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments
Spectrochemical analysis in blood plasma combined with subsequent chemometrics for fibromyalgia detection
Fibromyalgia is a rheumatologic condition characterized by multiple and chronic body pain, and other typical symptoms such as intense fatigue, anxiety and depression. It is a very complex disease where treatment is often made by non-medicated alternatives in order to alleviate symptoms and improve the patient’s quality of life. Herein, we propose a method to detect patients with fibromyalgia (n = 252, 126 controls and 126 patients with fibromyalgia) through the analysis of their blood plasma using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy in conjunction with chemometric techniques, hence, providing a low-cost, fast and accurate diagnostic approach. Different chemometric algorithms were tested to classify the spectral data; genetic algorithm with linear discriminant analysis (GA-LDA) achieved the best diagnostic results with a sensitivity of 89.5% in an external test set. The GA-LDA model identified 24 spectral wavenumbers responsible for class separation; amongst these, the Amide II (1,545 cm−1) and proteins (1,425 cm−1) were identified to be discriminant features. These results reinforce the potential of ATR-FTIR spectroscopy with multivariate analysis as a new tool to screen and detect patients with fibromyalgia in a fast, low-cost, non-destructive and minimally invasive fashion
Applications of brain imaging methods in driving behaviour research
Applications of neuroimaging methods have substantially contributed to the
scientific understanding of human factors during driving by providing a deeper
insight into the neuro-cognitive aspects of driver brain. This has been
achieved by conducting simulated (and occasionally, field) driving experiments
while collecting driver brain signals of certain types. Here, this sector of
studies is comprehensively reviewed at both macro and micro scales. Different
themes of neuroimaging driving behaviour research are identified and the
findings within each theme are synthesised. The surveyed literature has
reported on applications of four major brain imaging methods. These include
Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG),
Functional Near-Infrared Spectroscopy (fNIRS) and Magnetoencephalography (MEG),
with the first two being the most common methods in this domain. While
collecting driver fMRI signal has been particularly instrumental in studying
neural correlates of intoxicated driving (e.g. alcohol or cannabis) or
distracted driving, the EEG method has been predominantly utilised in relation
to the efforts aiming at development of automatic fatigue/drowsiness detection
systems, a topic to which the literature on neuro-ergonomics of driving
particularly has shown a spike of interest within the last few years. The
survey also reveals that topics such as driver brain activity in semi-automated
settings or the brain activity of drivers with brain injuries or chronic
neurological conditions have by contrast been investigated to a very limited
extent. Further, potential topics in relation to driving behaviour are
identified that could benefit from the adoption of neuroimaging methods in
future studies
Signal Processing Using Non-invasive Physiological Sensors
Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions
Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review
Non-oncologic chronic pain is a common high-morbidity impairment worldwide and
acknowledged as a condition with significant incidence on quality of life. Pain intensity is largely
perceived as a subjective experience, what makes challenging its objective measurement. However,
the physiological traces of pain make possible its correlation with vital signs, such as heart rate
variability, skin conductance, electromyogram, etc., or health performance metrics derived from daily
activity monitoring or facial expressions, which can be acquired with diverse sensor technologies
and multisensory approaches. As the assessment and management of pain are essential issues
for a wide range of clinical disorders and treatments, this paper reviews different sensor-based
approaches applied to the objective evaluation of non-oncological chronic pain. The space of available
technologies and resources aimed at pain assessment represent a diversified set of alternatives that
can be exploited to address the multidimensional nature of pain.Ministerio de Economía y Competitividad (Instituto de Salud Carlos III) PI15/00306Junta de Andalucía PIN-0394-2017Unión Europea "FRAIL
An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works
Schizophrenia (SZ) is a mental disorder that typically emerges in late
adolescence or early adulthood. It reduces the life expectancy of patients by
15 years. Abnormal behavior, perception of emotions, social relationships, and
reality perception are among its most significant symptoms. Past studies have
revealed the temporal and anterior lobes of hippocampus regions of brain get
affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and
decreased volume of white and gray matter can be observed due to this disease.
The magnetic resonance imaging (MRI) is the popular neuroimaging technique used
to explore structural/functional brain abnormalities in SZ disorder owing to
its high spatial resolution. Various artificial intelligence (AI) techniques
have been employed with advanced image/signal processing methods to obtain
accurate diagnosis of SZ. This paper presents a comprehensive overview of
studies conducted on automated diagnosis of SZ using MRI modalities. Main
findings, various challenges, and future works in developing the automated SZ
detection are described in this paper
Automatic Diagnosis of Schizophrenia and Attention Deficit Hyperactivity Disorder in rs-fMRI Modality using Convolutional Autoencoder Model and Interval Type-2 Fuzzy Regression
Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy
Comparison of EEG based epilepsy diagnosis using neural networks and wavelet transform
Epilepsy is one of the common neurological disorders characterized by
recurrent and uncontrollable seizures, which seriously affect the life of
patients. In many cases, electroencephalograms signal can provide important
physiological information about the activity of the human brain which can be
used to diagnose epilepsy. However, visual inspection of a large number of
electroencephalogram signals is very time-consuming and can often lead to
inconsistencies in physicians' diagnoses. Quantification of abnormalities in
brain signals can indicate brain conditions and pathology so the
electroencephalogram (EEG) signal plays a key role in the diagnosis of
epilepsy. In this article, an attempt has been made to create a single
instruction for diagnosing epilepsy, which consists of two steps. In the first
step, a low-pass filter was used to preprocess the data and three separate
mid-pass filters for different frequency bands and a multilayer neural network
were designed. In the second step, the wavelet transform technique was used to
process data. In particular, this paper proposes a multilayer perceptron neural
network classifier for the diagnosis of epilepsy, that requires normal data and
epilepsy data for education, but this classifier can recognize normal
disorders, epilepsy, and even other disorders taught in educational examples.
Also, the value of using electroencephalogram signal has been evaluated in two
ways: using wavelet transform and non-using wavelet transform. Finally, the
evaluation results indicate a relatively uniform impact factor on the use or
non-use of wavelet transform on the improvement of epilepsy data functions, but
in the end, it was shown that the use of perceptron multilayer neural network
can provide a higher accuracy coefficient for experts.Comment: 8 pages, 4 tables, 3 figure
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