75 research outputs found
A Study of Deep Learning Robustness Against Computation Failures
For many types of integrated circuits, accepting larger failure rates in
computations can be used to improve energy efficiency. We study the performance
of faulty implementations of certain deep neural networks based on pessimistic
and optimistic models of the effect of hardware faults. After identifying the
impact of hyperparameters such as the number of layers on robustness, we study
the ability of the network to compensate for computational failures through an
increase of the network size. We show that some networks can achieve equivalent
performance under faulty implementations, and quantify the required increase in
computational complexity
Alternative Techniques of Neural Signal Processing in Neuroengineering
Neural signal processing is a discipline within neuroengineering. This interdisciplinary approach combines principles from machine learning, signal processing theory, and computational neuroscience applied to problems in basic and clinical neuroscience. The ultimate goal of neuroengineering is a technological revolution, where machines would interact in real time with the brain. Machines and brains could interface, enabling normal function in cases of injury or disease, brain monitoring, and/or medical rehabilitation of brain disorders.
Much current research in neuroengineering is focused on understanding the coding and processing of information in the sensory and motor systems, quantifying how this processing is altered in the pathological state, and how it can be manipulated through interactions with artificial devices including brain–computer interfaces and neuroprosthetics
EEG Windowed statitical wavelet deviation for estimation of muscular artifacts
Electroencephalographic (EEG) recordings are, most of
the times, corrupted by spurious artifacts, which should be
rejected or cleaned by the practitioner. As human scalp EEG
screening is error-prone, automatic artifact detection is an issue
of capital importance, to ensure objective and reliable results.
In this paper we propose a new approach for discrimination
of muscular activity in the human scalp quantitative
EEG (QEEG), based on the time-frequency shape analysis.
The impact of the muscular activity on the EEG can be evaluated
from this methodology. We present an application of
this scoring as a preprocessing step for EEG signal analysis,
in order to evaluate the amount of muscular activity for two
set of EEG recordings for dementia patients with early stage
of Alzheimer’s disease and control age-matched subjects
Coherency and sharpness measures by using ICA algorithms. An investigation for Alzheimer’s disease discrimination
In this paper, we present a comprehensive study of different Independent Component Analysis (ICA) algorithms
for the calculation of coherency and sharpness of electroencephalogram (EEG) signals, in order to
investigate the possibility of early detection of Alzheimer’s disease (AD). We found that ICA algorithms can
help in the artifact rejection and noise reduction, improving the discriminative property of features in high frequency
bands (specially in high alpha and beta ranges). In addition to different ICA algorithms, the optimum
number of selected components is investigated, in order to help decision processes for future works
A Theta-Band EEG Based Index for Early Diagnosis of Alzheimer’s Disease
Despite recent advances, early diagnosis of Alzheimer’s disease (AD) from electroencephalography (EEG) remains
a difficult task. In this paper, we offer an added measure through which such early diagnoses can potentially be improved. One
feature that has been used for discriminative classification is changes in EEG synchrony. So far, only the decrease of synchrony
in the higher frequencies has been deeply analyzed. In this paper, we investigate the increase of synchrony found in narrow
frequency ranges within the θ band. This particular increase of synchrony is used with the well-known decrease of synchrony
in the band to enhance detectable differences between AD patients and healthy subjects. We propose a new synchrony ratio
that maximizes the differences between two populations. The ratio is tested using two different data sets, one of them containing
mild cognitive impairment patients and healthy subjects, and another one, containing mild AD patients and healthy subjects.
The results presented in this paper show that classification rate is improved, and the statistical difference between AD patients
and healthy subjects is increased using the proposed ratio
ICA Cleaning procedure for EEG signals analysis: application to Alzheimer's disease detection
To develop systems in order to detect Alzheimer’s disease we want to use EEG signals. Available database
is raw, so the first step must be to clean signals properly. We propose a new way of ICA cleaning on a
database recorded from patients with Alzheimer's disease (mildAD, early stage). Two researchers visually
inspected all the signals (EEG channels), and each recording's least corrupted (artefact-clean) continuous 20
sec interval were chosen for the analysis. Each trial was then decomposed using ICA. Sources were ordered
using a kurtosis measure, and the researchers cleared up to seven sources per trial corresponding to artefacts
(eye movements, EMG corruption, EKG, etc), using three criteria: (i) Isolated source on the scalp (only a
few electrodes contribute to the source), (ii) Abnormal wave shape (drifts, eye blinks, sharp waves, etc.),
(iii) Source of abnormally high amplitude (�100 �V). We then evaluated the outcome of this cleaning by
means of the classification of patients using multilayer perceptron neural networks. Results are very
satisfactory and performance is increased from 50.9% to 73.1% correctly classified data using ICA cleaning
procedure
Differences of Functional Connectivity Brain Network in Emotional Judgment
Using combined emotional stimuli, combining photos of faces and recording of voices, we investigated the
neural dynamics of emotional judgment using scalp EEG recordings. Stimuli could be either combioned in a
congruent, or a non-congruent way.. As many evidences show the major role of alpha in emotional
processing, the alpha band was subjected to be analyzed. Analysis was performed by computing the
synchronization of the EEGs and the conditions congruent vs. non-congruent were compared using
statistical tools. The obtained results demonstrate that scalp EEG ccould be used as a tool to investigate the
neural dynamics of emotional valence and discriminate various emotions (angry, happy and neutral stimuli)
Slowing and Loss of Complexity in Alzheimer's EEG: Two Sides of the Same Coin?
Medical studies have shown that EEG of
Alzheimer's disease (AD) patients is “slower” (i.e., contains
more low-frequency power) and is less complex compared to
age-matched healthy subjects. The relation between those two
phenomena has not yet been studied, and they are often silently
assumed to be independent. In this paper, it is shown that
both phenomena are strongly related. Strong correlation between
slowing and loss of complexity is observed in two independent
EEG datasets: (1) EEG of predementia patients (a.k.a. Mild
Cognitive Impairment; MCI) and control subjects; (2) EEG of
mild AD patients and control subjects. The two data sets are
from different patients, different hospitals and obtained through
different recording systems. The paper also investigates the potential of EEG slowing and
loss of EEG complexity as indicators of AD onset. In particular,
relative power and complexity measures are used as features to
classify the MCI and MiAD patients versus age-matched control
subjects. When combined with two synchrony measures (Granger causality and stochastic event
synchrony), classification rates of 83% (MCI) and 98% (MiAD)
are obtained. By including the compression ratios as features,
slightly better classification rates are obtained than with relative
power and synchrony measures alone
EEG windowed statistical wavelet scoring for evaluation and discrimination of muscular artifacts
EEG recordings are usually corrupted by spurious extra-cerebral artifacts,
which should be rejected or cleaned up by the practitioner. Since manual
screening of human EEGs is inherently error prone and might induce
experimental bias, automatic artifact detection is an issue of importance.
Automatic artifact detection is the best guarantee for objective and clean results.
We present a new approach, based on the time–frequency shape of muscular
artifacts, to achieve reliable and automatic scoring. The impact of muscular
activity on the signal can be evaluated using this methodology by placing
emphasis on the analysis of EEG activity. The method is used to discriminate
evoked potentials from several types of recorded muscular artifacts—with a
sensitivity of 98.8% and a specificity of 92.2%. Automatic cleaning ofEEGdata
are then successfully realized using this method, combined with independent
component analysis. The outcome of the automatic cleaning is then compared
with the Slepian multitaper spectrum based technique introduced by Delorme
et al (2007 Neuroimage 34 1443–9)
Chronic insomnia: are patients also suffering from PTSD symptoms?
IntroductionInsomnia is highly prevalent in the general population, and is commonly associated with somatic and psychiatric comorbidities. However, its origins remain poorly-understood. Recently, adverse childhood events (ACE), including traumatic experiences, have been found to be significantly associated with both insomnia and Post-Traumatic Stress Disorders (PTSD). Many patients with PTSD suffer from sleep disorders. However, we know much less about traumatic childhood experiences in patients with insomnia and PTSD.MethodsOur exploratory study investigated a cohort of 43 patients (14 males, 29 females) clinically diagnosed with chronic insomnia at a sleep center, and systematically evaluated their condition using the trauma history questionnaire (THQ), and the PTSD checklist (PCL-5).ResultsOur results show that 83.72% of insomnia patients reported at least one traumatic event, while the prevalence of PTSD symptoms was 53.49%. For 11.6% of patients, insomnia began in childhood, while for 27.07% it began in adolescence. PCL-5 scores were associated with higher Insomnia Severity Index (ISI) scores, but not trauma. ISI scores were also higher for women, and positive relationships were observed between ISI scores, PCL-5 scores and the number of self-reported traumatic events among women.ConclusionsThese exploratory results highlight that the relationship between PTSD symptoms and insomnia could be sex-specific. They also highlight the importance of PTSD symptoms screening for patients diagnosed with chronic insomnia
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