17,933 research outputs found
A real time classification algorithm for EEG-based BCI driven by self-induced emotions
Background and objective: The aim of this paper is to provide an efficient, parametric, general, and completely automatic real time classification method of electroencephalography (EEG) signals obtained from self-induced emotions. The particular characteristics of the considered low-amplitude signals (a self-induced emotion produces a signal whose amplitude is about 15% of a really experienced emotion) require exploring and adapting strategies like the Wavelet Transform, the Principal Component Analysis (PCA) and the Support Vector Machine (SVM) for signal processing, analysis and classification. Moreover, the method is thought to be used in a multi-emotions based Brain Computer Interface (BCI) and, for this reason, an ad hoc shrewdness is assumed. Method: The peculiarity of the brain activation requires ad-hoc signal processing by wavelet decomposition, and the definition of a set of features for signal characterization in order to discriminate different self-induced emotions. The proposed method is a two stages algorithm, completely parameterized, aiming at a multi-class classification and may be considered in the framework of machine learning. The first stage, the calibration, is off-line and is devoted at the signal processing, the determination of the features and at the training of a classifier. The second stage, the real-time one, is the test on new data. The PCA theory is applied to avoid redundancy in the set of features whereas the classification of the selected features, and therefore of the signals, is obtained by the SVM. Results: Some experimental tests have been conducted on EEG signals proposing a binary BCI, based on the self-induced disgust produced by remembering an unpleasant odor. Since in literature it has been shown that this emotion mainly involves the right hemisphere and in particular the T8 channel, the classification procedure is tested by using just T8, though the average accuracy is calculated and reported also for the whole set of the measured channels. Conclusions: The obtained classification results are encouraging with percentage of success that is, in the average for the whole set of the examined subjects, above 90%. An ongoing work is the application of the proposed procedure to map a large set of emotions with EEG and to establish the EEG headset with the minimal number of channels to allow the recognition of a significant range of emotions both in the field of affective computing and in the development of auxiliary communication tools for subjects affected by severe disabilities
Personality cannot be predicted from the power of resting state EEG
In the present study we asked whether it is possible to decode personality
traits from resting state EEG data. EEG was recorded from a large sample of
subjects (N = 309) who had answered questionnaires measuring personality trait
scores of the 5 dimensions as well as the 10 subordinate aspects of the Big
Five. Machine learning algorithms were used to build a classifier to predict
each personality trait from power spectra of the resting state EEG data. The
results indicate that the five dimensions as well as their subordinate aspects
could not be predicted from the resting state EEG data. Finally, to demonstrate
that this result is not due to systematic algorithmic or implementation
mistakes the same methods were used to successfully classify whether the
subject had eyes open or eyes closed and whether the subject was male or
female. These results indicate that the extraction of personality traits from
the power spectra of resting state EEG is extremely noisy, if possible at all.Comment: 14 pages, 4 figure
Characterization of spatio-temporal epidural event-related potentials for mouse models of psychiatric disorders.
Distinctive features in sensory event-related potentials (ERPs) are endophenotypic biomarkers of psychiatric disorders, widely studied using electroencephalographic (EEG) methods in humans and model animals. Despite the popularity and unique significance of the mouse as a model species in basic research, existing EEG methods applicable to mice are far less powerful than those available for humans and large animals. We developed a new method for multi-channel epidural ERP characterization in behaving mice with high precision, reliability and convenience and report an application to time-domain ERP feature characterization of the Sp4 hypomorphic mouse model for schizophrenia. Compared to previous methods, our spatio-temporal ERP measurement robustly improved the resolving power of key signatures characteristic of the disease model. The high performance and low cost of this technique makes it suitable for high-throughput behavioral and pharmacological studies
Classification of EEG recordings in auditory brain activity via a logistic functional linear regression model
We want to analyse EEG recordings in order to investigate the phonemic
categorization at a very early stage of auditory processing. This problem can
be modelled by a supervised classification of functional data. Discrimination
is explored via a logistic functional linear model, using a wavelet
representation of the data. Different procedures are investigated, based on
penalized likelihood and principal component reduction or partial least squares
reduction
Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation
Rapid Serial Visual Presentation (RSVP) is a paradigm that supports the
application of cortically coupled computer vision to rapid image search. In
RSVP, images are presented to participants in a rapid serial sequence which can
evoke Event-related Potentials (ERPs) detectable in their Electroencephalogram
(EEG). The contemporary approach to this problem involves supervised spatial
filtering techniques which are applied for the purposes of enhancing the
discriminative information in the EEG data. In this paper we make two primary
contributions to that field: 1) We propose a novel spatial filtering method
which we call the Multiple Time Window LDA Beamformer (MTWLB) method; 2) we
provide a comprehensive comparison of nine spatial filtering pipelines using
three spatial filtering schemes namely, MTWLB, xDAWN, Common Spatial Pattern
(CSP) and three linear classification methods Linear Discriminant Analysis
(LDA), Bayesian Linear Regression (BLR) and Logistic Regression (LR). Three
pipelines without spatial filtering are used as baseline comparison. The Area
Under Curve (AUC) is used as an evaluation metric in this paper. The results
reveal that MTWLB and xDAWN spatial filtering techniques enhance the
classification performance of the pipeline but CSP does not. The results also
support the conclusion that LR can be effective for RSVP based BCI if
discriminative features are available
Adaptive Mantel Test for AssociationTesting in Imaging Genetics Data
Mantel's test (MT) for association is conducted by testing the linear
relationship of similarity of all pairs of subjects between two observational
domains. Motivated by applications to neuroimaging and genetics data, and
following the succes of shrinkage and kernel methods for prediction with
high-dimensional data, we here introduce the adaptive Mantel test as an
extension of the MT. By utilizing kernels and penalized similarity measures,
the adaptive Mantel test is able to achieve higher statistical power relative
to the classical MT in many settings. Furthermore, the adaptive Mantel test is
designed to simultaneously test over multiple similarity measures such that the
correct type I error rate under the null hypothesis is maintained without the
need to directly adjust the significance threshold for multiple testing. The
performance of the adaptive Mantel test is evaluated on simulated data, and is
used to investigate associations between genetics markers related to
Alzheimer's Disease and heatlhy brain physiology with data from a working
memory study of 350 college students from Beijing Normal University
Multilevel functional principal component analysis
The Sleep Heart Health Study (SHHS) is a comprehensive landmark study of
sleep and its impacts on health outcomes. A primary metric of the SHHS is the
in-home polysomnogram, which includes two electroencephalographic (EEG)
channels for each subject, at two visits. The volume and importance of this
data presents enormous challenges for analysis. To address these challenges, we
introduce multilevel functional principal component analysis (MFPCA), a novel
statistical methodology designed to extract core intra- and inter-subject
geometric components of multilevel functional data. Though motivated by the
SHHS, the proposed methodology is generally applicable, with potential
relevance to many modern scientific studies of hierarchical or longitudinal
functional outcomes. Notably, using MFPCA, we identify and quantify
associations between EEG activity during sleep and adverse cardiovascular
outcomes.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS206 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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