33,151 research outputs found
EEG sleep stages identification based on weighted undirected complex networks
Sleep scoring is important in sleep research because any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings can cause serious problems such as incorrect diagnosis, medication errors, and misinterpretations of patient's EEG recordings. The aim of this research is to develop a new automatic method for EEG sleep stages classification based on a statistical model and weighted brain networks.
Methods
each EEG segment is partitioned into a number of blocks using a sliding window technique. A set of statistical features are extracted from each block. As a result, a vector of features is obtained to represent each EEG segment. Then, the vector of features is mapped into a weighted undirected network. Different structural and spectral attributes of the networks are extracted and forwarded to a least square support vector machine (LS-SVM) classifier. At the same time the network's attributes are also thoroughly investigated. It is found that the network's characteristics vary with their sleep stages. Each sleep stage is best represented using the key features of their networks.
Results
In this paper, the proposed method is evaluated using two datasets acquired from different channels of EEG (Pz-Oz and C3-A2) according to the R&K and the AASM without pre-processing the original EEG data. The obtained results by the LS-SVM are compared with those by NaĂŻve, k-nearest and a multi-class-SVM. The proposed method is also compared with other benchmark sleep stages classification methods. The comparison results demonstrate that the proposed method has an advantage in scoring sleep stages based on single channel EEG signals.
Conclusions
An average accuracy of 96.74% is obtained with the C3-A2 channel according to the AASM standard, and 96% with the Pz-Oz channel based on the R&K standard
Frequency Recognition in SSVEP-based BCI using Multiset Canonical Correlation Analysis
Canonical correlation analysis (CCA) has been one of the most popular methods
for frequency recognition in steady-state visual evoked potential (SSVEP)-based
brain-computer interfaces (BCIs). Despite its efficiency, a potential problem
is that using pre-constructed sine-cosine waves as the required reference
signals in the CCA method often does not result in the optimal recognition
accuracy due to their lack of features from the real EEG data. To address this
problem, this study proposes a novel method based on multiset canonical
correlation analysis (MsetCCA) to optimize the reference signals used in the
CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple
linear transforms that implement joint spatial filtering to maximize the
overall correlation among canonical variates, and hence extracts SSVEP common
features from multiple sets of EEG data recorded at the same stimulus
frequency. The optimized reference signals are formed by combination of the
common features and completely based on training data. Experimental study with
EEG data from ten healthy subjects demonstrates that the MsetCCA method
improves the recognition accuracy of SSVEP frequency in comparison with the CCA
method and other two competing methods (multiway CCA (MwayCCA) and phase
constrained CCA (PCCA)), especially for a small number of channels and a short
time window length. The superiority indicates that the proposed MsetCCA method
is a new promising candidate for frequency recognition in SSVEP-based BCIs
Object Segmentation in Images using EEG Signals
This paper explores the potential of brain-computer interfaces in segmenting
objects from images. Our approach is centered around designing an effective
method for displaying the image parts to the users such that they generate
measurable brain reactions. When an image region, specifically a block of
pixels, is displayed we estimate the probability of the block containing the
object of interest using a score based on EEG activity. After several such
blocks are displayed, the resulting probability map is binarized and combined
with the GrabCut algorithm to segment the image into object and background
regions. This study shows that BCI and simple EEG analysis are useful in
locating object boundaries in images.Comment: This is a preprint version prior to submission for peer-review of the
paper accepted to the 22nd ACM International Conference on Multimedia
(November 3-7, 2014, Orlando, Florida, USA) for the High Risk High Reward
session. 10 page
Two Different Approaches of Feature Extraction for Classifying the EEG Signals
The electroencephalograph (EEG) signal is one of the most widely used signals in the biomedicine field due to its rich information about human tasks. This research study describes a new approach based on i) build reference models from a set of time series, based on the analysis of the events that they contain, is suitable for domains where the relevant information is concentrated in specific regions of the time series, known as events. In order to deal with events, each event is characterized by a set of attributes. ii) Discrete wavelet transform to the EEG data in order to extract temporal information in the form of changes in the frequency domain over time- that is they are able to extract non-stationary signals embedded in the noisy background of the human brain.
The performance of the model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals
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Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice.
Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today's world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios
EEG Classification based on Image Configuration in Social Anxiety Disorder
The problem of detecting the presence of Social Anxiety Disorder (SAD) using
Electroencephalography (EEG) for classification has seen limited study and is
addressed with a new approach that seeks to exploit the knowledge of EEG sensor
spatial configuration. Two classification models, one which ignores the
configuration (model 1) and one that exploits it with different interpolation
methods (model 2), are studied. Performance of these two models is examined for
analyzing 34 EEG data channels each consisting of five frequency bands and
further decomposed with a filter bank. The data are collected from 64 subjects
consisting of healthy controls and patients with SAD. Validity of our
hypothesis that model 2 will significantly outperform model 1 is borne out in
the results, with accuracy -- higher for model 2 for each machine
learning algorithm we investigated. Convolutional Neural Networks (CNN) were
found to provide much better performance than SVM and kNNs
Data-driven multivariate and multiscale methods for brain computer interface
This thesis focuses on the development of data-driven multivariate and multiscale methods
for brain computer interface (BCI) systems. The electroencephalogram (EEG), the
most convenient means to measure neurophysiological activity due to its noninvasive nature,
is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its
multichannel recording nature require a new set of data-driven multivariate techniques to
estimate more accurately features for enhanced BCI operation. Also, a long term goal
is to enable an alternative EEG recording strategy for achieving long-term and portable
monitoring.
Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully
data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary
EEG signal into a set of components which are highly localised in time and frequency. It
is shown that the complex and multivariate extensions of EMD, which can exploit common
oscillatory modes within multivariate (multichannel) data, can be used to accurately
estimate and compare the amplitude and phase information among multiple sources, a
key for the feature extraction of BCI system. A complex extension of local mean decomposition
is also introduced and its operation is illustrated on two channel neuronal
spike streams. Common spatial pattern (CSP), a standard feature extraction technique
for BCI application, is also extended to complex domain using the augmented complex
statistics. Depending on the circularity/noncircularity of a complex signal, one of the
complex CSP algorithms can be chosen to produce the best classification performance
between two different EEG classes.
Using these complex and multivariate algorithms, two cognitive brain studies are
investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user
attention to a sound source among a mixture of sound stimuli, which is aimed at improving
the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments
elicited by taste and taste recall are examined to determine the pleasure and displeasure
of a food for the implementation of affective computing. The separation between two
emotional responses is examined using real and complex-valued common spatial pattern
methods.
Finally, we introduce a novel approach to brain monitoring based on EEG recordings
from within the ear canal, embedded on a custom made hearing aid earplug. The new
platform promises the possibility of both short- and long-term continuous use for standard
brain monitoring and interfacing applications
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