2,057 research outputs found
Complex Independent Component Analysis of Frequency-Domain Electroencephalographic Data
Independent component analysis (ICA) has proven useful for modeling brain and
electroencephalographic (EEG) data. Here, we present a new, generalized method
to better capture the dynamics of brain signals than previous ICA algorithms.
We regard EEG sources as eliciting spatio-temporal activity patterns,
corresponding to, e.g., trajectories of activation propagating across cortex.
This leads to a model of convolutive signal superposition, in contrast with the
commonly used instantaneous mixing model. In the frequency-domain, convolutive
mixing is equivalent to multiplicative mixing of complex signal sources within
distinct spectral bands. We decompose the recorded spectral-domain signals into
independent components by a complex infomax ICA algorithm. First results from a
visual attention EEG experiment exhibit (1) sources of spatio-temporal dynamics
in the data, (2) links to subject behavior, (3) sources with a limited spectral
extent, and (4) a higher degree of independence compared to sources derived by
standard ICA.Comment: 21 pages, 11 figures. Added final journal reference, fixed minor
typo
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EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks.
Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a "reach/saccade to spatial target" cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI
Enhancing brain-computer interfacing through advanced independent component analysis techniques
A Brain-computer interface (BCI) is a direct communication system between a brain
and an external device in which messages or commands sent by an individual do not
pass through the brain’s normal output pathways but is detected through brain signals.
Some severe motor impairments, such as Amyothrophic Lateral Sclerosis, head
trauma, spinal injuries and other diseases may cause the patients to lose their muscle
control and become unable to communicate with the outside environment. Currently
no effective cure or treatment has yet been found for these diseases. Therefore using a
BCI system to rebuild the communication pathway becomes a possible alternative
solution. Among different types of BCIs, an electroencephalogram (EEG) based BCI
is becoming a popular system due to EEG’s fine temporal resolution, ease of use,
portability and low set-up cost. However EEG’s susceptibility to noise is a major
issue to develop a robust BCI. Signal processing techniques such as coherent
averaging, filtering, FFT and AR modelling, etc. are used to reduce the noise and
extract components of interest. However these methods process the data on the
observed mixture domain which mixes components of interest and noise. Such a
limitation means that extracted EEG signals possibly still contain the noise residue or
coarsely that the removed noise also contains part of EEG signals embedded.
Independent Component Analysis (ICA), a Blind Source Separation (BSS)
technique, is able to extract relevant information within noisy signals and separate the
fundamental sources into the independent components (ICs). The most common
assumption of ICA method is that the source signals are unknown and statistically
independent. Through this assumption, ICA is able to recover the source signals.
Since the ICA concepts appeared in the fields of neural networks and signal
processing in the 1980s, many ICA applications in telecommunications, biomedical
data analysis, feature extraction, speech separation, time-series analysis and data
mining have been reported in the literature. In this thesis several ICA techniques are
proposed to optimize two major issues for BCI applications: reducing the recording
time needed in order to speed up the signal processing and reducing the number of
recording channels whilst improving the final classification performance or at least
with it remaining the same as the current performance. These will make BCI a more
practical prospect for everyday use.
This thesis first defines BCI and the diverse BCI models based on different
control patterns. After the general idea of ICA is introduced along with some
modifications to ICA, several new ICA approaches are proposed. The practical work
in this thesis starts with the preliminary analyses on the Southampton BCI pilot
datasets starting with basic and then advanced signal processing techniques. The
proposed ICA techniques are then presented using a multi-channel event related
potential (ERP) based BCI. Next, the ICA algorithm is applied to a multi-channel
spontaneous activity based BCI. The final ICA approach aims to examine the
possibility of using ICA based on just one or a few channel recordings on an ERP
based BCI.
The novel ICA approaches for BCI systems presented in this thesis show that ICA
is able to accurately and repeatedly extract the relevant information buried within
noisy signals and the signal quality is enhanced so that even a simple classifier can
achieve good classification accuracy. In the ERP based BCI application, after multichannel
ICA the data just applied to eight averages/epochs can achieve 83.9%
classification accuracy whilst the data by coherent averaging can reach only 32.3%
accuracy. In the spontaneous activity based BCI, the use of the multi-channel ICA
algorithm can effectively extract discriminatory information from two types of singletrial
EEG data. The classification accuracy is improved by about 25%, on average,
compared to the performance on the unpreprocessed data. The single channel ICA
technique on the ERP based BCI produces much better results than results using the
lowpass filter. Whereas the appropriate number of averages improves the signal to
noise rate of P300 activities which helps to achieve a better classification. These
advantages will lead to a reliable and practical BCI for use outside of the clinical
laboratory
Tensor Analysis and Fusion of Multimodal Brain Images
Current high-throughput data acquisition technologies probe dynamical systems
with different imaging modalities, generating massive data sets at different
spatial and temporal resolutions posing challenging problems in multimodal data
fusion. A case in point is the attempt to parse out the brain structures and
networks that underpin human cognitive processes by analysis of different
neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the
multimodal, multi-scale nature of neuroimaging data is well reflected by a
multi-way (tensor) structure where the underlying processes can be summarized
by a relatively small number of components or "atoms". We introduce
Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network
notation in order to analyze these models. These diagrams not only clarify
matrix and tensor EEG and fMRI time/frequency analysis and inverse problems,
but also help understand multimodal fusion via Multiway Partial Least Squares
and Coupled Matrix-Tensor Factorization. We show here, for the first time, that
Granger causal analysis of brain networks is a tensor regression problem, thus
allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI
recordings shows the potential of the methods and suggests their use in other
scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE
Translation of EEG spatial filters from resting to motor imagery using independent component analysis.
Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) often use spatial filters to improve signal-to-noise ratio of task-related EEG activities. To obtain robust spatial filters, large amounts of labeled data, which are often expensive and labor-intensive to obtain, need to be collected in a training procedure before online BCI control. Several studies have recently developed zero-training methods using a session-to-session scenario in order to alleviate this problem. To our knowledge, a state-to-state translation, which applies spatial filters derived from one state to another, has never been reported. This study proposes a state-to-state, zero-training method to construct spatial filters for extracting EEG changes induced by motor imagery. Independent component analysis (ICA) was separately applied to the multi-channel EEG in the resting and the motor imagery states to obtain motor-related spatial filters. The resultant spatial filters were then applied to single-trial EEG to differentiate left- and right-hand imagery movements. On a motor imagery dataset collected from nine subjects, comparable classification accuracies were obtained by using ICA-based spatial filters derived from the two states (motor imagery: 87.0%, resting: 85.9%), which were both significantly higher than the accuracy achieved by using monopolar scalp EEG data (80.4%). The proposed method considerably increases the practicality of BCI systems in real-world environments because it is less sensitive to electrode misalignment across different sessions or days and does not require annotated pilot data to derive spatial filters
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