2,912 research outputs found
Motor imagery task classification using a signal-dependent orthogonal transform based feature extraction
© Springer International Publishing Switzerland 2015. In this paper, we present the results of classifying electroencephalographic (EEG) signals into four motor imagery tasks using a new method for feature extraction. This method is based on a signal-dependent orthogonal transform, referred to as LP-SVD, defined as the left singular vectors of the LPC filter impulse response matrix. Using a logistic tree based model classifier, the extracted features are mapped into one of four motor imagery movements, namely left hand, right hand, foot, and tongue. The proposed technique-based classification performance was benchmarked against those based on two widely used linear transform for feature extraction methods, namely discrete cosine transform (DCT) and adaptive autoregressive (AAR). By achieving an accuracy of 67.35 %, the LP-SVD based method outperformed the other two by large margins (+25 % compared to DCT and +6 % compared to AAR-based methods)
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
An Effective Brain-Computer Interface System Based on the Optimal Timeframe Selection of Brain Signals
Background: Brain responds in a short timeframe (with certain delay) after the request for doing a motor imagery task and therefore it is most likely that the individual not focus continuously on the task at entire interval of data acquisition time or even think about other things in a very short time slice. In this paper, an effective brain-computer interface system is presented based on the optimal timeframe selection of brain signals.Methods: To prove the stated claim, various timeframes with different durations and delays selected based on a specific rule from EEG signals recorded during right/left hand motor imagery task and subsequently, feature extraction and classification are done.Results: Implementation results on the two well-known datasets termed Graz 2003 and Graz 2005; shows that the smallest systematically created timeframe of data acquisition interval have had the best results of classification. Using this smallest timeframe, the classification accuracy increased up to 91.43% for Graz 2003 and 88.96, 83.64 and 84.86 percent for O3, S4 and X11 subjects of Graz 2005 database respectively.Conclusion: Removing the additional information in which the individual does not focus on the motor imagery task and utilizing the most distinguishing timeframe of EEG signals that correctly interpret individual intentions improves the BCI system performance
Wavelet transform methods for identifying onset of SEMG activity
Quantifying improvements in motor control is predicated on the accurate identification of the onset of surface electromyograpic (sEMG) activity. Applying methods from wavelet theory developed in the past decade to digitized signals, a robust algorithm has been designed for use with sEMG collected during reaching tasks executed with the less-affected arm of stroke patients. The method applied both Discretized Continuous Wavelet Transforms (CWT) and Discrete Wavelet Transforms (DWT) for event detection and no-lag filtering, respectively. Input parameters were extracted from the assessed signals.
The onset times found in the sEMG signals using the wavelet method were compared with physiological instants of motion onset, determined from video data. Robustness was evaluated by considering the response in onset time with variations of input parameter values.
The wavelet method found physiologically relevant onset times in all signals, averaging 147 ms prior to motion onset, compared to predicted onset latencies of 90-110 ins. Latency exhibited slight dependence on subject, but no other variables
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
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