132 research outputs found
A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery
Common spatial pattern (CSP) is a popular feature extraction method for
electroencephalogram (EEG) motor imagery (MI). This study modifies the
conventional CSP algorithm to improve the multi-class MI classification
accuracy and ensure the computation process is efficient. The EEG MI data is
gathered from the Brain-Computer Interface (BCI) Competition IV. At first, a
bandpass filter and a time-frequency analysis are performed for each experiment
trial. Then, the optimal EEG signals for every experiment trials are selected
based on the signal energy for CSP feature extraction. In the end, the
extracted features are classified by three classifiers, linear discriminant
analysis (LDA), na\"ive Bayes (NVB), and support vector machine (SVM), in
parallel for classification accuracy comparison. The experiment results show
the proposed algorithm average computation time is 37.22% less than the FBCSP
(1st winner in the BCI Competition IV) and 4.98% longer than the conventional
CSP method. For the classification rate, the proposed algorithm kappa value
achieved 2nd highest compared with the top 3 winners in BCI Competition IV.Comment: Accepted by 42nd Annual International Conferences of the IEEE
Engineering in Medicine and Biology Society in conjunction with the 43rd
Annual Conference of the Canadian Medical and Biological Engineering Society,
202
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
CES-513 Stages for Developing Control Systems using EMG and EEG Signals: A survey
Bio-signals such as EMG (Electromyography), EEG (Electroencephalography), EOG (Electrooculogram), ECG (Electrocardiogram) have been deployed recently to develop control systems for improving the quality of life of disabled and elderly people. This technical report aims to review the current deployment of these state of the art control systems and explain some challenge issues. In particular, the stages for developing EMG and EEG based control systems are categorized, namely data acquisition, data segmentation, feature extraction, classification, and controller. Some related Bio-control applications are outlined. Finally a brief conclusion is summarized.
ICA-SVM combination algorithm for identification of motor imagery potentials
Mental tasks such as motor imagery in synchronization with a cue which result event related desynchronization (ERD) and event related synchronization (ERS) are usually studied in brain-computer interface (BCI) system. In this paper we analyze and classify the ERD/ERS response evoked by the motor imagery of left hand, right hand, foot and tongue. The signals were spatially filtered by Independent Component Analysis (ICA) before calculating the power spectral density (PSD) for related electrodes, and then the Support Vector Machine (SVM) was adopted to recognise the different imagery pattern according to ERD/ERS feature for the signals. The results showed that the combination of ICA-based signal extraction algorithm and SVM-based classification method was an effective tool for the identification of motor imagery potentials, with the highest accuracy rate of 91.4% and 77.6% for the lowest. © 2010 IEEE.published_or_final_versionThe 2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA), Taranto, Apulia, Italy, 6-8 September 2010. In Proceedings of IEEE-CIMSA, 2010, p. 92-9
Trends in EEG signal feature extraction applications
This paper will focus on electroencephalogram (EEG) signal analysis with an emphasis on common feature extraction techniques mentioned in the research literature, as well as a variety of applications that this can be applied to. In this review, we cover single and multi-dimensional EEG signal processing and feature extraction techniques in the time domain, frequency domain, decomposition domain, time-frequency domain, and spatial domain. We also provide pseudocode for the methods discussed so that they can be replicated by practitioners and researchers in their specific areas of biomedical work. Furthermore, we discuss artificial intelligence applications such as assistive technology, neurological disease classification, brain-computer interface systems, as well as their machine learning integration counterparts, to complete the overall pipeline design for EEG signal analysis. Finally, we discuss future work that can be innovated in the feature extraction domain for EEG signal analysis
The classification of wink-based eeg signals by means of transfer learning models
Stroke is one of the dominant causes of impairme nt. An estimation of half post-stroke survivors suffer from a severe motor or cognitive deterioration, that affects the functionality of the affected parts of the body, which in turn, prevents the patients from carrying out Activities of Daily Living (ADL). EEG signals which contains information on the activities carried out by a human that is widely used in many applications of BCI technologies which offers a means of controlling exoskeletons or automated orthosis to facilitate their ADL. Although motor imagery signals have been used in assisting the hand grasping motion amongst others motions, nonetheless, such signals are often difficult to be generated. It is non-trivial to note that EEG-based signals for instance, winking could mitigate the aforesaid issue. Nevertheless, extracting and attaining significant features from EEG signals are also somewhat challenging. The utilization of deep learning, particularly Transfer Learning (TL), have been demonstrated in the literature to b e able to provide seamless extraction of such signals in a myria d of various applications. Hitherto, limited studies have investigated the classification of wink-based EEG signals through TL accompanied by classical Machine Learning (ML) pipelines. This study aimed to explore the performance of different pre-processing methods, namely Fast Fourier Transform, Short-Time Fourier Transform, Discrete Wavelet Transform, and Continuous Wavelet Transform (CWT) that could allow TL models to extract features from the images generated and classify through selected classical ML algorithms . These pre-processing methods were utilized to convert the digital signals into respective images of all the right and left winking EEG signals along with no winking signals that were collected from ten (6 males and 4 females, aged between 22 and 29) subjects. The implementation of pre-processing algorithms has been demonstrated to be able to mitigate the signal noises that arises from the winking signals without the need for the use signal filtering algorithms. A new form of input which consists of scalogram and spectrogram images that represents both time and frequency domains , are then introduced in the classification of wink-based EEG signals. Different TL models were exploited to extract features from the transformed EEG signals. The features extracted were then classified through three classical ML models, namely Support Vector Machine, k -Nearest Neighbour (k-NN) and Random Forest to determine the best pipeline for wink -based EEG signals. The hyperparameters of the ML models were tuned through a 5-fold crossvalidation technique via an exhaustive grid search approach. The training, validation and testing of the models were split with a stratified ratio of 60:20:20, respectively. The results obtained from the TL-ML pipelines were evaluated in terms of classification accuracy, Precision, Recall, F1-Score and confusion matrix. It was demonstrated from the simulation investigation that the CWT model could yield a better signal transformation amongst the preprocessing algorithms. In addition, amongst the eighteen TL models evaluated based on the CWT transformation, fourteen was f ound to be able to extract the features reasonable, i.e., VGG16, VGG19, ResNet101, ResNet101 V2, ResNet152, ResNet152 V2, Inception V3, Inception ResNet V2, Xception, MobileNetV2, DenseNet 121, DenseNet 169, NasNetMobile and NasNetLarge. Whilst it was observed that the optimized k-NN model based on the aforesaid pipeline could achieve a classification accuracy of 100% for the training, validation, and tes t data. Nonetheless, upon carrying out a robustness test on new data, it was demonstrated that the CWT-NasNetMobile-kNN pipeline yielded the best performance. Therefore, it could be concluded that the proposed CWT-NasNetMobile-k-NN pipeline is suitable to be adopted to classify -winkbased EEG signals for BCI applications,for instance a grasping exoskeleton
Single-trial classification of an EEG-based brain computer interface using the wavelet packet decomposition and cepstral analysis
Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2009.ENGLISH ABSTRACT: Brain-Computer Interface (BCI) monitors brain activity by using signals
such as EEG, EcOG, and MEG, and attempts to bridge the gap between
thoughts and actions by providing control to physical devices that range from
wheelchairs to computers. A crucial process for a BCI system is feature extraction,
and many studies have been undertaken to find relevant information
from a set of input signals.
This thesis investigated feature extraction from EEG signals using two
different approaches. Wavelet packet decomposition was used to extract information
from the signals in their frequency domain, and cepstral analysis was
used to search for relevant information in the cepstral domain. A BCI was implemented
to evaluate the two approaches, and three classification techniques
contributed to finding the effectiveness of each feature type.
Data containing two-class motor imagery was used for testing, and the BCI
was compared to some of the other systems currently available. Results indicate
that both approaches investigated were effective in producing separable
features, and, with further work, can be used for the classification of trials
based on a paradigm exploiting motor imagery as a means of control.AFRIKAANSE OPSOMMING: ’n Brein-Rekenaar Koppelvlak (BRK) monitor brein aktiwiteit deur gebruik
te maak van seine soos EEG, EcOG, en MEG. Dit poog om die gaping
tussen gedagtes en fisiese aksies te oorbrug deur beheer aan toestelle soos
rolstoele en rekenaars te verskaf. ’n Noodsaaklike proses vir ’n BRK is die
ontginning van toepaslike inligting uit inset-seine, wat kan help om tussen verskillende
gedagtes te onderskei. Vele studies is al onderneem oor hoe om sulke
inligting te vind.
Hierdie tesis ondersoek die ontginning van kenmerk-vektore in EEG-seine
deur twee verskillende benaderings. Die eerste hiervan is golfies pakkie ontleding,
’n metode wat gebruik word om die sein in die frekwensie gebied voor
te stel. Die tweede benadering gebruik kepstrale analise en soek vir toepaslike
inligting in die kepstrale domein. ’n BRK is geïmplementeer om beide metodes
te evalueer.
Die toetsdata wat gebruik is, het bestaan uit twee-klas motoriese verbeelde
bewegings, en drie klassifikasie-tegnieke was gebruik om die doeltreffendheid
van die twee metodes te evalueer. Die BRK is vergelyk met ander stelsels
wat tans beskikbaar is, en resultate dui daarop dat beide metodes doeltreffend
was. Met verdere navorsing besit hulle dus die potensiaal om gebruik te word
in stelsels wat gebruik maak van motoriese verbeelde bewegings om fisiese
toestelle te beheer
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