A general framework to estimate spatial and spatio-spectral filters for EEG signal classification

Fattahi, Davood; Nasihatkon, Behrooz; Boostani, Reza

doi:10.1016/j.neucom.2013.03.044

Cited by 32 publications

(12 citation statements)

References 18 publications

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“…Wavelet CSP methods have been considered in (Mousavi et al, 2011;Robinson et al, 2013) whereas the authors of (Falzon et al, 2012) improve the discriminative capability of CSP by taking into account both the amplitude and phase components of the EEG signal. A CSP variant directly optimizing the discriminativity of the features was proposed in (Thomas et al, 2009;Fattahi et al, 2013). A recently published approach (Li et al, 2013) learns spatial filters by considering signal propagation and volume conduction effects.…”

Section: Other Approachesmentioning

confidence: 99%

On robust spatial filtering of EEG in nonstationary environments

Samek

2016

It - Information Technology

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bibliography 119 I N T R O D U C T I O NVariability is the law of life, and as no two faces are the same, so no two bodies are alike, and no two individuals react alike and behave alike under the abnormal conditions which we know as disease. (Sir William Osler, 1903) T he Canadian physician Sir William Osler (1849 -1919 revolutionized the medical world of the 19th century by advocating an approach to therapy which focuses on the needs of individual patients. He recognized the great variability among individuals with respect to their physical conditions, their mental status and their responses to drugs and concluded that focusing solely on clinical signs and symptoms does not result in the most effective therapy. The development of this novel patient-centered paradigm made him the father of personalized medicine; a branch of medicine that is also very popular today due to a better understanding of the genetic influences on a person's risk of acquiring certain diseases.In the last two decades personalized approaches have also revolutionized the field of Brain-Computer Interfacing (BCI). A BCI system (see e.g. Wolpaw et al., 2002;Dornhege et al., 2007;Wolpaw and Wolpaw, 2012) aims to decode the intention of a user from recordings of brain activity and to use this information for controlling a computer application or a robotic device. Today's BCIs extract user-specific features from electroencelographic (EEG) recordings and adapt to the user's signal characteristics. This subject-centered or machine learning approach not only largely reduces the calibration time in comparison to classical BCI systems based on neurofeedback training (Kamiya et al., 1969) but also increases classification accuracy. Unfortunately, EEG responses to a stimulus or a task not only differ from subject to subject but also from trial to trial and from day to day. This change in the signal properties over time is termed the intrinsic nonstationarity of EEG (Kaplan et al., 2005) and constitutes a major challenge for data analysis and classification by violating a basic assumption of many machine learning methods, namely that data are sampled from a fixed (but unknown) distribution (Vapnik, 1998;Hastie et al., 2001). The analysis of EEG is further aggravated by the presence of artifacts in the data, e.g., eye movements, muscular activity or loose electrodes. The lack of robustness to unexpected events and the nonstationary nature of EEG are major reasons in explaining why current BCI technology is seldom used in out-of-lab scenarios and clinical practice. Since artifacts and nonstationarity can not be fully eliminated, even with the best experimental protocol, robust and invariant feature 1 2 introduction representations are required for optimal signal analysis and high classification accuracy. structure of the thesisThis thesis is divided into three parts. The first part introduces the basic concepts of motor imagery-based Brain-Computer Interfacing and discusses the advantages and limitations of several state-of-the-art spatial filtering al...

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Section: Other Approachesmentioning

confidence: 99%

On robust spatial filtering of EEG in nonstationary environments

Samek

2016

It - Information Technology

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“…Common spatial pattern (CSP) is a powerful motor imagery feature extraction method used for classification problems [16]. The high dimensionality of extracted feature vector adversely affects the classifier performance [17]. Hence feature selection is used to select only relevant features [18].…”

Section: Introductionmentioning

confidence: 99%

Investigation of EEG Signal Classification Techniques for Brain Computer Interface

Ghumman,

Singh

2020

IJEAT

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Brain-computer interface (BCI) has emerged as a popular research domain in recent years. The use of electroencephalography (EEG) signals for motor imagery (MI) based BCI has gained widespread attention. The first step in its implementation is to fetch EEG signals from scalp of human subject. The preprocessing of EEG signals is done before applying feature extraction, selection and classification techniques as main steps of signal processing. In preprocessing stage, artifacts are removed from raw brain signals before these are input to next stage of feature extraction. Subsequently classifier algorithms are used to classify selected features into intended MI tasks. The major challenge in a BCI systems is to improve classification accuracy of a BCI system. In this paper, an approach based on Support Vector Machine (SVM), is proposed for signal classification to improve accuracy of the BCI system. The parameters of kernel are varied to attain improvement in classification accuracy. Independent component analysis (ICA) technique is used for preprocessing and filter bank common spatial pattern (FBCSP) for feature extraction and selection. The proposed approach is evaluated on data set 2a of BCI Competition IV by using 5-fold crossvalidation procedure. Results show that it performs better in terms of classification accuracy, as compared to other methods reported in literature.

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“…Up to now, several feature extraction methods for EEG signals have been applied in BCI applications, such as the Common Spatial Patterns (CSP) (Fattahi, Nasihatkon, & Boostani, 2013), Wavelet Transform (WT) (Liao, Zhu, & Ding, 2013;Ting, Guozheng, Bang-hua, & Hong, 2008), Power Spectral Density (PSD) (Park et al, 2013) and spatio-spectral patterns (Wu, Gao, Hong, & Gao, 2008). Many researchers have analyzed the linear spatial filtering methods like CSP, such as the Regularized CSP (RCSP), stationary CSP (sCSP), spectrally weighted CSP (SPEC-CSP), Fisher's common spatio-spectral pattern (FCSSP) and iterative spatio-spectral pattern learning (ISSPL) (Fattahi et al, 2013;Lotte & Guan, 2011;Samek, Vidaurre, Müller, & Kawanabe, 2012;Wu, Lai, Xia, Wu, & Yao, 2008). These methods do not consider the non-stationary and high variable nature on time and frequency of the EEG signals.…”

Section: Introductionmentioning

confidence: 99%