Sparse common spatial patterns in brain computer interface applications

Goksu, Fikri; Ince, Nuri F.; Tewfik, A.H.

doi:10.1109/icassp.2011.5946458

Cited by 33 publications

(21 citation statements)

References 8 publications

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“…Regularization of the covariance matrix (Lu et al, 2009;Kang et al, 2009;Lu et al, 2010;Lotte and Guan, 2011) is also a common approach to increase robustness, especially in small-sample settings. Other authors (Lal et al, 2004;Arvaneh et al, 2011;Goksu et al, 2011) propose to improve the CSP solution by performing channel selection or enforcing sparsity on the spatial filters. The idea of computing CSP in a region of interest was used in (Grosse-Wentrup et al, 2007, 2009Sannelli et al, 2011).…”

Section: Robust Estimationmentioning

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: Robust Estimationmentioning

confidence: 99%

On robust spatial filtering of EEG in nonstationary environments

Samek

2016

It - Information Technology

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“…We can decrease the number of channels to the desired cardinality level by recursively applying this algorithm to the smaller matrices. Each cardinality reduction involves solving a traditional CSP, therefore this method is faster than other 0 norm based greedy search algorithms such as BE or FS [9].…”

Section: Recursive Weight Eliminationmentioning

confidence: 99%

“…Recently, quasi 0 norm based methods was used for obtaining the sparse solution which resulted an improved classification accuracy. Since 0 norm is non-convex, combinatorial and NP-hard, they implemented greedy solutions such as forward selection (FS), backward elimination (BE) [9] and recursive weight elimination (RWE) [10] to decrease the computational complexity. It has been shown that BE was better than RWE and FS (less myopic) in terms of classification error and sparseness level but associated with very high complexity making it difficult to use in rapid prototyping scenarios.…”

Section: Introductionmentioning

confidence: 99%

Baseline regularized sparse spatial filters

Onaran

Ince

Çetin

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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The common spatial pattern (CSP) method has large number of applications in brain machine interfaces (BMI) to extract features from the multichannel neural activity through a set of linear spatial projections. These spatial projections minimize the Rayleigh quotient (RQ) as the objective function, which is the variance ratio of the classes. The CSP method easily overfits the data when the number of training trials is not sufficiently large and it is sensitive to daily variation of multichannel electrode placement, which limits its applicability for everyday use in BMI systems. To overcome these problems, the amount of channels that is used in projections, should be limited to some adequate number. We introduce a spatially sparse projection (SSP) method that renders unconstrained minimization possible via a new objective function with an approximated ℓ1 penalty. We apply our new algorithm with a baseline regularization to the ECoG data involving finger movements to gain stability with respect to the number of sparse channels. © 2013 IEEE

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“…These studies have reported a slight decrease or no change in the classification accuracy while decreasing the number of channels significantly. Recently, in [8] quasi 0 norm based criterion was used for obtaining the sparse solution which resulted an improved classification accuracy. Since 0 norm is non-convex, combinatorial and NP-hard, they implemented greedy solutions such as Forward Selection (FS) and Backward Elimination (BE) to decrease the computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Sparse spatial filter via a novel objective function minimization with smooth ℓ1 regularization

Onaran

Ince

Çetin

2013

Biomedical Signal Processing and Control

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Common spatial pattern (CSP) method is widely used in brain machine interface (BMI) applications to extract features from the multichannel neural activity through a set of spatial projections. These spatial projections minimize the Rayleigh quotient (RQ) as the objective function, which is the variance ratio of the classes. The CSP method easily overfits the data when the number of training trials is not sufficiently large and it is sensitive to daily variation of multichannel electrode placement, which limits its applicability for everyday use in BMI systems. To overcome these problems, the amount of channels that is used in projections, should be limited to some adequate number. We introduce a spatially sparse projection (SSP) method that exploits the unconstrained minimization of a new objective function with approximated 1 penalty. Unlike the RQ, this new objective function depends on the magnitude of the sparse filter. The SSP method is employed to classify the multiclass ECoG and two class EEG data sets. We compared our results with a recently introduced sparse CSP solution based on 0 norm. Our method outperforms the standard CSP method and provides comparable results to 0 norm based solution and it is associated with less computational complexity. We also conducted several simulation studies on the effect of noisy channel and intersession variability on the performance of the CSP and sparse filters.

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Sparse common spatial patterns in brain computer interface applications

Cited by 33 publications

References 8 publications

On robust spatial filtering of EEG in nonstationary environments

On robust spatial filtering of EEG in nonstationary environments

Baseline regularized sparse spatial filters

Sparse spatial filter via a novel objective function minimization with smooth ℓ1 regularization

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