Bayesian Multi-task Learning for Common Spatial Patterns

Kang, Hyohyeong; Choi, Seungjin

doi:10.1109/prni.2011.8

Cited by 23 publications

(22 citation statements)

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“…The authors of (De-vlaminck et al, 2011) jointly train the spatial filters of several subjects by applying a multi-task learning algorithm. A Bayesian method for subject-to-subject information transfer has been proposed in (Kang and Choi, 2011). Data from other users have also been used as regularization target by (Kang et al, 2009;Lotte and Guan, 2010b).…”

Section: Multi-subject Methodsmentioning

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: Multi-subject Methodsmentioning

confidence: 99%

On robust spatial filtering of EEG in nonstationary environments

Samek

2016

It - Information Technology

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“…We briefly review PCSP [15] and our earlier work on Bayesian CSP (BCSP) [6]. We are given the data matrix X s,c = [x s,c 1 , .…”

Section: Related Workmentioning

confidence: 99%

“…1(a)). Bayesian CSP [6], shown in Fig. 1(b), employed a Bayesian multi-task learning method [5], enforcing spatial patterns (isotropic Gaussian prior placed on) across subjects to share the hyperparameters (precision of Gaussian) of their prior distributions which are allowed for learning from each other subjects:…”

Section: Related Workmentioning

confidence: 99%

“…A Bayesian multi-task extension of CSP (BCSP) was recently developed in [6], where subject-to-subject information was transferred during the learning of model for a subject of interest by sharing hyperparameters across subjects, while treating subjects as tasks. BCSP [6] works better than PCSP, although similarities among spatial patterns are neglected because all spatial patterns are forced to share the same hyperparameters. Bayesian CSP with Dirichlet process (DP) priors (BCSP-DP) [7] jointly learns and groups spatial patterns, so that spatial patterns in the same group, determined by the DP mixture model, share the hyperparameters of their prior distributions.…”

Section: Introductionmentioning

confidence: 99%

“…Our method, BCSP-IBP, is motivated by multi-task learning methods, based on infinite latent feature models [13,16]. Numerical experiments on BCI competition IV 2a dataset demonstrate the high performance of our method, compared to PCSP [15] and existing Bayesian multi-task CSP models [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Bayesian multi-subject common spatial patterns with Indian Buffet process priors

Kang

Choi

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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Common spatial patterns (CSP) or its probabilistic counterpart, PCSP, is a popular discriminative feature extraction method for electroencephalography (EEG) classification. Models in CSP or PCSP are trained on a subject-by-subject basis so that inter-subject information is not used. In the case of multi-subject EEG classification where brain waves recorded from multiple subjects who undergo the same mental task are available, it is desirable to capture intersubject relatedness in learning a model. In this paper we present a nonparametric Bayesian model for a multi-subject extension of CSP where subject relatedness is captured by assuming that spatial patterns across subjects share a latent subspace. Spatial patterns and the shared latent subspace are jointly learned by variational inference. We use an infinite latent feature model to automatically infer the dimension of the shared latent subspace, placing Indian Buffet process (IBP) priors on our model. Numerical experiments on BCI competition IV 2a dataset demonstrate the high performance of our method, compared to PCSP and existing Bayesian multi-task CSP models.

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