Dynamically weighted ensemble classification for non-stationary EEG processing

Liyanage, S.R.; Guan, Cuntai; Zhang, Haihong; Ang, Kai Keng; Xu, Jian‐Xin; Lee, Tong Heng

doi:10.1088/1741-2560/10/3/036007

Cited by 65 publications

(58 citation statements)

References 30 publications

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“…In contrast, an active approach based non-stationary learning method in BCI system may be a possible option that uses a CSD test to detect the presence of covariate shifts in the streaming EEG features, and based upon the detected shift, an adaptive action is initiated. In our previous work [16], we have implemented the active approach in single-trial EEG classification in offline mode and the performance of the system was superior to existing passive approach based BCI systems [32], [17], [29], [18], [33], [34]. However, improving the accuracy of the online BCI system for the neurorehabilitation purpose is still an open challenge.…”

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confidence: 99%

Online Covariate Shift Detection-Based Adaptive Brain–Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation

Chowdhury

Raza

Meena

et al. 2018

IEEE Trans. Cogn. Dev. Syst.

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Abstract-A major issue in electroencephalogram (EEG) based brain-computer interfaces (BCIs) is the intrinsic nonstationarities in the brain waves, which may degrade the performance of the classifier, while transitioning from calibration to feedback generation phase. The non-stationary nature of the EEG data may cause its input probability distribution to vary over time, which often appear as a covariate shift. To adapt to the covariate shift, we had proposed an adaptive learning method in our previous work and tested it on offline standard datasets. This paper presents an online BCI system using previously developed covariate shift detection (CSD)-based adaptive classifier to discriminate between mental tasks and generate neurofeedback in the form of visual and exoskeleton motion. The CSD test helps prevent unnecessary retraining of the classifier. The feasibility of the developed online-BCI system was first tested on 10 healthy individuals, and then on 10 stroke patients having hand disability. A comparison of the proposed online CSD-based adaptive classifier with conventional non-adaptive classifier has shown a significantly (p ≤ 0.01) higher classification accuracy in both the cases of healthy and patient groups. The results demonstrate that the online CSDbased adaptive BCI system is superior to the non-adaptive BCI system and it is feasible to be used for actuating hand exoskeleton for the stroke-rehabilitation applications.

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confidence: 99%

Online Covariate Shift Detection-Based Adaptive Brain–Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation

Chowdhury

Raza

Meena

et al. 2018

IEEE Trans. Cogn. Dev. Syst.

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“…Other approaches (Barachant et al, 2010(Barachant et al, , 2012 omit spatial filtering and directly perform classification on the manifold of covariance matrices. Ensemble classification methods have also been used for nonstationary EEG processing (Liyanage et al, 2013).…”

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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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“…To cope up with this issue, several non-stationary learning algorithms have been proposed in the literature. Non-stationary BCI learning algorithms include co-adaptation [1], covariate shift-adaptation using density ratio estimation approach [2], bias adaptation [6], dynamically weighted ensemble classification [16], principal component based covariate shift-adaptation [17], covariate shift-minimization [11], and unsupervised short-term covariate shift-minimization [18]. Most of the methods are focused on …”

Section: B Non-stationarity In Bcimentioning

confidence: 99%

Covariate shift-adaptation using a transductive learning model for handling non-stationarity in EEG based brain-computer interfaces

Raza

Prasad

et al. 2014

2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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A major challenge to devising robust braincomputer interfaces (BCIs) based on electroencephalogram (EEG) data is the immanent non-stationary characteristics of EEG signals. Statistical properties of the signals may shift during interor-intra session transfers that often leads to deteriorated BCI performance. The shift in the input data distribution from training to testing phase is called a covariate shift. It can be caused by various reasons such as different electrode placements, varying impedances and other ongoing brain activities. We propose an algorithm to handle this issue by adapting to the covariate shifts in the EEG data using a transductive learning approach. The performance of the proposed method is evaluated on the BCI competition 2008-Graz dataset B. The results show an improvement in classification accuracy of the BCI system over a traditional learning method. The obtained results support the conclusion that covariate-shift-adaptation using transductive learning is helpful to realize adaptive BCI systems.

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Dynamically weighted ensemble classification for non-stationary EEG processing

Abstract: The cluster based analysis provides insight into session-to-session non-stationarity in EEG data. The results demonstrate the effectiveness of the proposed method in addressing non-stationarity in EEG data for the operation of a BCI.

Cited by 65 publications

References 30 publications

Online Covariate Shift Detection-Based Adaptive Brain–Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation

Online Covariate Shift Detection-Based Adaptive Brain–Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation

On robust spatial filtering of EEG in nonstationary environments

Covariate shift-adaptation using a transductive learning model for handling non-stationarity in EEG based brain-computer interfaces

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