2,113 research outputs found
Covariate shift detection-based nonstationary adaptation in motor-imagery-based brain–computer interface
Nonstationary learning refers to the process that can learn patterns from data, adapt to shifts, and improve performance of the system with its experience while operating in the nonstationary environments (NSEs). Covariate shift (CS) presents a major challenge during data processing within NSEs wherein the input-data distribution shifts during transitioning from training to testing phase. CS is one of the fundamental issues in electroencephalogram (EEG)-based brain-computer interface (BCI) systems and can be often observed during multiple trials of EEG data recorded over different sessions. Thus, conventional learning algorithms struggle to accommodate these CSs in streaming EEG data resulting in low performance (in terms of classification accuracy) of motor imagery (MI)-related BCI systems. This chapter aims to introduce a novel framework for nonstationary adaptation in MI-related BCI system based on CS detection applied to the temporal and spatial filtered features extracted from raw EEG signals. The chapter collectively provides an efficient method for accounting nonstationarity in EEG data during learning in NSEs
Online Covariate Shift Detection based Adaptive Brain-Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation
A major issue in electroencephalogram (EEG) based brain-computer interfaces (BCIs) is the intrinsic non-stationarities 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 CSD-based 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
An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams
Existing FNNs are mostly developed under a shallow network configuration
having lower generalization power than those of deep structures. This paper
proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be
automatically extracted from data streams or removed if they play limited role
during their lifespan. The structure of the network can be deepened on demand
by stacking additional layers using a drift detection method which not only
detects the covariate drift, variations of input space, but also accurately
identifies the real drift, dynamic changes of both feature space and target
space. DEVFNN is developed under the stacked generalization principle via the
feature augmentation concept where a recently developed algorithm, namely
gClass, drives the hidden layer. It is equipped by an automatic feature
selection method which controls activation and deactivation of input attributes
to induce varying subsets of input features. A deep network simplification
procedure is put forward using the concept of hidden layer merging to prevent
uncontrollable growth of dimensionality of input space due to the nature of
feature augmentation approach in building a deep network structure. DEVFNN
works in the sample-wise fashion and is compatible for data stream
applications. The efficacy of DEVFNN has been thoroughly evaluated using seven
datasets with non-stationary properties under the prequential test-then-train
protocol. It has been compared with four popular continual learning algorithms
and its shallow counterpart where DEVFNN demonstrates improvement of
classification accuracy. Moreover, it is also shown that the concept drift
detection method is an effective tool to control the depth of network structure
while the hidden layer merging scenario is capable of simplifying the network
complexity of a deep network with negligible compromise of generalization
performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System
Deep Learning based Prediction of EEG Motor Imagery of Stroke Patients' for Neuro-Rehabilitation Application
Due to the non-stationary nature of electroencephalography (EEG) signals, a Brain-computer Interfacing (BCI) system requires frequent calibration. This leads to intersession inconsistency which is one of the main reason that impedes the widespread adoption of non-invasive BCI for realworld applications, especially in rehabilitation and medicine. Domain adaptation and deep learning-based techniques have gained relevance in designing calibration-free BCIs to solve this issue. EEGNet is one such deep net architecture that has been successful in performing inter-subject classification, albeit on data from healthy participants. This is the first paper, which tests the performance of EEGNet on data obtained from 10 hemiparetic stroke patients while performing left and right motor imagery tasks. Results obtained on implementing EEGNet have been promising and it has comparably good performance as from expensive feature engineering-based approaches for both withinsubject and cross-subject classification. The less dependency on feature engineering techniques and the ability to extract generalized features for inter-subject classification makes EEGNet a promising deep-learning architecture for developing practically feasible solutions for BCI based neuro-rehabilitation applications
- …