Weighted transfer learning for improving motor imagery-based brain-computer interface

One of the major limitations of motor imagery (MI)-based brain-computer interface (BCI) is its long calibration time. Due to between sessions/subjects variations in the properties of brain signals, typically a large amount of training data needs to be collected at the beginning of each session to calibrate the parameters of the BCI system for the target user. In this paper, we propose a novel transfer learning approach on the classification domain to reduce the calibration time without sacrificing the classification accuracy of MI-BCI. Thus, when only few subject-specific trials are available for training, the estimation of the classification parameters is improved by incorporating previously recorded data from other users. For this purpose, a regularization parameter is added to the objective function of the classifier to make the classification parameters as close as possible to the classification parameters of the previous users who have feature spaces similar to that of the target subject. In this study, a new similarity measure based on the kullback leibler divergence (KL) is used to measure similarity between two feature spaces obtained using subject-specific common spatial patterns (CSP). The proposed transfer learning approach is applied on the logistic regression classifier and evaluated using three datasets. The results showed that compared to the subject-specific classifier, the proposed weighted transfer learning classifier improved the classification results particularly when few subject-specific trials were available for training (p<0.05). Importantly, this improvement was more pronounced for users with medium and poor accuracy. Moreover, the statistical results showed that the proposed weighted transfer learning classifier performed significantly better than the considered comparable baseline algorithms

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

Brain–computer interfacing under distraction: an evaluation study

Objective. While motor-imagery based brain–computer interfaces (BCIs) have been studied over many years by now, most of these studies have taken place in controlled lab settings. Bringing BCI technology into everyday life is still one of the main challenges in this field of research. Approach. This paper systematically investigates BCI performance under 6 types of distractions that mimic out-of-lab environments. Main results. We report results of 16 participants and show that the performance of the standard common spatial patterns (CSP) + regularized linear discriminant analysis classification pipeline drops significantly in this 'simulated' out-of-lab setting. We then investigate three methods for improving the performance: (1) artifact removal, (2) ensemble classification, and (3) a 2-step classification approach. While artifact removal does not enhance the BCI performance significantly, both ensemble classification and the 2-step classification combined with CSP significantly improve the performance compared to the standard procedure. Significance. Systematically analyzing out-of-lab scenarios is crucial when bringing BCI into everyday life. Algorithms must be adapted to overcome nonstationary environments in order to tackle real-world challenges.BMBF, 01GQ1115, Adaptive Gehirn-Computer-Schnittstellen (BCI) in nichtstationären Umgebunge

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

Novel Transfer Learning Approaches forImproving Brain Computer Interfaces

Despite several recent advances, most of the electroencephalogram(EEG)-based brain-computer interface (BCI) applications are still limited to the laboratory due to their long calibration time. Due toconsiderable inter-subject/inter-session and intra-session variations, atime-consuming and fatiguing calibration phase is typically conductedat the beginning of each new session to acquire sufficient labelled train-ing data to train the subject-specific BCI model.This thesis focuses on developing reliable machine learning algorithmsand approaches that reduce BCI calibration time while keeping accu-racy in an acceptable range. Calibration time could be reduced viatransfer learning approaches where data from other sessions or sub-jects are mined and used to compensate for the lack of labelled datafrom the current user or session. In BCI, transfer learning can beapplied on either raw EEG, feature or classification domains.In this thesis, firstly, a novel weighted transfer learning approach isproposed in the classification domain to improve the MI-based BCIperformance when only few subject-specific trials are available fortraining.Transfer learning techniques should be applied in a different domainbefore the classification domain to improve the classification accuracyfor subjects whom their subject-specific features for different classesare not separable. Thus, secondly, this thesis proposes a novel regu-larized common spatial patterns framework based on dynamic timewarping and transfer learning (DTW-R-CSP) in raw EEG and featuredomains.In previous transfer learning approaches, it is hypothesised that thereare enough labelled trials available from the previous subjects or ses-sions. However, in the case when there are no labelled trials available from other subjects or sessions, domain adaptation transfer learningcould potentially mitigate problems of having small training size byreducing variations between the testing and training trials. Thus, todeal with non-stationarity between training and testing trials, a novelensemble adaptation framework with temporal alignment is proposed