A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface.

Recent advancements in magnetoencephalography (MEG)-based brain-computer interfaces (BCIs) have shown great potential. However, the performance of current MEG-BCI systems is still inadequate and one of the main reasons for this is the unavailability of open-source MEG-BCI datasets. MEG systems are expensive and hence MEG datasets are not readily available for researchers to develop effective and efficient BCI-related signal processing algorithms. In this work, we release a 306-channel MEG-BCI data recorded at 1KHz sampling frequency during four mental imagery tasks (i.e. hand imagery, feet imagery, subtraction imagery, and word generation imagery). The dataset contains two sessions of MEG recordings performed on separate days from 17 healthy participants using a typical BCI imagery paradigm. The current dataset will be the only publicly available MEG imagery BCI dataset as per our knowledge. The dataset can be used by the scientific community towards the development of novel pattern recognition machine learning methods to detect brain activities related to motor imagery and cognitive imagery tasks using MEG signals.

Assessing impact of channel selection on decoding of motor and cognitive imagery from MEG data.

OBJECTIVE: Magnetoencephalography (MEG) based brain-computer interface (BCI) involves a large number of sensors allowing better spatiotemporal resolution for assessing brain activity patterns. There have been many efforts to develop BCI using MEG with high accuracy, though an increase in the number of channels (NoC) means an increase in computational complexity. However, not all sensors necessarily contribute significantly to an increase in classification accuracy (CA) and specifically in the case of MEG-based BCI no channel selection methodology has been performed. Therefore, this study investigates the effect of channel selection on the performance of MEG-based BCI. APPROACH: MEG data were recorded for two sessions from 15 healthy participants performing motor imagery, cognitive imagery and a mixed imagery task pair using a unique paradigm. Performance of four state-of-the-art channel selection methods (i.e. Class-Correlation, ReliefF, Random Forest, and Infinite Latent Feature Selection were applied across six binary tasks in three different frequency bands) were evaluated in this study on two state-of-the-art features, i.e. bandpower and common spatial pattern (CSP). MAIN RESULTS: All four methods provided a statistically significant increase in CA compared to a baseline method using all gradiometer sensors, i.e. 204 channels with band-power features from alpha (8-12 Hz), beta (13-30 Hz), or broadband (α + ß) (8-30 Hz). It is also observed that the alpha frequency band performed better than the beta and broadband frequency bands. The performance of the beta band gave the lowest CA compared with the other two bands. Channel selection improved accuracy irrespective of feature types. Moreover, all the methods reduced the NoC significantly, from 204 to a range of 1-25, using bandpower as a feature and from 15 to 105 for CSP. The optimal channel number also varied not only in each session but also for each participant. Reducing the NoC will help to decrease the computational cost and maintain numerical stability in cases of low trial numbers. SIGNIFICANCE: The study showed significant improvement in performance of MEG-BCI with channel selection irrespective of feature type and hence can be successfully applied for BCI applications.

Brain-Machine Interface-Driven Post-Stroke Upper-Limb Functional Recovery Correlates With Beta-Band Mediated Cortical Networks.

Brain-machine interface (BMI)-driven robot-assisted neurorehabilitation intervention has demonstrated improvement in upper-limb (UL) motor function, specifically, with post-stroke hemiparetic patients. However, neurophysiological patterns related to such interventions are not well understood. This paper examined the longitudinal changes in band-limited resting-state (RS) functional connectivity (FC) networks in association with post-stroke UL functional recovery achieved by a multimodal intervention involving motor attempt (MA)-based BMI and robotic hand-exoskeleton. Four adults were rehabilitated with the intervention for a period lasting up to six weeks. RS magnetoencephalography (MEG) signals, Action Research Arm Test (ARAT), and grip strength (GS) measures were recorded at five equispaced sessions over the intervention period. An average post-interventional increase of 100.0% (p=0.00028) and 88.0% was attained for ARAT and GS, respectively. A cluster-based statistical test involving correlation estimates between beta-band (15-26 Hz) RS-MEG FCs and UL functional recovery provided the positively correlated sub-networks in both the contralesional and ipsilesional motor cortices. The frontoparietal FC exhibited hemispheric lateralization wherein the majority of the positively and negatively correlated connections were found in contralesional and ipsilesional hemispheres, respectively. Our findings are consistent with the theory of bilateral motor cortical association with UL recovery and predict novel FC patterns that can be important for higher level cognitive functions.

Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain-computer interface.

The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based brain-computer interface (BCI) a dynamic system, thus improving its performance is a challenging task. In addition, it is well-known that due to non-stationarity based covariate shifts, the input data distributions of EEG-based BCI systems change during inter- and intra-session transitions, which poses great difficulty for developments of online adaptive data-driven systems. Ensemble learning approaches have been used previously to tackle this challenge. However, passive scheme based implementation leads to poor efficiency while increasing high computational cost. This paper presents a novel integration of covariate shift estimation and unsupervised adaptive ensemble learning (CSE-UAEL) to tackle non-stationarity in motor-imagery (MI) related EEG classification. The proposed method first employs an exponentially weighted moving average model to detect the covariate shifts in the common spatial pattern features extracted from MI related brain responses. Then, a classifier ensemble was created and updated over time to account for changes in streaming input data distribution wherein new classifiers are added to the ensemble in accordance with estimated shifts. Furthermore, using two publicly available BCI-related EEG datasets, the proposed method was extensively compared with the state-of-the-art single-classifier based passive scheme, single-classifier based active scheme and ensemble based passive schemes. The experimental results show that the proposed active scheme based ensemble learning algorithm significantly enhances the BCI performance in MI classifications.

Classification of propofol-induced sedation states using brain connectivity analysis.

connectivity measurements can provide key information about ongoing brain processes. In this paper, we propose to investigate the performance of the binary classification of Propofol-induced sedation states using partial granger causality analysis. Based on the brain connectivity measurements obtained from EEG signals in a database that contains four sedation states: baseline, mild, moderate, and recovery, we consider eight sensors and evaluate the area under the ROC curve with five classifiers: the k-nearest neighbor (density method), support vector machine, linear discriminant analysis, Bayesian discriminant analysis, and a model based on extreme learning machine. The results support the conclusion that the different Propofol-induced sedation states can be identified with an AUC of around 0.75, by considering signal segments of only 4 second. These results highlight the discriminant power that can be obtained from scalp level connectivity measures for online brain monitoring.

Current source density estimates improve the discriminability of scalp-level brain connectivity features related to motor-imagery tasks.

Recent progress in the number of studies involving brain connectivity analysis of motor imagery (MI) tasks for brain-computer interface (BCI) systems has warranted the need for pre-processing methods. The objective of this study is to evaluate the impact of current source density (CSD) estimation from raw electroencephalogram (EEG) signals on the classification performance of scalp level brain connectivity feature based MI-BCI. In particular, time-domain partial Granger causality (PGC) method was implemented on the raw EEG signals and CSD signals of a publicly available dataset for the estimation of brain connectivity features. Moreover, pairwise binary classifications of four different MI tasks were performed in inter-session and intra-session conditions using a support vector machine classifier. The results showed that CSD provided a statistically significant increase of the AUC: 20.28% in the inter-session condition; 12.54% and 13.92% with session 01 and session 02, respectively, in the intra-session condition. These results show that pre-processing of EEG signals is crucial for single-trial connectivity features based MI-BCI systems and CSD can enhance their overall performance.

Propofol-induced sedation diminishes the strength of frontal-parietal-occipital EEG network.

The level of conscious experience can be effectively and reversibly altered by the administration of sedative agents. Several studies attempted to explore the variations in frontal-parietal network during propofol-induced sedation. However, contradictory outcomes warrant further investigations. In this study, we implemented the Neural Gas algorithm-based delay symbolic transfer entropy (NG-dSTE) for investigation of frontal-parietal-occipital (F-P-O) network using scalp EEG signals recorded during altered levels of consciousness. Our results show significant disruption of the F-P-O network during mild and moderate levels of propofol sedation. In particular, the interaction between frontal and parietal-occipital region is highly disturbed. Moreover, we found measurable effect of sedation on local interactions in the frontal network whereas parietal-occipital network experienced least variations. The results support the conclusion that the connectivity based features can be utilized as reliable biomarker for assessment of sedation levels effectively.

Current Source Density Estimation Enhances the Performance of Motor-Imagery-Related Brain-Computer Interface.

The objective is to evaluate the impact of EEG referencing schemes and spherical surface Laplacian (SSL) methods on the classification performance of motor-imagery (MI)-related brain-computer interface systems. Two EEG referencing schemes: common referencing and common average referencing and three surface Laplacian methods: current source density (CSD), finite difference method, and SSL using realistic head model were implemented separately for pre-processing of the EEG signals recorded at the scalp. A combination of filter bank common spatial filter for features extraction and support vector machine for classification was used for both pairwise binary classifications and four-class classification of MI tasks. The study provides three major outcomes: 1) the CSD method performs better than CR, providing a significant improvement of 3.02% and 5.59% across six binary classification tasks and four-class classification task, respectively; 2) the combination of a greater number of channels at the pre-processing stage as compared with the feature extraction stage yields better classification accuracies for all the Laplacian methods; and 3) the efficiency of all the surface Laplacian methods reduced significantly in the case of a fewer number of channels considered during the pre-processing.

Single-trial effective brain connectivity patterns enhance discriminability of mental imagery tasks.

OBJECTIVE: The majority of the current approaches of connectivity based brain-computer interface (BCI) systems focus on distinguishing between different motor imagery (MI) tasks. Brain regions associated with MI are anatomically close to each other, hence these BCI systems suffer from low performances. Our objective is to introduce single-trial connectivity feature based BCI system for cognition imagery (CI) based tasks wherein the associated brain regions are located relatively far away as compared to those for MI. APPROACH: We implemented time-domain partial Granger causality (PGC) for the estimation of the connectivity features in a BCI setting. The proposed hypothesis has been verified with two publically available datasets involving MI and CI tasks. MAIN RESULTS: The results support the conclusion that connectivity based features can provide a better performance than a classical signal processing framework based on bandpass features coupled with spatial filtering for CI tasks, including word generation, subtraction, and spatial navigation. These results show for the first time that connectivity features can provide a reliable performance for imagery-based BCI system. SIGNIFICANCE: We show that single-trial connectivity features for mixed imagery tasks (i.e. combination of CI and MI) can outperform the features obtained by current state-of-the-art method and hence can be successfully applied for BCI applications.