Xinyang Li

Discriminative learning of propagation and spatial pattern for motor imagery eeg analysis

Effective learning and recovery of relevant source brain activity patterns is a major challenge to brain-computer interface using scalp EEG. Various spatial filtering solutions have been developed. Most current methods estimate an instantaneous demixing with the assumption of uncorrelatedness of the source signals. However, recent evidence in neuroscience suggests that multiple brain regions cooperate, especially during motor imagery, a major modality of brain activity for brain-computer interface. In this sense, methods that assume uncorrelatedness of the sources become inaccurate. Therefore, we are promoting a new methodology that considers both volume conduction effect and signal propagation between multiple brain regions. Specifically, we propose a novel discriminative algorithm for joint learning of propagation and spatial pattern with an iterative optimization solution. To validate the new methodology, we conduct experiments involving 16 healthy subjects and perform numerical analysis of the proposed algorithm for EEG classification in motor imagery brain-computer interface. Results from extensive analysis validate the effectiveness of the new methodology with high statistical significance.

Adaptation of motor imagery EEG classification model based on tensor decomposition

OBJECTIVE Session-to-session nonstationarity is inherent in brain-computer interfaces based on electroencephalography. The objective of this paper is to quantify the mismatch between the training model and test data caused by nonstationarity and to adapt the model towards minimizing the mismatch. APPROACH We employ a tensor model to estimate the mismatch in a semi-supervised manner, and the estimate is regularized in the discriminative objective function. MAIN RESULTS The performance of the proposed adaptation method was evaluated on a dataset recorded from 16 subjects performing motor imagery tasks on different days. The classification results validated the advantage of the proposed method in comparison with other regularization-based or spatial filter adaptation approaches. Experimental results also showed that there is a significant correlation between the quantified mismatch and the classification accuracy. SIGNIFICANCE The proposed method approached the nonstationarity issue from the perspective of data-model mismatch, which is more direct than data variation measurement. The results also demonstrated that the proposed method is effective in enhancing the performance of the feature extraction model.

Discriminative Ocular Artifact Correction for Feature Learning in EEG Analysis

Electrooculogram (EOG) artifact contamination is a common critical issue in general electroencephalogram (EEG) studies as well as in brain–computer interface (BCI) research. It is especially challenging when dedicated EOG channels are unavailable or when there are very few EEG channels available for independent component analysis based ocular artifact removal. It is even more challenging to avoid loss of the signal of interest during the artifact correction process, where the signal of interest can be multiple magnitudes weaker than the artifact. To address these issues, we propose a novel discriminative ocular artifact correction approach for feature learning in EEG analysis. Without extra ocular movement measurements, the artifact is extracted from raw EEG data, which is totally automatic and requires no visual inspection of artifacts. Then, artifact correction is optimized jointly with feature extraction by maximizing oscillatory correlations between trials from the same class and minimizing them between trials from different classes. We evaluate this approach on a real-world EEG dataset comprising 68 subjects performing cognitive tasks. The results showed that the approach is capable of not only suppressing the artifact components but also improving the discriminative power of a classifier with statistical significance. We also demonstrate that the proposed method addresses the confounding issues induced by ocular movements in cognitive EEG study.Electrooculogram (EOG) artifact contamination is a common critical issue in general electroencephalogram (EEG) studies as well as in brain-computer interface (BCI) research. It is especially challenging when dedicated EOG channels are unavailable or when there are very few EEG channels available for independent component analysis based ocular artifact removal. It is even more challenging to avoid loss of the signal of interest during the artifact correction process, where the signal of interest can be multiple magnitudes weaker than the artifact. To address these issues, we propose a novel discriminative ocular artifact correction approach for feature learning in EEG analysis. Without extra ocular movement measurements, the artifact is extracted from raw EEG data, which is totally automatic and requires no visual inspection of artifacts. Then, artifact correction is optimized jointly with feature extraction by maximizing oscillatory correlations between trials from the same class and minimizing them between trials from different classes. We evaluate this approach on a real-world EEG dataset comprising 68 subjects performing cognitive tasks. The results showed that the approach is capable of not only suppressing the artifact components but also improving the discriminative power of a classifier with statistical significance. We also demonstrate that the proposed method addresses the confounding issues induced by ocular movements in cognitive EEG study.

Spatial filter adaptation based on geodesic-distance for motor EEG classification

The non-stationarity inherent across sessions recorded on different days poses a major challenge for practical electroencephalography (EEG)-based Brain Computer Interface (BCI) systems. To address this issue, the computational model trained using the training data needs to adapt to the data from the test sessions. In this paper, we propose a novel approach to compute the variations between labelled training data and a batch of unlabelled test data based on the geodesic-distance of the discriminative subspaces of EEG data on the Grassmann manifold. Subsequently, spatial filters can be updated and features that are invariant against such variations can be obtained using a subset of training data that is closer to the test data. Experimental results show that the proposed adaptation method yielded improvements in classification performance.

Joint spatial-temporal filter design for analysis of motor imagery EEG

This paper addresses the key issue of discriminative feature extraction of electroencephalogram (EEG) signals in brain-computer interfaces. Recent advances in neuroscience indicate that multiple brain regions can be activated during motor imagery. The signal propagation among the regions can give rise to spurious effects in identifying event-related desynchronization/synchronization for discriminative motor imagery detection in conventional feature extraction methods. Particularly, we propose that computational models which account for both signal propagation and volume conduction effects of the source neuronal activities can more accurately describe EEG during the specific brain activities and lead to more effective feature extraction. To this end, we devise a unified model for joint learning of signal propagation and spatial patterns. The preliminary results obtained with real-world motor imagery EEG data sets confirm that the new methodology can improve classification accuracy with statistical significance.

Connectivity pattern modeling of motor imagery EEG

In this paper, the functional connectivity network of motor imagery based on EEG is investigated to understand brain function during motor imagery. In particular, partial directed coherence and directed transfer function measurements are applied to multi-channel EEG data to find out event related connectivity pattern with the direction and strength. The t-test is applied to these connectivity measurements to compare the network between motor imagery and the rest state. The possible relationship between this connectivity pattern and subjects performances are discussed. Based on the Granger causality analysis, a feature extraction method is proposed to compensate for nonstationarity in data. By attenuating the time-lagged correlation, this feature extraction method based on the multi-variate autoregression model is proposed to reduce the effects of noises caused by time propagation. The validity of the proposed method is verified through experimental studies with a two-class dataset, and significant improvement in term of classification accuracy is achieved.

Spatial filter subspace optimization based on mutual information

Discriminating EEG signals between different motor imagery states is an important application of brain computer interface (BCI). However, low signal-to-noise ratio and significant data variation of EEG make it very difficult for BCI to obtain reliable results. Spatial filtering is one of the most successful feature extraction methods, and many efforts have been made to construct spatial filters that are robust against the data nonstationarity. In this paper, we propose a novel spatial filter optimization method based on mutual information which is estimated by Gaussian functions instead of Parzen window. We analyze the relationship between the mutual information and feature distance using a simulation study to show that optimization based on mutual information can contribute to feature stationarity. Moreover, we also evaluate the proposed method on a real world motor imagery EEG data set recorded from 16 subjects performing motor imagery or staying in idle state. The experimental results validate the effectiveness of the proposed spatial filter optimization method as it outperforms both the common spatial pattern analysis and filter-bank common spatial pattern analysis.

An ocular artefacts correction method for discriminative EEG analysis based on logistic regression

Electrooculogram (EOG) contamination is a common critical issue in general EEG studies as well as in building highperformance brain computer interfaces (BCI). Existing regression or independent component analysis based artefacts correction methods are usually not applicable when EOG is not available or when there are very few EEG channels. In this paper, we propose a novel ocular artefacts correction method for processing EEG without using dedicated EOG channels. The method constructs estimate of ocular components through artefacts detection in EEG. Then, an optimization based on logistic regression is introduced to remove the components from EEG. Specifically, the optimization ensures that the discriminative information is maintained in the corrected EEG signals. The proposed method is offline evaluated with a large EEG data set containing 68 subjects. Experimental results show that, through the artefacts removal correction by the proposed method, EEG classification accuracy can be improved with statistical significance.

Spatial Filter Adaptation Based on the Divergence Framework for Motor Imagery EEG Classification

To address the nonstationarity issue in EEG-based brain computer interface (BCI), the computational model trained using the training data needs to adapt to the data from the test sessions. In this paper, we propose a novel adaptation approach based on the divergence framework. Cross-session changes can be taken into consideration by searching the discriminative subspaces for test data on the manifold of orthogonal matrices in a semi-supervised manner. Subsequently, the feature space becomes more consistent across sessions and classifiers performance can be enhanced. Experimental results show that the proposed adaptation method yields improvements in classification performance.To address the nonstationarity issue in EEG-based brain computer interface (BCI), the computational model trained using the training data needs to adapt to the data from the test sessions. In this paper, we propose a novel adaptation approach based on the divergence framework. Cross-session changes can be taken into consideration by searching the discriminative subspaces for test data on the manifold of orthogonal matrices in a semi-supervised manner. Subsequently, the feature space becomes more consistent across sessions and classifiers performance can be enhanced. Experimental results show that the proposed adaptation method yields improvements in classification performance.

Spatial filter design based on re-estimated projection matrices

In this paper, motor imagery electroencephalograph classification problem is investigated and a method which modifies the projection matrix is proposed based on common spatial pattern analysis. Exceptional samples are detected through examining the features generated by the projection matrix in the first place, which are special in terms that the projection matrix in common spatial pattern analysis fails to extract discriminant features from them. Projection matrices for exceptional trials are re-estimated and integrated together to form the final projection model. Based on this integrated model, feature extraction is carried out and classification follows by employing support vector machine. The validity of the proposed method is verified through experiment studies. Two data sets that consist of two classes are used, and results show that the proposed method generates more discriminant features.