Evaluation of EEG Oscillatory Patterns and Cognitive Process during Simple and Compound Limb Motor Imagery

Spatio-temporal filtering has been widely used for extracting discriminative features in the motor imagery-based brain-computer interface (MI-BCI). In order to obtain high performance, the algorithms need to enhance robustness or find class-discriminative bands for the spatial filter. However, the existing methods either cannot derive the spatial and spectral filters with a unique objective function for guaranteeing convergence or rarely consider the combined optimization of spatial-spectral filters and other patterns for enhancing the discrimination. In this study, we present a novel feature extraction method termed Spectrum-weighted Tensor Discriminant Analysis (SwTDA), which optimizes spectral filters along with spatial filters and other associated patterns by tensor-based discriminant analysis. The proposed method considers intrinsic spatial-spectral-temporal information contained by the physiological signal and hence can identify discriminative characteristics robustly. The effectiveness of the algorithm is demonstrated by comparing it with several state-of-the-art methods on two datasets involving 15 different subjects. Results indicate that the SwTDA method yields higher classification accuracies than the competing methods. Furthermore, interpretable spatial-spectral patterns that are determined by the algorithm can be used for further analysis of the MI-based EEG signal. INDEX TERMS Brain computer interface (BCI), spatio-spectral filter, tensor-based discriminant analysis, motor imagery. TONG WANG (Member, IEEE) received the B.Eng. degree in electrical engineering and automation from the Beijing University of Aeronautics and Astronautics (now Beihang University), Beijing, China, in 2006, and the M.Sc. degree (Hons.) in communications engineering and the Ph.D. degree in electronic engineering from the

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“…Compound limb motor imagery (cMI) is a more complex cognitive process than simple limb motor imagery (sMI) [18]. Fig.…”

Section: Results For Dataset Bmentioning

confidence: 99%

Spectrum-Weighted Tensor Discriminant Analysis for Motor Imagery-Based BCI

et al. 2020

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“…This is not unexpected, since connectivity features, in general, have so far shown only moderate success in classification of motor imagery tasks [ 98 , 99 ]. It should be also noted that some effort has been made in analyzing effective networks of compound motor imagery tasks [ 100 ]. Differentiating anatomical levels and consecutive classification should perhaps be better explored along the lines of time-varying connectivity [ 95 , 101 – 103 ] instead of spatial pattern analysis.…”

Section: Discussionmentioning

confidence: 99%

Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury

et al. 2018

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Reciprocal communication of the central and peripheral nervous systems is compromised during spinal cord injury due to neurotrauma of ascending and descending pathways. Changes in brain organization after spinal cord injury have been associated with differences in prognosis. Changes in functional connectivity may also serve as injury biomarkers. Most studies on functional connectivity have focused on chronic complete injury or resting-state condition. In our study, ten right-handed patients with incomplete spinal cord injury and ten age- and gender-matched healthy controls performed multiple visual motor imagery tasks of upper extremities and walking under high-resolution electroencephalography recording. Directed transfer function was used to study connectivity at the cortical source space between sensorimotor nodes. Chronic disruption of reciprocal communication in incomplete injury could result in permanent significant decrease of connectivity in a subset of the sensorimotor network, regardless of positive or negative neurological outcome. Cingulate motor areas consistently contributed the larger outflow (right) and received the higher inflow (left) among all nodes, across all motor imagery categories, in both groups. Injured subjects had higher outflow from left cingulate than healthy subjects and higher inflow in right cingulate than healthy subjects. Alpha networks were less dense, showing less integration and more segregation than beta networks. Spinal cord injury patients showed signs of increased local processing as adaptive mechanism. This trial is registered with NCT02443558.

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“…Eight MI datasets were used in our experiments (Schalk et al, 2004;Leeb et al, 2007;Tangermann et al, 2012;Yi et al, 2014;Zhou et al, 2016;Cho et al, 2017). All datasets are publicly available and details of them are listed in Table 1.…”

Section: Datasetsmentioning

confidence: 99%

Cross-Dataset Variability Problem in EEG Decoding With Deep Learning

et al. 2020

Front. Hum. Neurosci.

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Cross-subject variability problems hinder practical usages of Brain-Computer Interfaces. Recently, deep learning has been introduced into the BCI community due to its better generalization and feature representation abilities. However, most studies currently only have validated deep learning models for single datasets, and the generalization ability for other datasets still needs to be further verified. In this paper, we validated deep learning models for eight MI datasets and demonstrated that the cross-dataset variability problem weakened the generalization ability of models. To alleviate the impact of cross-dataset variability, we proposed an online pre-alignment strategy for aligning the EEG distributions of different subjects before training and inference processes. The results of this study show that deep learning models with online pre-alignment strategies could significantly improve the generalization ability across datasets without any additional calibration data.

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Evaluation of EEG Oscillatory Patterns and Cognitive Process during Simple and Compound Limb Motor Imagery

Cited by 61 publications

References 29 publications

Spectrum-Weighted Tensor Discriminant Analysis for Motor Imagery-Based BCI

Spectrum-Weighted Tensor Discriminant Analysis for Motor Imagery-Based BCI

Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury

Cross-Dataset Variability Problem in EEG Decoding With Deep Learning

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