Motor Imagery Tasks Research Articles

Analysis of Minimal Channel Electroencephalography for Wearable Brain–Computer Interface

Electroencephalography (EEG)-based brain—computer interface (BCI) is a non-invasive technology with potential in various healthcare applications, including stroke rehabilitation and neuro-feedback training. These applications typically require multi-channel EEG. However, setting up a multi-channel EEG headset is time-consuming, potentially resulting in patient reluctance to use the system despite its potential benefits. Therefore, we investigated the appropriate number of electrodes required for a successful BCI application in wearable devices using various numbers of EEG channels. EEG multi-frequency features were extracted using the “filter bank” feature extraction technique. A support vector machine (SVM) was used to classify a left/right-hand opening/closing motor imagery (MI) task. Nine electrodes around the center of the scalp (F3, Fz, F4, C3, Cz, C4, P3, Pz, and P4) provided high classification accuracy with a moderate setup time; hence, this system was selected as the minimal number of required channels. Spherical spline interpolation (SSI) was also applied to investigate the feasibility of generating EEG signals from limited channels on an EEG headset. We found classification accuracies of interpolated groups only, and combined interpolated and collected groups were significantly lower than the measured groups. The results indicate that SSI may not provide additional EEG data to improve classification accuracy of the collected minimal channels. The conclusion is that other techniques could be explored or a sufficient number of EEG channels must be collected without relying on generated data. Our proposed method, which uses a filter bank feature, session-dependent training, and the exploration of many groups of EEG channels, offers the possibility of developing a successful BCI application using minimal channels on an EEG device.

Beta bursts question the ruling power for brain–computer interfaces

Objective: Current efforts to build reliable brain–computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience. Approach: In light of these advances, and particularly the characterization of the burst-like nature of beta frequency band activity and the diversity of beta bursts, we revisit the role of beta activity in ‘left vs. right hand’ motor imagery (MI) tasks. Current decoding approaches for such tasks take advantage of the fact that MI generates time-locked changes in induced power in the sensorimotor cortex and rely on band-passed power changes in single or multiple channels. Although little is known about the dynamics of beta burst activity during MI, we hypothesized that beta bursts should be modulated in a way analogous to their activity during performance of real upper limb movements. Main results and Significance: We show that classification features based on patterns of beta burst modulations yield decoding results that are equivalent to or better than typically used beta power across multiple open electroencephalography datasets, thus providing insights into the specificity of these bio-markers.

Genetic Algorithm-Based Data Optimization for Efficient Transfer Learning in Convolutional Neural Networks: A Brain–Machine Interface Implementation

In brain–machine interface (BMI) systems, the performance of trained Convolutional Neural Networks (CNNs) is significantly influenced by the quality of the training data. Another issue is the training time of CNNs. This paper introduces a novel approach by combining transfer learning and a Genetic Algorithm (GA) to optimize the training data of CNNs. Transfer learning is implemented across different subjects, and the data chosen by GA aim to improve CNN performance. In addition, the GA-selected data shed light on the similarity in brain activity between subjects. Two datasets are used: (1) the publicly available BCI Competition IV, in which the subjects performed motor imagery (MI) tasks, and (2) the dataset created by healthy subjects of our laboratory performing motor movement (MO) tasks. The experimental results indicate that the brain data selected by the GA improve the recognition accuracy of the target CNN (TCNN) using pre-trained base CNN (BCNN). The improvement in accuracy is 11% and 4% for the BCI Competition IV and our laboratory datasets, respectively. In addition, the GA-selected training data reduce the CNN training time. The performance of the trained CNN, utilizing transfer learning, is tested for real-time control of a robot manipulator.

Empowering EEG motor imagery classification with deep transfer learning approach

AbstractThe brain‐computer interface (BCI) enables individuals with impairments to interact with the real world without relying on the neuromuscular pathway. BCI leverages artificial intelligence (AI) models for control. It can capture brain activity patterns associated with mental processes and convert them into commands for actuators. One promising application of BCI is in rehabilitation centres. When compared to traditional methods integrated with machine learning (ML) approaches that were initially explored in the past decade, there is a relatively limited number of studies utilizing deep learning (DL) approaches in BCI applications. A novel approach has been developed for the automatic recognition of motor imagery (MI) tasks. This article introduces the improved sparrow search algorithm with deep transfer learning‐based EEG motor imagery classification (ISSADTL‐EEGMIC) algorithm for BCIs. The presented ISSADTL‐EEGMIC algorithm primarily focuses on the automated classification of motor imagery tasks. To achieve this, the ISSADTL‐EEGMIC method first preprocesses EEG signals using the wavelet packet decomposition (WPD) technique. Additionally, it employs the neural architecture search network (NASNet) technique for feature extraction. Moreover, the ISSADTL‐EEGMIC model performs the classification process using the stacked sparse autoencoder (SSAE) model. Furthermore, hyperparameter optimization of the SSAE model is performed using the ISSA technique, which helps achieve maximum classification performance. The results from the simulation of the ISSADTL‐EEGMIC algorithm have been examined using two benchmark datasets. The experimental findings suggest that it outperforms recent approaches in terms of performance.

Aggregating intrinsic information to enhance BCI performance through federated learning

Insufficient data is a long-standing challenge for Brain–Computer Interface (BCI) to build a high-performance deep learning model. Though numerous research groups and institutes collect a multitude of EEG datasets for the same BCI task, sharing EEG data from multiple sites is still challenging due to the heterogeneity of devices. The significance of this challenge cannot be overstated, given the critical role of data diversity in fostering model robustness. However, existing works rarely discuss this issue, predominantly centering their attention on model training within a single dataset, often in the context of inter-subject or inter-session settings. In this work, we propose a hierarchical personalized Federated Learning EEG decoding (FLEEG) framework to surmount this challenge. This innovative framework heralds a new learning paradigm for BCI, enabling datasets with disparate data formats to collaborate in the model training process. Each client is assigned a specific dataset and trains a hierarchical personalized model to manage diverse data formats and facilitate information exchange. Meanwhile, the server coordinates the training procedure to harness knowledge gleaned from all datasets, thus elevating overall performance. The framework has been evaluated in Motor Imagery (MI) classification with nine EEG datasets collected by different devices but implementing the same MI task. Results demonstrate that the proposed framework can boost classification performance up to 8.4% by enabling knowledge sharing between multiple datasets, especially for smaller datasets. Visualization results also indicate that the proposed framework can empower the local models to put a stable focus on task-related areas, yielding better performance. To the best of our knowledge, this is the first end-to-end solution to address this important challenge.

Revealing brain connectivity: graph embeddings for EEG representation learning and comparative analysis of structural and functional connectivity.

This study employs deep learning techniques to present a compelling approach for modeling brain connectivity in EEG motor imagery classification through graph embedding. The compelling aspect of this study lies in its combination of graph embedding, deep learning, and different brain connectivity types, which not only enhances classification accuracy but also enriches the understanding of brain function. The approach yields high accuracy, providing valuable insights into brain connections and has potential applications in understanding neurological conditions. The proposed models consist of two distinct graph-based convolutional neural networks, each leveraging different types of brain connectivities to enhance classification performance and gain a deeper understanding of brain connections. The first model, Adjacency-based Convolutional Neural Network Model (Adj-CNNM), utilizes a graph representation based on structural brain connectivity to embed spatial information, distinguishing it from prior spatial filtering approaches dependent on subjects and tasks. Extensive tests on a benchmark dataset-IV-2a demonstrate that an accuracy of 72.77% is achieved by the Adj-CNNM, surpassing baseline and state-of-the-art methods. The second model, Phase Locking Value Convolutional Neural Network Model (PLV-CNNM), incorporates functional connectivity to overcome structural connectivity limitations and identifies connections between distinct brain regions. The PLV-CNNM achieves an overall accuracy of 75.10% across the 1-51 Hz frequency range. In the preferred 8-30 Hz frequency band, known for motor imagery data classification (including α, μ, and β waves), individual accuracies of 91.9%, 90.2%, and 85.8% are attained for α, μ, and β, respectively. Moreover, the model performs admirably with 84.3% accuracy when considering the entire 8-30 Hz band. Notably, the PLV-CNNM reveals robust connections between different brain regions during motor imagery tasks, including the frontal and central cortex and the central and parietal cortex. These findings provide valuable insights into brain connectivity patterns, enriching the comprehension of brain function. Additionally, the study offers a comprehensive comparative analysis of diverse brain connectivity modeling methods.

Multiclass classification of motor imagery tasks based on multi-branch convolutional neural network and temporal convolutional network model.

Motor imagery (MI) is a cognitive process wherein an individual mentally rehearses a specific movement without physically executing it. Recently, MI-based brain-computer interface (BCI) has attracted widespread attention. However, accurate decoding of MI and understanding of neural mechanisms still face huge challenges. These seriously hinder the clinical application and development of BCI systems based on MI. Thus, it is very necessary to develop new methods to decode MI tasks. In this work, we propose a multi-branch convolutional neural network (MBCNN) with a temporal convolutional network (TCN), an end-to-end deep learning framework to decode multi-class MI tasks. We first used MBCNN to capture the MI electroencephalography signals information on temporal and spectral domains through different convolutional kernels. Then, we introduce TCN to extract more discriminative features. The within-subject cross-session strategy is used to validate the classification performance on the dataset of BCI Competition IV-2a. The results showed that we achieved 75.08% average accuracy for 4-class MI task classification, outperforming several state-of-the-art approaches. The proposed MBCNN-TCN-Net framework successfully captures discriminative features and decodes MI tasks effectively, improving the performance of MI-BCIs. Our findings could provide significant potential for improving the clinical application and development of MI-based BCI systems.

A time segment adaptive optimization method based on separability criterion and correlation analysis for motor imagery BCIs

Motor imagery (MI) plays a crucial role in brain-computer interface (BCI), and the classification of MI tasks using electroencephalogram (EEG) is currently under extensive investigation. During MI classification, individual differences among subjects in terms of response and time latency need to be considered. Optimizing the time segment for different subjects can enhance subsequent classification performance. In view of the individual differences of subjects in motor imagery tasks, this article proposes a Time Segment Adaptive Optimization method based on Separability criterion and Correlation analysis (TSAOSC). The fundamental principle of this method involves applying the separability criterion to various sizes of time windows within the training data, identifying the optimal raw reference signal, and adaptively adjusting the time segment position for each trial’s data by analyzing its relationship with the optimal reference signal. We evaluated our method on three BCI competition datasets, respectively. The utilization of the TSAOSC method in the experiments resulted in an enhancement of 4.90% in average classification accuracy compared to its absence. Additionally, building upon the TSAOSC approach, this study proposes a Nonlinear-TSAOSC method (N-TSAOSC) for analyzing EEG signals with nonlinearity, which shows improvements in the classification accuracy of certain subjects. The results of the experiments demonstrate that the proposed method is an effective time segment optimization method, and it can be integrated into other algorithms to further improve their accuracy.

Visual contrast sensitivity is associated with community structure integrity in cognitively unimpaired older adults: the Brain Networks and Mobility (B-NET) Study

Older adults with impairment in contrast sensitivity (CS), the ability to visually perceive differences in light and dark, are more likely to demonstrate limitations in mobility function, but the mechanisms underlying this relationship are poorly understood. We sought to determine if functional brain networks important to visual processing and mobility may help elucidate possible neural correlates of this relationship. This cross-sectional analysis utilized functional MRI both at rest and during a motor imagery (MI) task in 192 community-dwelling, cognitively-unimpaired older adults ≥ 70 years of age from the Brain Networks and Mobility study (B-NET). Brain networks were partitioned into network communities, groups of regions that are more interconnected with each other than the rest of the brain, the spatial consistency of the communities for multiple brain subnetworks was assessed. Lower baseline binocular CS was significantly associated with degraded sensorimotor network (SMN) community structure at rest. During the MI task, lower binocular CS was significantly associated with degraded community structure in both the visual (VN) and default mode network (DMN). These findings may suggest shared neural pathways for visual and mobility dysfunction that could be targeted in future studies.

Large-Scale Cortical Network Analysis and Classification of MI-BCI Tasks Based on Bayesian Nonnegative Matrix Factorization.

Motor imagery (MI) is a high-level cognitive process that has been widely applied to clinical rehabilitation and brain-computer interfaces (BCIs). However, the decoding of MI tasks still faces challenges, and the neural mechanisms underlying its application are unclear, which seriously hinders the development of MI-based clinical applications and BCIs. Here, we combined EEG source reconstruction and Bayesian nonnegative matrix factorization (NMF) methods to construct large-scale cortical networks of left-hand and right-hand MI tasks. Compared to right-hand MI, the results showed that the significantly increased functional network connectivities (FNCs) mainly located among the visual network (VN), sensorimotor network (SMN), right temporal network, right central executive network, and right parietal network in the left-hand MI at the β (13-30Hz) and all (8-30Hz) frequency bands. For the network properties analysis, we found that the clustering coefficient, global efficiency, and local efficiency were significantly increased and characteristic path length was significantly decreased in left-hand MI compared to right-hand MI at the β and all frequency bands. These network pattern differences indicated that the left-hand MI may need more modulation of multiple large-scale networks (i.e., VN and SMN) mainly located in the right hemisphere. Finally, based on the spatial pattern network of FNC and network properties, we propose a classification model. The proposed model achieves a top classification accuracy of 78.2% in cross-subject two-class MI-BCI tasks. Overall, our findings provide new insights into the neural mechanisms of MI and a potential network biomarker to identify MI-BCI tasks.

An Adaptive Hybrid Brain Computer Interface for Hand Function Rehabilitation of Stroke Patients.

Motor imagery (MI) based brain computer interface (BCI) has been extensively studied to improve motor recovery for stroke patients by inducing neuroplasticity. However, due to the lower spatial resolution and signal-to-noise ratio (SNR) of electroencephalograph (EEG), MI based BCI system that involves decoding hand movements within the same limb remains lower classification accuracy and poorer practicality. To overcome the limitations, an adaptive hybrid BCI system combining MI and steady-state visually evoked potential (SSVEP) is developed to improve decoding accuracy while enhancing neural engagement. On the one hand, the SSVEP evoked by visual stimuli based on action-state flickering coding approach significantly improves the recognition accuracy compared to the pure MI based BCI. On the other hand, to reduce the impact of SSVEP on MI due to the dual-task interference effect, the event-related desynchronization (ERD) based neural engagement is monitored and employed for feedback in real-time to ensure the effective execution of MI tasks. Eight healthy subjects and six post-stroke patients were recruited to verify the effectiveness of the system. The results showed that the four-class gesture recognition accuracies of healthy individuals and patients could be improved to 94.37 ± 4.77% and 79.38 ± 6.26%, respectively. Moreover, the designed hybrid BCI could maintain the same degree of neural engagement as observed when subjects solely performed MI tasks. These phenomena demonstrated the interactivity and clinical utility of the developed system for the rehabilitation of hand function in stroke patients.

Lumbar extension peak-torque, muscle endurance and motor imagery in junior-elite basketballers with and without a history of low back pain: a pilot study.

Low back pain (LBP) is common in elite athletes. Several peripheral and central factors have been identified to be altered in non-athletic LBP populations, however whether these alterations also exist in elite athletes with LBP is unknown. The aim of this study was to determine whether elite basketballers with a history of persistent LBP perform worse than those without LBP at a lumbar muscle endurance task, a lumbar extension peak-torque task, and a lumbar motor imagery task. An observational pilot study. Twenty junior elite-level male basketballers with (n=11) and without (n=9) a history of persistent LBP were recruited. Athletes completed a lumbar extensor muscle endurance (Biering-Sorensen) task, two lumbar extensor peak-torque (modified Biering-Sorensen) tasks and two motor imagery (left/right lumbar and hand judgement) tasks across two sessions (48 hours apart). Performance in these tasks were compared between the groups with and without a history of LBP. Young athletes with a history of LBP had reduced lumbar extensor muscle endurance (p<0.001), reduced lumbar extension peak-torque (p<0.001), and were less accurate at the left/right lumbar judgement task (p=0.02) but no less accurate at a left/right hand judgement task (p=0.59), than athletes without a history of LBP. Response times for both left/right judgement tasks did not differ between groups (lumbar p=0.24; hand p=0.58). Junior elite male basketballers with a history of LBP demonstrate reduced lumbar extensor muscle endurance and lumbar extension peak-torque and are less accurate at a left/right lumbar rotation judgement task, than those without LBP.

A novel recognition and classification approach for motor imagery based on spatio-temporal features.

Motor imagery, as a paradigm of brainmachine interfaces, holds vast potential in the field of medical rehabilitation. Addressing the challenges posed by the non-stationarity and low signal-to-noise ratio of EEG signals, the effective extraction of features from motor imagery signals for accurate recognition stands as a key focus in motor imagery brain-machine interface technology. This paper proposes a motor imagery EEG signal classification model that combines functional brain networks with graph convolutional networks. First, functional brain networks are constructed using different brain functional connectivity metrics, and graph theory features are calculated to deeply analyze the characteristics of brain networks under different motor tasks. Then, the constructed functional brain networks are combined with graph convolutional networks for the classification and recognition of motor imagery tasks. The analysis based on brain functional connectivity reveals that the functional connectivity strength during the both fists task is significantly higher than that of other motor imagery tasks, and the functional connectivity strength during actual movement is generally superior to that of motor imagery tasks. In experiments conducted on the Physionet public dataset, the proposed model achieved a classification accuracy of 88.39% under multi-subject conditions, significantly outperforming traditional methods. Under single-subject conditions, the model effectively addressed the issue of individual variability, achieving an average classification accuracy of 99.31%. These results indicate that the proposed model not only exhibits excellent performance in the classification of motor imagery tasks but also provides new insights into the functional connectivity characteristics of different motor tasks and their corresponding brain regions.

Deep Neural Network-Based Empirical Mode Decomposition for Motor Imagery EEG Classification.

Motor imagery refers to the brain's response during the mental simulation of physical activities, which can be detected through electroencephalogram (EEG) signals. However, EEG signals exhibit a low signal-to-noise ratio (SNR) due to various artifacts originating from other physiological sources. To enhance the classification performance of motor imagery tasks by increasing the SNR of EEG signals, several signal decomposition approaches have been proposed. Empirical mode decomposition (EMD) has shown promising results in extracting EEG components associated with motor imagery tasks more effectively than traditional linear decomposition algorithms such as Fourier and wavelet methods. Nevertheless, the EMD-based algorithm suffers from a significant challenge known as mode mixing, where frequency components intertwine with the intrinsic mode functions obtained through EMD. This issue severely hampers the accuracy of motor imagery classification. Despite numerous algorithms proposed, mode mixing remains a persistent issue. In this paper, we propose the Deep-EMD algorithm, a deep neural network-based approach to mode mixing problem. We employ two datasets to compare the motor imagery classification and mode mixing improvement achieved by the conventional EMD algorithms. Our experimental results demonstrate that the Deep-EMD algorithm effectively mitigates the mode mixing problem in decomposed EEG components, leading to improved motor imagery classification performance.

Recruiting neural field theory for data augmentation in a motor imagery brain-computer interface.

We introduce a novel approach to training data augmentation in brain-computer interfaces (BCIs) using neural field theory (NFT) applied to EEG data from motor imagery tasks. BCIs often suffer from limited accuracy due to a limited amount of training data. To address this, we leveraged a corticothalamic NFT model to generate artificial EEG time series as supplemental training data. We employed the BCI competition IV '2a' dataset to evaluate this augmentation technique. For each individual, we fitted the model to common spatial patterns of each motor imagery class, jittered the fitted parameters, and generated time series for data augmentation. Our method led to significant accuracy improvements of over 2% in classifying the "total power" feature, but not in the case of the "Higuchi fractal dimension" feature. This suggests that the fit NFT model may more favorably represent one feature than the other. These findings pave the way for further exploration of NFT-based data augmentation, highlighting the benefits of biophysically accurate artificial data.

EEG Characteristic Comparison of Motor Imagery between Supernumerary and Inherent limb: Sixth-finger MI Enhances the ERD Pattern and Classification Performance.

Adding supernumerary robotic limbs (SRLs) to humans and controlling them directly through the brain are main goals for movement augmentation. However, it remains uncertain whether neural patterns different from the traditional inherent limbs motor imagery (MI) can be extracted, which is essential for high-dimensional control of external devices. In this work, we established a MI neo-framework consisting of novel supernumerary robotic sixth-finger MI (SRF-MI) and traditional right-hand MI (RH-MI) paradigms and validated the distinctness of EEG response patterns between two MI tasks for the first time. Twenty-four subjects were recruited for this experiment involving three mental tasks. Event-related spectral perturbation was adopted to supply details about event-related desynchronization (ERD). Activation region, intensity and response time (RT) of ERD were compared between SRF-MI and RH-MI tasks. Three classical classification algorithms were utilized to verify the separability between different mental tasks. And genetic algorithm aims to select optimal combination of channels for neo-framework. A bilateral sensorimotor and prefrontal modulation was found during the SRF-MI task, whereas in RH-MI only contralateral sensorimotor modulation was exhibited. The novel SRF-MI paradigm enhanced ERD intensity by a maximum of 117% in prefrontal area and 188% in the ipsilateral somatosensory-association cortex. And, a global decrease of RT was exhibited during SRF-MI tasks compared to RH-MI. Classification results indicate well separable performance among different mental tasks (88.1% maximum for 2-class and 88.2% maximum for 3-class). This work demonstrated the difference between the SRF-MI and RH-MI paradigms, widening the control bandwidth of the BCI system.

Cross-Subject Motor Imagery Decoding by Transfer Learning of Tactile ERD.

For Brain-Computer Interface (BCI) based on motor imagery (MI), the MI task is abstract and spontaneous, presenting challenges in measurement and control and resulting in a lower signal-to-noise ratio. The quality of the collected MI data significantly impacts the cross-subject calibration results. To address this challenge, we introduce a novel cross-subject calibration method based on passive tactile afferent stimulation, in which data induced by tactile stimulation is utilized to calibrate transfer learning models for cross-subject decoding. During the experiments, tactile stimulation was applied to either the left or right hand, with subjects only required to sense tactile stimulation. Data from these tactile tasks were used to train or fine-tune models and subsequently applied to decode pure MI data. We evaluated BCI performance using both the classical Common Spatial Pattern (CSP) combined with the Linear Discriminant Analysis (LDA) algorithm and a state-of-the-art deep transfer learning model. The results demonstrate that the proposed calibration method achieved decoding performance at an equivalent level to traditional MI calibration, with the added benefit of outperforming traditional MI calibration with fewer trials. The simplicity and effectiveness of the proposed cross-subject tactile calibration method make it valuable for practical applications of BCI, especially in clinical settings.

Paresis of an upper extremity. Action observation and motor imagery in recovery of patients with chronic stroke

Action observation (AO) and motor imagery (MI) are considered functionally equivalent forms of motor representation related to movement execution (ME). Because of their characteristics, AO and MI have been proposed as techniques to facilitate the recovery of post-stroke hemiparesis in the upper extremities. An experimental, longitudinal, prospective, single-blinded design was undertaken. Eleven patients participated, and were randomly assigned to each study group. Both groups received 10 to 12 sessions of physical therapy. Five patients were assigned to the control treatment group, and six patients to the experimental treatment group (AO + MI). All were assessed before and after treatment for function, strength (newtons) and mobility (percentage) in the affected limb, as well as alpha desynchronisation (8-13 Hz) in the supplementary motor area, the premotor cortex and primary motor cortex while performing AO + MI tasks and action observation plus motor execution (AO + ME). The experimental group presented improvement in function and strength. A negative correlation was found between desynchronisation in the supplementary motor area and function, as well as a post-treatment increase in desynchronisation in the premotor cortex of the injured hemisphere in the experimental group only. An AO + MI-based intervention positively impacts recovery of the paretic upper extremity by stimulating the supplementary motor area, a cortex involved in movement preparation and learning. AO + MI therapy can be used as adjunctive treatment in patients with upper extremity paresis following chronic stroke.

Subject-Independent Deep Architecture for EEG-Based Motor Imagery Classification.

Motor imagery (MI) classification based on electroencephalogram (EEG) is a widely-used technique in non-invasive brain-computer interface (BCI) systems. Since EEG recordings suffer from heterogeneity across subjects and labeled data insufficiency, designing a classifier that performs the MI independently from the subject with limited labeled samples would be desirable. To overcome these limitations, we propose a novel subject-independent semi-supervised deep architecture (SSDA). The proposed SSDA consists of two parts: an unsupervised and a supervised element. The training set contains both labeled and unlabeled data samples from multiple subjects. First, the unsupervised part, known as the columnar spatiotemporal auto-encoder (CST-AE), extracts latent features from all the training samples by maximizing the similarity between the original and reconstructed data. A dimensional scaling approach is employed to reduce the dimensionality of the representations while preserving their discriminability. Second, a supervised part learns a classifier based on the labeled training samples using the latent features acquired in the unsupervised part. Moreover, we employ center loss in the supervised part to minimize the embedding space distance of each point in a class to its center. The model optimizes both parts of the network in an end-to-end fashion. The performance of the proposed SSDA is evaluated on test subjects who were not seen by the model during the training phase. To assess the performance, we use two benchmark EEG-based MI task datasets. The results demonstrate that SSDA outperforms state-of-the-art methods and that a small number of labeled training samples can be sufficient for strong classification performance.

Granger Causal Inference Based on Dual Laplacian Distribution and Its Application to MI-BCI Classification.

Granger causality-based effective brain connectivity provides a powerful tool to probe the neural mechanism for information processing and the potential features for brain computer interfaces. However, in real applications, traditional Granger causality is prone to the influence of outliers, such as inevitable ocular artifacts, resulting in unreasonable brain linkages and the failure to decipher inherent cognition states. In this work, motivated by constructing the sparse causality brain networks under the strong physiological outlier noise conditions, we proposed a dual Laplacian Granger causality analysis (DLap-GCA) by imposing Laplacian distributions on both model parameters and residuals. In essence, the first Laplacian assumption on residuals will resist the influence of outliers in electroencephalogram (EEG) on causality inference, and the second Laplacian assumption on model parameters will sparsely characterize the intrinsic interactions among multiple brain regions. Through simulation study, we quantitatively verified its effectiveness in suppressing the influence of complex outliers, the stable capacity for model estimation, and sparse network inference. The application to motor-imagery (MI) EEG further reveals that our method can effectively capture the inherent hemispheric lateralization of MI tasks with sparse patterns even under strong noise conditions. The MI classification based on the network features derived from the proposed approach shows higher accuracy than other existing traditional approaches, which is attributed to the discriminative network structures being captured in a timely manner by DLap-GCA even under the single-trial online condition. Basically, these results consistently show its robustness to the influence of complex outliers and the capability of characterizing representative brain networks for cognition information processing, which has the potential to offer reliable network structures for both cognitive studies and future brain-computer interface (BCI) realization.