Brain-computer Interface Classification Research Articles

Convolutional Transformer-Based Cross Subject Model for SSVEP-Based BCI Classification.

Steady-state visual evoked potential (SSVEP) is a commonly used brain-computer interface (BCI) paradigm. The performance of cross-subject SSVEP classification has a strong impact on SSVEP-BCI. This study designed a cross subject generalization SSVEP classification model based on an improved transformer structure that uses domain generalization (DG). The global receptive field of multi-head self-attention is used to learn the global generalized SSVEP temporal information across subjects. This is combined with a parallel local convolution module, designed to avoid oversmoothing the oscillation characteristics of temporal SSVEP data and better fit the feature. Moreover, to improve the cross-subject calibration-free SSVEP classification performance, an DG method named StableNet is combined with the proposed convolutional transformer structure to form the DG-Conformer method, which can eliminate spurious correlations between SSVEP discriminative information and background noise to improve cross-subject generalization. Experiments on two public datasets, Benchmark and BETA, demonstrated the outstanding performance of the proposed DG-Conformer compared with other calibration-free methods, FBCCA, tt-CCA, Compact-CNN, FB-tCNN, and SSVEPNet. Additionally, DG-Conformer outperforms the classic calibration-required algorithms eCCA, eTRCA and eSSCOR when calibration is used. An incomplete partial stimulus calibration scheme was also explored on the Benchmark dataset, and it was demonstrated to be a potential solution for further high-performance personalized SSVEP-BCI with quick calibration.

Tensor decomposition of EEG signals for transfer learning applications

ABSTRACT We address the recognized person-to-person Brain–Computer Interface (BCI) calibration problem and tackle session-dependency through the use of unsupervised canonical polyadic (CP) tensor decomposition. For a motor imagery task, the approach reveals universal structures within EEG data, common between subjects and prominent for a certain task. Further, we develop a novel similarity measure that includes weighting of the decomposition’s factor matrices, and argue that it is more representative than what has previously been presented in literature. The proposed similarity measure shows potential in a BCI classification task, i.e. drowsiness during simulated driving (average Pearson correlation of 0.6).

Motor Imagery Classification Using Effective Channel Selection of Multichannel EEG.

Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain-computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to select effective EEG channels for motor imagery (MI) classification in brain-computer interface (BCI) systems. The method identifies channels with higher entropy scores, which is an indication of greater information content. It discards redundant or noisy channels leading to reduced computational complexity and improved classification accuracy. High entropy means a more disordered pattern, whereas low entropy means a less disordered pattern with less information. The entropy of each channel for individual trials is calculated. The weight of each channel is represented by the mean entropy of the channel over all the trials. A set of channels with higher mean entropy are selected as effective channels for MI classification. A limited number of sub-band signals are created by decomposing the selected channels. To extract the spatial features, the common spatial pattern (CSP) is applied to each sub-band space of EEG signals. The CSP-based features are used to classify the right-hand and right-foot MI tasks using a support vector machine (SVM). The effectiveness of the proposed approach is validated using two publicly available EEG datasets, known as BCI competition III-IV(A) and BCI competition IV-I. The experimental results demonstrate that the proposed approach surpasses cutting-edge techniques.

DTP-Net: Learning to Reconstruct EEG Signals in Time-Frequency Domain by Multi-Scale Feature Reuse.

Electroencephalography (EEG) signals are prone to contamination by noise, such as ocular and muscle artifacts. Minimizing these artifacts is crucial for EEG-based downstream applications like disease diagnosis and brain-computer interface (BCI). This paper presents a new EEG denoising model, DTP-Net. It is a fully convolutional neural network comprising Densely-connected Temporal Pyramids (DTPs) placed between two learnable time-frequency transformations. In the time-frequency domain, DTPs facilitate efficient propagation of multi-scale features extracted from EEG signals of any length, leading to effective noise reduction. Comprehensive experiments on two public semi-simulated datasets demonstrate that the proposed DTP-Net consistently outperforms existing state-of-the-art methods on metrics including relative root mean square error (RRMSE) and signal-to-noise ratio improvement ( ∆SNR). Moreover, the proposed DTP-Net is applied to a BCI classification task, yielding an improvement of up to 5.55% in accuracy. This confirms the potential of DTP-Net for applications in the fields of EEG-based neuroscience and neuro-engineering. An in-depth analysis further illustrates the representation learning behavior of each module in DTP-Net, demonstrating its robustness and reliability.

Neural interface technology for human-computer interaction

The Brain-Computer Interface (BCI) is a highly promising way to establish a direct link between the human brain and external computerised apparatus, enabling individuals with severe disabilities to interact with their external environment. By harnessing BCI technology, these individuals can exert control over specific computerized devices, ranging from computers and wheelchairs to neural prosthetics, thus facilitating meaningful interaction with the world around them. However, while BCI technology holds immense potential, several aspects remain in conceptual stages or are constrained by specific circumstances. This review offers a comprehensive exploration of the fundamental principles governing BCI classification. It also provides an overview of applications that BCI has to offer and its multifaceted utility, particularly in the application of neuroprostheses, in conjunction with FES, and in the rehabilitation of stroke and epilepsy. The review navigates through the landscape of BCI application technologies, elucidating their challenges, and constraints. After analyses, potential developments in BCI technology were identified, and possible future challenges were highlighted.

Algorithm contest of motor imagery BCI in the World Robot Contest 2022: A survey

From August 19 to 21, 2022, the BCI Controlled Robot Contest finals in the World Robot Contest 2022 were held in Beijing, China. Fifteen teams participated in the finals in the Algorithm Contest of Motor Imagery BCI. This paper introduces the algorithms in the motor imagery (MI) classification area, describes the competition content and set, and summarizes the algorithms and results of the top five teams in the finals. First, the MI paradigm and the overview of the existing motor imagery brain–computer interface classification algorithms are introduced, followed by the introduction of the algorithms of the top five teams in the final step by step, including electroencephalography channel selection, data length selection, data preprocessing, data augmentation, classification network, training, and testing settings. Finally, the highlights and results of each algorithm are discussed.

Temporal–spatial transformer based motor imagery classification for BCI using independent component analysis

Motor Imagery (MI) classification with electroencephalography (EEG) is a critical aspect of Brain–Computer Interface (BCI) systems, enabling individuals with mobility limitations to communicate with the outside world. However, the complexity, variability, and low signal-to-noise ratio of EEG data present significant challenges in decoding these signals, particularly in a subject-independent manner. To overcome these challenges, we propose a transformer-based approach that employs a self-attention process to extract features in the temporal and spatial domains. To establish spatial correlations across MI EEG channels, the self-attention module periodically updates each channel by averaging its features across all channels. This weighted averaging improves classification accuracy and removes artifacts generated by manually selecting channels. Furthermore, the temporal self-attention mechanism encodes global sequential information into the features for each sample time step, allowing for the extraction of superior temporal properties in the time domain from MI EEG data. The effectiveness of the proposed strategy has been confirmed through testing against the BCI Competition IV 2a and 2b benchmarks. Overall, our proposed model outperforms state-of-the-art methods and demonstrates greater stability in both subject-dependent and subject-independent strategies.

Conformal in-ear bioelectronics for visual and auditory brain-computer interfaces

Brain-computer interfaces (BCIs) have attracted considerable attention in motor and language rehabilitation. Most devices use cap-based non-invasive, headband-based commercial products or microneedle-based invasive approaches, which are constrained for inconvenience, limited applications, inflammation risks and even irreversible damage to soft tissues. Here, we propose in-ear visual and auditory BCIs based on in-ear bioelectronics, named as SpiralE, which can adaptively expand and spiral along the auditory meatus under electrothermal actuation to ensure conformal contact. Participants achieve offline accuracies of 95% in 9-target steady state visual evoked potential (SSVEP) BCI classification and type target phrases successfully in a calibration-free 40-target online SSVEP speller experiment. Interestingly, in-ear SSVEPs exhibit significant 2nd harmonic tendencies, indicating that in-ear sensing may be complementary for studying harmonic spatial distributions in SSVEP studies. Moreover, natural speech auditory classification accuracy can reach 84% in cocktail party experiments. The SpiralE provides innovative concepts for designing 3D flexible bioelectronics and assists the development of biomedical engineering and neural monitoring.

Enhanced motor imagery of digits within the same hand via vibrotactile stimulation.

The aim of the present study is to evaluate the effect of vibrotactile stimulation prior to repeated complex motor imagery of finger movements using the non-dominant hand on motor imagery (MI) performance. Ten healthy right-handed adults (4 females and 6 males) participated in the study. The subjects performed motor imagery tasks with and without a brief vibrotactile sensory stimulation prior to performing motor imagery using either their left-hand index, middle, or thumb digits. Mu- and beta-band event-related desynchronization (ERD) at the sensorimotor cortex and an artificial neural network-based digit classification was evaluated. The ERD and digit discrimination results from our study showed that ERD was significantly different between the vibration conditions for the index, middle, and thumb. It was also found that digit classification accuracy with-vibration (mean ± SD = 66.31 ± 3.79%) was significantly higher than without-vibration (mean ± SD = 62.68 ± 6.58%). The results showed that a brief vibration was more effective at improving MI-based brain-computer interface classification of digits within a single limb through increased ERD compared to performing MI without vibrotactile stimulation.

EEG Signal Complexity Measurements to Enhance BCI-Based Stroke Patients' Rehabilitation.

The second leading cause of death and one of the most common causes of disability in the world is stroke. Researchers have found that brain-computer interface (BCI) techniques can result in better stroke patient rehabilitation. This study used the proposed motor imagery (MI) framework to analyze the electroencephalogram (EEG) dataset from eight subjects in order to enhance the MI-based BCI systems for stroke patients. The preprocessing portion of the framework comprises the use of conventional filters and the independent component analysis (ICA) denoising approach. Fractal dimension (FD) and Hurst exponent (Hur) were then calculated as complexity features, and Tsallis entropy (TsEn) and dispersion entropy (DispEn) were assessed as irregularity parameters. The MI-based BCI features were then statistically retrieved from each participant using two-way analysis of variance (ANOVA) to demonstrate the individuals' performances from four classes (left hand, right hand, foot, and tongue). The dimensionality reduction algorithm, Laplacian Eigenmap (LE), was used to enhance the MI-based BCI classification performance. Utilizing k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) classifiers, the groups of post-stroke patients were ultimately determined. The findings show that LE with RF and KNN obtained 74.48% and 73.20% accuracy, respectively; therefore, the integrated set of the proposed features along with ICA denoising technique can exactly describe the proposed MI framework, which may be used to explore the four classes of MI-based BCI rehabilitation. This study will help clinicians, doctors, and technicians make a good rehabilitation program for people who have had a stroke.

Novel electrotactile brain-computer interface with somatosensory event-related potential based control.

A brain computer interface (BCI) allows users to control external devices using non-invasive brain recordings, such as electroencephalography (EEG). We developed and tested a novel electrotactile BCI prototype based on somatosensory event-related potentials (sERP) as control signals, paired with a tactile attention task as a control paradigm. A novel electrotactile BCI comprises commercial EEG device, an electrical stimulator and custom software for EEG recordings, electrical stimulation control, synchronization between devices, signal processing, feature extraction, selection, and classification. We tested a novel BCI control paradigm based on tactile attention on a sensation at a target stimulation location on the forearm. Tactile stimuli were electrical pulses delivered at two proximal locations on the user's forearm for stimulating branches of radial and median nerves, with equal probability of the target and distractor stimuli occurrence, unlike in any other ERP-based BCI design. We proposed a compact electrical stimulation electrodes configuration for delivering electrotactile stimuli (target and distractor) using 2 stimulation channels and 3 stimulation electrodes. We tested the feasibility of a single EEG channel BCI control, to determine pseudo-online BCI performance, in ten healthy subjects. For optimizing the BCI performance we compared the results for two classifiers, sERP averaging approaches, and novel dedicated feature extraction/selection methods via cross-validation procedures. We achieved a single EEG channel BCI classification accuracy in the range of 75.1 to 88.1% for all subjects. We have established an optimal combination of: single trial averaging to obtain sERP, feature extraction/selection methods and classification approach. The obtained results demonstrate that a novel electrotactile BCI paradigm with equal probability of attended (target) and unattended (distractor) stimuli and proximal stimulation sites is feasible. This method may be used to drive restorative BCIs for sensory retraining in stroke or brain injury, or assistive BCIs for communication in severely disabled users.

A logistic binary Jaya optimization-based channel selection scheme for motor-imagery classification in brain-computer interface

BCI systems use motor imagery to allow users to control external devices through their brain activity. They extract neural signals from the brain using a large number of EEG channels. However, high-dimensional data from multichannel BCI systems increases the computational burden, leading to slower processing and higher costs. In this study, we proposed a Logistic S-shaped Binary Jaya Optimization Algorithm (LS-BJOA), which combines a logistic map with the Jaya optimization algorithm to alleviate the computational burden caused by many channels. The logistic map introduces stochasticity to better simulate the chaotic behavior of brain signals and improve predictive accuracy. Our method initializes a set of three electrodes as a candidate solution and subsequently determines the most relevant channels iteratively. We used a bi-objective fitness function to evaluate the significance of the selected channels, which involves maximizing classification accuracy and minimizing the length of the channel subset. Initially, a fifth-order bandpass filter with Independent Component Analysis (ICA) was applied to filter MI signals and artifacts reduction, respectively. The Regularized Common Spatial Pattern (RCSP) was used to extract spatiotemporal features from the selected channels. Finally, the three classifiers: (1) Support Vector Machine (SVM), (2) Naïve Bayes (NB), and (3) Linear Discriminant Analysis (LDA) were used to determine maximum classification accuracy. The experiment is validated on three public EEG datasets (BCI Competition IV- 2008 – IIA, BCI Competition IV- dataset 1, BCI competition III – dataset IVa). Our method achieved superior classification accuracy (83.59%, 82.09%, and 89.02% on datasets 1, 2, and 3 respectively) with fewer channels than baseline methods. Additionally, computation time was significantly reduced without compromising accuracy. Topographical mapping revealed frontal lobe involvement in MI tasks during physical activities. Topographical mapping of the selected channels showed that the frontal lobe executes various MI tasks during physical activities.

Polyvinyl alcohol/polyacrylamide double-network hydrogel-based semi-dry electrodes for robust electroencephalography recording at hairy scalp for noninvasive brain–computer interfaces

Objective. Reliable and user-friendly electrodes can continuously and real-time capture the electroencephalography (EEG) signals, which is essential for real-life brain–computer interfaces (BCIs). This study develops a flexible, durable, and low-contact-impedance polyvinyl alcohol/polyacrylamide double-network hydrogel (PVA/PAM DNH)-based semi-dry electrode for robust EEG recording at hairy scalp. Approach. The PVA/PAM DNHs are developed using a cyclic freeze-thaw strategy and used as a saline reservoir for semi-dry electrodes. The PVA/PAM DNHs steadily deliver trace amounts of saline onto the scalp, enabling low and stable electrode-scalp impedance. The hydrogel also conforms well to the wet scalp, stabilizing the electrode-scalp interface. The feasibility of the real-life BCIs is validated by conducting four classic BCI paradigms on 16 participants. Main results. The results show that the PVA/PAM DNHs with 7.5 wt% PVA achieve a satisfactory trade-off between the saline load-unloading capacity and the compressive strength. The proposed semi-dry electrode exhibits a low contact impedance (18 ± 8.9 kΩ at 10 Hz), a small offset potential (0.46 mV), and negligible potential drift (1.5 ± 0.4 μV min−1). The temporal cross-correlation between the semi-dry and wet electrodes is 0.91, and the spectral coherence is higher than 0.90 at frequencies below 45 Hz. Furthermore, no significant differences are present in BCI classification accuracy between these two typical electrodes. Significance. Based on the durability, rapid setup, wear-comfort, and robust signals of the developed hydrogel, PVA/PAM DNH-based semi-dry electrodes are a promising alternative to wet electrodes in real-life BCIs.

Hybrid Brain–Computer Interface System Using Auditory Stimulation and Speech Imagination Paradigms for Assistive Technology

In the case of paralysis with visual and tactile impairments, brain–computer interfaces (BCIs) based on auditory and mental imagery paradigms are alternative methods for controlling external devices. This study demonstrates the use of a hybrid BCI via auditory stimulation and speech imagination for assistive technology. The proposed auditory BCI using Thai vowel and numeral stimulus patterns as well as multi-loudspeaker position settings for multi-command BCI are investigated. To avoid auditory stimulation during resting periods, a speech imagery method is used to enable an audio stimulator. We observe the classification efficiency of speech imagery and auditory BCIs from selected electroencephalogram channels using the proposed algorithms. We examine the efficiency of using the proposed BCI in the presence of background noise (speech). One command is created using the proposed speech-imagination paradigm. Four commands are created using the proposed auditory stimulation paradigm. We design an experiment to verify the proposed BCI paradigm and classification algorithms for real-time processing. The results show that the average classification accuracy of the proposed auditory BCI using numeral stimuli and scatter patterns without speech noise is 72.2% to 83.3%, respectively. The efficiency under background noise is approximately two times lower than that without background noise.

Motor Imagery BCI Classification with Frequency and Time-Frequency Features by Using Different Dimensions of the Feature Space Using Autoencoders

Brain-Computer Interfaces (BCIs) enable the users to directly communicate with machines based on various desired purposes through brain signals without moving any body parts. Thus, they have become very useful for prostheses, electric wheelchairs, virtual keyboards, and other studies like survey applications and emotion classifications. In this study, EEG signal processing was performed on the BCI Competition III-3a dataset, which contains motor imagery (MI) signals with four classes. Features of the non-stationary EEG signals belonging to three subjects were extracted using Power Spectral Density (PSD) with welch method, Wavelet Decomposition (WD), Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT). From extracted 900 features, feature space dimension reduction was realized using Autoencoder, an unsupervised learning algorithm. The average accuracy obtained with Artificial Neural Network (ANN) is 74.5% for all binary classifications, which is generally a good result because of the non-stationary nature of EEG signals. 801 features yielded the best classification performance, obtained using an autoencoder with 400 hidden layer neurons.

MI-DABAN: A dual-attention-based adversarial network for motor imagery classification

The brain–computer interface (BCI) based on motor imagery electroencephalography (EEG) is widely used because of its convenience and safety. However, due to the distributional disparity between EEG signals, data from other subjects cannot be used directly to train a subject-specific classifier. For efficient use of the labeled data, domain transfer learning and adversarial learning are gradually applied to BCI classification tasks. While these methods improve classification performance, they only align globally and ignore task-specific class boundaries, which may lead to the blurring of features near the classification boundaries. Simultaneously, they employ fully shared generators to extract features, resulting in the loss of domain-specific information and the destruction of performance. To address these issues, we propose a novel dual-attention-based adversarial network for motor imagery classification (MI-DABAN). Our framework leverages multiple subjects’ knowledge to improve a single subject’s motor imagery classification performance by cleverly using a novel adversarial learning method and two unshared attention blocks. Specifically, without introducing additional domain discriminators, we iteratively maximize and minimize the output difference between the two classifiers to implement adversarial learning to ensure accurate domain alignment. Among them, maximization is used to identify easily confused samples near the decision boundary, and minimization is used to align the source and target domain distributions. Moreover, for the shallow features from source and target domains, we use two non-shared attention blocks to preserve domain-specific information, which can prevent the negative transfer of domain information and further improve the classification performance on test data. We conduct extensive experiments on two publicly available EEG datasets, namely BCI Competition IV Datasets 2a and 2b. The experiment results demonstrate our method’s effectiveness and superiority.

EEG-based covert speech decoding using random rotation extreme learning machine ensemble for intuitive BCI communication

Background and motivation:Brain–computer interfaces (BCI) assist communication for the handicapped and disabled. Previous work has shown that it is usually non-intuitive, and it frequently employs Electroencephalogram (EEG) based motor imagery. Ensemble learning has been shown to be reliable in a variety of BCI classification tasks, including motor imagery and P300 event-related potential. The purpose of ensemble learning, according to past research, is to get trained accurate but diverse base classifiers to increase generalization performance. Extreme Learning Machine (ELM) with fast learning speed has been proven effective in classification applications and suitable for ensemble learning due to its randomness. The suitability of ensemble methods for intuitive BCI communication using EEG data of different covert speech words is investigated in this work. Methods:To achieve this goal, we construct a random rotation based kernel ELM ensemble, resulting in very diverse classifiers which can learn through transformed spaces. Minimize error ensemble pruning is used to prune low complementarity classifiers, and consequently, combining the remaining high complementarity classifiers to get the final ensemble classifier. Eight subjects performed covert speech tasks which involved mentally repeating five words namely; ‘left’, ‘right’, ‘up’, ‘down’, and ‘stop’. Results:We achieved classification accuracy with pruning and without pruning of 63.67% and 57.13% respectively. Experimental results show that, in comparison to other classification techniques, the proposed technique produces competitive results. Conclusion and Significance:Our findings indicate the potential of random rotation kernel ELM ensemble pruning to effectively classify EEG-based covert speech signals. This proposed research involving an ensemble method for the classification of covert speech words can make a significant contribution towards intuitive BCI development using silent speech.

Learning Optimal Time-Frequency-Spatial Features by the CiSSA-CSP Method for Motor Imagery EEG Classification.

The common spatial pattern (CSP) is a popular method in feature extraction for motor imagery (MI) electroencephalogram (EEG) classification in brain-computer interface (BCI) systems. However, combining temporal and spectral information in the CSP-based spatial features is still a challenging issue, which greatly affects the performance of MI-based BCI systems. Here, we propose a novel circulant singular spectrum analysis embedded CSP (CiSSA-CSP) method for learning the optimal time-frequency-spatial features to improve the MI classification accuracy. Specifically, raw EEG data are first segmented into multiple time segments and spectrum-specific sub-bands are further derived by CiSSA from each time segment in a set of non-overlapping filter bands. CSP features extracted from all time-frequency segments contain more sufficient time-frequency-spatial information. An experimental study was implemented on the publicly available EEG dataset (BCI Competition III dataset IVa) and a self-collected experimental EEG dataset to validate the effectiveness of the CiSSA-CSP method. Experimental results demonstrate that discriminative and robust features are extracted effectively. Compared with several state-of-the-art methods, the proposed method exhibited optimal accuracies of 96.6% and 95.2% on the public and experimental datasets, respectively, which confirms that it is a promising method for improving the performance of MI-based BCIs.

A new attention-based 3D densely connected cross-stage-partial network for motor imagery classification in BCI

Objective. The challenge for motor imagery (MI) in brain-computer interface (BCI) systems is finding a reliable classification model that has high classification accuracy and excellent robustness. Currently, one of the main problems leading to degraded classification performance is the inaccuracy caused by nonstationarities and low signal-to-noise ratio in electroencephalogram (EEG) signals. Approach. This study proposes a novel attention-based 3D densely connected cross-stage-partial network (DCSPNet) model to achieve efficient EEG-based MI classification. This is an end-to-end classification model framework based on the convolutional neural network (CNN) architecture. In this framework, to fully utilize the complementary features in each dimension, the optimal features are extracted adaptively from the EEG signals through the spatial-spectral-temporal (SST) attention mechanism. The 3D DCSPNet is introduced to reduce the gradient loss by segmenting the extracted feature maps to strengthen the network learning capability. Additionally, the design of the densely connected structure increases the robustness of the network. Main results. The performance of the proposed method was evaluated using the BCI competition IV 2a and the high gamma dataset, achieving an average accuracy of 84.45% and 97.88%, respectively. Our method outperformed most state-of-the-art classification algorithms, demonstrating its effectiveness and strong generalization ability. Significance. The experimental results show that our method is promising for improving the performance of MI-BCI. As a general framework based on time-series classification, it can be applied to BCI-related fields.

Logistic Regression With Tangent Space-Based Cross-Subject Learning for Enhancing Motor Imagery Classification

Brain-computer interface (BCI) performance is often impacted due to the inherent nonstationarity in the recorded EEG signals coupled with a high variability across subjects. This study proposes a novel method using logistic regression with tangent space-based transfer learning (LR-TSTL) for motor imagery (MI)-based BCI classification problems. The single-trial covariance matrix (CM) features computed from the EEG signals are transformed into a Riemannian geometry frame and tangent space features are computed by considering the lower triangular matrix. These are then further classified using the logistic regression model to improve classification accuracy. The performance of LR-TSTL is tested on healthy subjects’ data set as well as on stroke patients’ data set. As compared to existing within-subject learning approaches the proposed method gave an equivalent or better performance in terms of average classification accuracy (78.95 ± 11.68%), while applied as leave-one-out cross-subject learning for healthy subjects. Interestingly, for the patient data set LR-TSTL significantly ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p&lt; 0.05$ </tex-math></inline-formula> ) outperformed the current benchmark performance by achieving an average classification accuracy of 81.75 ± 6.88%. The results show that the proposed method for cross-subject learning has the potential to realize the next generation of calibration-free BCI technologies with enhanced practical usability especially in the case of neurorehabilitative BCI designs for stroke patients.