Brain-computer Interface Research Articles

Enhanced Motor Imagery Classification through Channel Selection and Machine Learning Algorithms for BCI Applications

Brain-Computer Interface (BCI) applications utilizing Electroencephalography (EEG) signals have garnered significant attention for their potential to facilitate through communication between the brain and external devices. EEG-based BCIs offer a non-invasive means to interpret neural activity, enabling a range of applications in healthcare, gaming, and cognitive neuroscience. This study explores motor imagery (MI) EEG signals classification, employing a variety of signal processing techniques as well as machine learning algorithms to increase accuracy and reliability. Using data from the BCI Competition IV dataset, the proposed methodology involves EEG band separation via Butterworth bandpass filters, channel selection through a wrapper method using K-nearest neighbors (KNN), and classification of motor imagery tasks. The study demonstrates a high classification accuracy of 98% across different motor imagery tasks, highlighting the effectiveness of the proposed approach. This method not only shows promise for BCI applications aimed at assisting individuals with motor disabilities but also for gaming and potential security applications such as user authentication. Future work will focus on further enhancing the model's accuracy and exploring its integration into diverse practical applications.

Brain–computer interface for simultaneous dual‐region spatial coding in hippocampal and somatosensory cortex of freely behaving rats

AbstractDecoding of spatial information from place cells is currently limited to individual brain regions, severely constraining the understanding of the brain's spatial navigation mechanisms. In this study, synchronized detection of dual‐brain region spatial encoding was conducted using brain–computer interface (BCI). Therefore, an implantable microelectrode array (MEA) was developed tailored for the simultaneous detection of neuronal activities in the CA1 of the hippocampus and the Barrel Cortex (BC) of the somatosensory cortex in rats as BCI. The MEA was improved by modifying the nanocomposite PtNP/PEDOT:PSS to improve their electrical properties (reducing impedance from 1.82 ± 0.17 MΩ to 3.4 ± 0.3 kΩ), facilitating neural recordings in freely behaving rats. The neural activities were obtained from the CA1 and the BC simultaneously and validated the existence of place cells in both brain regions. This study highlighted the potential of PtNP/PEDOT:PSS‐modified MEA as BCI in concurrently detecting neural activity associated with spatial encoding in two brain regions, offering novel perspectives on the processing of tactile stimuli and the formation of cognitive maps for navigation.

Comparing emotion classification: machine learning algorithms and hybrid model with support vector machines

<p>Recently, the use of artificial intelligence techniques has become widespread, having been adopted in brain-computer interfaces (BCIs) with electroencephalograms (EEGs). BCIs allow direct communication between a person's brain and a computer, and have various uses ranging from assistive technology to neuroscientific study. This paper provides an introductory overview of BCIs and EEG. We adopted the use of machine learning (ML) algorithms, including K-nearest neighbors (KNN), logistic regression, decision trees, random forests, and support vector machine (SVM). Additionally, we proposed a hybrid model of deep learning (DL) and ML by combining convolutional neural networks (CNNs) and SVMs. Our achieved 98% accuracy. The goal is to classify EEG signals into three emotional states: happy, normal, and sad. The study aims to achieve a comprehensive understanding of the effectiveness of these algorithms in accurately classifying emotional states based on EEG data. By comparing the performance of traditional ML methods and the proposed hybrid model, we seek to identify the most robust and accurate approach to sentiment classification.</p>

Design and application of pneumatic rehabilitation glove system based on brain-computer interface.

Stroke has been the second leading cause of death and disability worldwide. With the innovation of therapeutic schedules, its death rate has decreased significantly but still guides chronic movement disorders. Due to the lack of independent activities and minimum exercise standards, the traditional rehabilitation means of occupational therapy and constraint-induced movement therapy pose challenges in stroke patients with severe impairments. Therefore, specific and effective rehabilitation methods seek innovation. To address the overlooked limitation, we design a pneumatic rehabilitation glove system. Specially, we developed a pneumatic glove, which utilizes ElectroEncephaloGram (EEG) acquisition to gain the EEG signals. A proposed EEGTran model is inserted into the system to distinguish the specific motor imagination behavior, thus, the glove can perform specific activities according to the patient's imagination, facilitating the patients with severe movement disorders and promoting the rehabilitation technology. The experimental results show that the proposed EEGTrans reached an accuracy of 87.3% and outperformed that of competitors. It demonstrates that our pneumatic rehabilitation glove system contributes to the rehabilitation training of stroke patients.

MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces.

Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response to external events and their associated underlying complex spatiotemporal feature information is governed by ongoing oscillatory activity within the brain. Deep learning methods have been increasingly adopted for ERP-based brain-computer interfaces (BCIs) due to their excellent feature representation abilities, which allow for deep analysis of oscillatory activity within the brain. Features with higher spatiotemporal frequencies usually represent detailed and localized information, while features with lower spatiotemporal frequencies usually represent global structures. Mining EEG features from multiple spatiotemporal frequencies is conducive to obtaining more discriminative information. A multiscale feature fusion octave convolution neural network (MOCNN) is proposed in this article. MOCNN divides the ERP signals into high-, medium- and low-frequency components corresponding to different resolutions and processes them in different branches. By adding mid- and low-frequency components, the feature information used by MOCNN can be enriched, and the required amount of calculations can be reduced. After successive feature mapping using temporal and spatial convolutions, MOCNN realizes interactive learning among different components through the exchange of feature information among branches. Classification is accomplished by feeding the fused deep spatiotemporal features from various components into a fully connected layer. The results, obtained on two public datasets and a self-collected ERP dataset, show that MOCNN can achieve state-of-the-art ERP classification performance. In this study, the generalized concept of octave convolution is introduced into the field of ERP-BCI research, which allows effective spatiotemporal features to be extracted from multiscale networks through branch width optimization and information interaction at various scales.

Average Time Consumption Per Character - a Practical Performance Metric for Generic Synchronous BCI Spellers.

The information transfer rate (ITR) is widely accepted as a performance metric for generic brain-computer interface (BCI) spellers, while it is noticeable that the communication speed given by ITR is actually an upper bound which however can never be reached in real systems. A new performance metric is therefore needed. In this paper, a new metric named average time consumption per character (ATCPC) is proposed. It quantifies how long it takes on average to type one character using a typical synchronous BCI speller. To analytically derive ATCPC, the real typing process is modelled with a random walk on a graph. Misclassification and backspace are carefully characterized. A close-form formula of ATCPC is obtained through computing the hitting time of the random walk. The new metric is validated through simulated typing experiments and compared with ITR. Firstly, the formula and simulation show a good consistency. Secondly, ITR always tends to overestimate the communication speed, while ATCPC is more realistic. The proposed ATCPC metric is valid. ATCPC is a qualified substitute for ITR. ATCPC also reveals the great potential of keyboard optimization to further enhance the performance of BCI spellers, which was hardly investigated before.

Cross-subject emotion recognition in brain-computer interface based on frequency band attention graph convolutional adversarial neural networks

Background:Emotion is an important area in neuroscience. Cross-subject emotion recognition based on electroencephalogram (EEG) data is challenging due to physiological differences between subjects. Domain gap, which refers to the different distributions of EEG data at different subjects, has attracted great attention for cross-subject emotion recognition. Comparison with existing methods:This study focuses on narrowing the domain gap between subjects through the emotional frequency bands and the relationship information between EEG channels. Emotional frequency band features represent the energy distribution of EEG data in different frequency ranges, while relationship information between EEG channels provides spatial distribution information about EEG data. New method:To achieve this, this paper proposes a model called the Frequency Band Attention Graph convolutional Adversarial neural Network (FBAGAN). This model includes three components: a feature extractor, a classifier, and a discriminator. The feature extractor consists of a layer with a frequency band attention mechanism and a graph convolutional neural network. The mechanism effectively extracts frequency band information by assigning weights and Graph Convolutional Networks can extract relationship information between EEG channels by modeling the graph structure. The discriminator then helps minimize the gap in the frequency information and relationship information between the source and target domains, improving the model’s ability to generalize. Results:The FBAGAN model is extensively tested on the SEED, SEED-IV, and DEAP datasets. The accuracy and standard deviation scores are 88.17% and 4.88, respectively, on the SEED dataset, and 77.35% and 3.72 on the SEED-IV dataset. On the DEAP dataset, the model achieves 69.64% for Arousal and 65.18% for Valence. These results outperform most existing models. Conclusions:The experiments indicate that FBAGAN effectively addresses the challenges of transferring EEG channel domain and frequency band domain, leading to improved performance.

Translational Bioethics in China: Brain-Computer Interface Research as a Case Study.

The research and development of emerging technologies has potential long-term and societal impacts that pose governance challenges. This essay summarizes the development of research ethics in China over the past few decades, as well as the measures taken by the Chinese government to build its ethical governance system of science and technology after the occurrence of the CRISPR-babies incident. The essay then elaborates on the current problems of this system through the case study of ethical governance of brain-computer interface research, and explores how the transition from research ethics to translational bioethics, which encourages interdisciplinary collaboration and focuses on societal implications, may respond to the challenges of ethical governance of science and technology.

Neural Cyber Augmentation (NCA): A Future Frontier in Public Health

Neural Cyber Augmentation (NCA) is a groundbreaking interdisciplinary field that integrates neuroscience, cybernetics, and biotechnology to enhance human cognitive and physical capabilities. This technology holds transformative potential for public health, enabling advancements in cognitive enhancement, disease management, and human-machine interaction. Early research in brain-computer interfaces (BCIs) demonstrated the feasibility of using neural signals to control external devices, laying the foundation for more complex applications such as advanced prosthetics and cognitive training. Subsequent developments in cybernetic implants have significantly improved the quality of life for individuals with sensory and motor impairments by providing enhanced capabilities beyond natural limits. The integration of artificial intelligence (AI) with neural interfaces has further propelled the field, enabling personalized cognitive training and adaptive therapies. Despite these advancements, the ethical and social implications of NCA, such as privacy concerns, informed consent, and equitable access, must be addressed. Future research should focus on improving neural interfaces, integrating AI for personalized interventions, establishing comprehensive ethical guidelines, conducting longitudinal studies on long-term effects, and developing strategies to ensure accessibility for all. By addressing these challenges, NCA has the potential to revolutionize public health and usher in a new era of human enhancement and well-being.

CTNet: a convolutional transformer network for EEG-based motor imagery classification

Brain-computer interface (BCI) technology bridges the direct communication between the brain and machines, unlocking new possibilities for human interaction and rehabilitation. EEG-based motor imagery (MI) plays a pivotal role in BCI, enabling the translation of thought into actionable commands for interactive and assistive technologies. However, the constrained decoding performance of brain signals poses a limitation to the broader application and development of BCI systems. In this study, we introduce a convolutional Transformer network (CTNet) designed for EEG-based MI classification. Firstly, CTNet employs a convolutional module analogous to EEGNet, dedicated to extracting local and spatial features from EEG time series. Subsequently, it incorporates a Transformer encoder module, leveraging a multi-head attention mechanism to discern the global dependencies of EEG's high-level features. Finally, a straightforward classifier module comprising fully connected layers is followed to categorize EEG signals. In subject-specific evaluations, CTNet achieved remarkable decoding accuracies of 82.52% and 88.49% on the BCI IV-2a and IV-2b datasets, respectively. Furthermore, in the challenging cross-subject assessments, CTNet achieved recognition accuracies of 58.64% on the BCI IV-2a dataset and 76.27% on the BCI IV-2b dataset. In both subject-specific and cross-subject evaluations, CTNet holds a leading position when compared to some of the state-of-the-art methods. This underscores the exceptional efficacy of our approach and its potential to set a new benchmark in EEG decoding.

Entropy parameter optimization for epileptic seizure detection: A parallel approach

Brain Computer Interface (BCI) – one of the recent advancements in the field of Bioinformatics which offers a real-time support for the people, who are affected by chronic neurological disorders. Owing to the rapid progression of Electroencephalogram (EEG) – based BCI system, the detection of epileptic seizures has become much simpler. However, accurate detection through visual inspection is tedious, time-consuming and prone to error. Thus, automation has become inevitable and for automating the epileptic seizure detection, entropies are appropriate as the nature of EEG signals are complex, arrhythmic, ephemeral, and non-stationary. Several renowned entropies are widely applied, nevertheless, the existing models fail to identify the optimal parameters of the entropies which greatly influences the performance of the Machine Learning models that could make better predictions. Hence to address the aforementioned issue, this paper presents a parallel machine learning based farmland fertility algorithm which optimizes the parameters of various entropies thereby detecting Epileptic Seizures in a systematic way. A novel weighted fitness function has been designed based on Kullback-Leibler Divergence (KLD). The extracted features are further classified using state-of-the-art classifiers. The overall performance of the proposed algorithm was evaluated using the EEG dataset obtained from University of Bonn, Germany, University of Bern and Indian EEG, New Delhi and the results show the supremacy of the proposed model in terms of sensitivity, specificity, precision, F1-score, G-mean and classification accuracy.

Effects of motor imagery-based brain-computer interface-controlled electrical stimulation on lower limb function in hemiplegic patients in the acute phase of stroke: a randomized controlled study.

Lower limb motor dysfunction is one of the most serious consequences of stroke; however, there is insufficient evidence for optimal rehabilitation strategies. Improving lower limb motor function through effective rehabilitation strategies is a top priority for stroke patients. Neuroplasticity is a key factor in the recovery of motor function. The extent to which neuroplasticity-based rehabilitation therapy using brain-computer interface (BCI) is effective in treating lower limb motor dysfunction in acute ischemic stroke patients has not been extensively investigated. This study aimed to assess the impact of BCI rehabilitation on lower limb motor dysfunction in individuals with acute ischemic stroke by evaluating motor function, walking ability, and daily living activities. This study was conducted in a randomized controlled trial, involving 64 patients with acute ischemic stroke who experienced lower limb motor dysfunction. All patients were divided into two groups, with 32 patients assigned to the control group was given conventional rehabilitation once a day for 70 min, 5 times a week for 2 weeks, and the experimental group (n = 32) was given BCI rehabilitation on top of the conventional rehabilitation for 1 h a day, 30 min of therapy in the morning and an additional 30 min in the afternoon, for a total of 20 sessions over a two-week period. The primary outcome was lower extremity motor function, which was assessed using the lower extremity portion of the Fugl-Meyer Rating Scale (FMA-LE), and the secondary endpoints were the Functional Ambulation Scale (FAC), and the Modified Barthel index (MBI). After 20 sessions of treatment, both groups improved in motor function, walking function, and activities of daily living, and the improvements in FMA-LE scores (p < 0.001), FAC (p = 0.031), and MBI (p < 0.001) were more pronounced in the experimental group compared with the control group. Conventional rehabilitation therapy combined with BCI rehabilitation therapy can improve the lower limb motor function of hemiplegic patients with stroke, enhance the patient's ability to perform activities of daily living, and promote the improvement of walking function, this is an effective rehabilitation policy to promote recovery from lower extremity motor function disorders.

Robotics in Physical Rehabilitation: Systematic Review.

As the global prevalence of motor disabilities continues to rise, there is a pressing need for advanced solutions in physical rehabilitation. This systematic review examines the progress and challenges of implementing robotic technologies in the motor rehabilitation of patients with physical disabilities. The integration of robotic technologies such as exoskeletons, assistive training devices, and brain-computer interface systems holds significant promise for enhancing functional recovery and patient autonomy. The review synthesizes findings from the most important studies, focusing on the clinical effectiveness of robotic interventions in comparison to traditional rehabilitation methods. The analysis reveals that robotic therapies can significantly improve motor function, strength, co-ordination, and dexterity. Robotic systems also support neuroplasticity, enabling patients to relearn lost motor skills through precise, controlled, and repetitive exercises. However, the adoption of these technologies is hindered by high costs, the need for specialized training, and limited accessibility. Key insights from the review highlight the necessity of personalizing robotic therapies to meet individual patient needs, alongside addressing technical, economic, social, and cultural barriers. The review also underscores the importance of continued research to optimize these technologies and develop effective implementation strategies. By overcoming these challenges, robotic technologies can revolutionize motor rehabilitation, improving quality of life and social integration for individuals with motor disabilities.

Bio-inspired EEG signal computing using machine learning and fuzzy theory for decision making in future-oriented brain-controlled vehicles

One kind of autonomous vehicle that can take instructions from the driver by reading their electroencephalogram (EEG) signals using a Brain-Computer Interface (BCI) is called a Brain-Controlled Vehicle (BCV). The operation of such a vehicle is greatly affected by how well the BCI works. At present, there are limitations on the accuracy of BCI recognition, the number of distinguishable command categories, and the execution duration of command recognition. Consequently, vehicles that are exclusively controlled by EEG signals demonstrate suboptimal control performance. To address the difficulty of improving the control capabilities of brain-controlled cars while maintaining BCI performance, a fuzzy logic-based technique called as Fuzzy Brain-Control Fusion Control is introduced. This approach uses Fuzzy Discrete Event System (FDES) supervisory theory to verify the accuracy of the driver's brain-controlled directives. Concurrently, a fuzzy logic-based automatic controller is developed to generate decisions automatically in accordance with the present state of the vehicle via fuzzy reasoning. The final decision is then reached through the application of secondary fuzzy reasoning to the accuracy of the driver's instructions and the automated decisions to make adjustments that are more consistent with human intent. A clever BCI gadget known as the Consistent State Visual Evoked Potential (SSVEP) is utilized to show the viability of the proposed technique. We recommend that additional research should be conducted at this time to confirm that our recommended system may further improve the control execution of BCI-fueled cars, regardless of whether BCIs have special limitations.

Iterative alignment discovery of speech-associated neural activity.

Objective. Brain-computer interfaces (BCIs) have the potential to preserve or restore speech in patients with neurological disorders that weaken the muscles involved in speech production. However, successful training of low-latency speech synthesis and recognition models requires alignment of neural activity with intended phonetic or acoustic output with high temporal precision. This is particularly challenging in patients who cannot produce audible speech, as ground truth with which to pinpoint neural activity synchronized with speech is not available.Approach. In this study, we present a new iterative algorithm for neural voice activity detection (nVAD) called iterative alignment discovery dynamic time warping (IAD-DTW) that integrates DTW into the loss function of a deep neural network (DNN). The algorithm is designed to discover the alignment between a patient's electrocorticographic (ECoG) neural responses and their attempts to speak during collection of data for training BCI decoders for speech synthesis and recognition.Main results. To demonstrate the effectiveness of the algorithm, we tested its accuracy in predicting the onset and duration of acoustic signals produced by able-bodied patients with intact speech undergoing short-term diagnostic ECoG recordings for epilepsy surgery. We simulated a lack of ground truth by randomly perturbing the temporal correspondence between neural activity and an initial single estimate for all speech onsets and durations. We examined the model's ability to overcome these perturbations to estimate ground truth. IAD-DTW showed no notable degradation (<1% absolute decrease in accuracy) in performance in these simulations, even in the case of maximal misalignments between speech and silence.Significance. IAD-DTW is computationally inexpensive and can be easily integrated into existing DNN-based nVAD approaches, as it pertains only to the final loss computation. This approach makes it possible to train speech BCI algorithms using ECoG data from patients who are unable to produce audible speech, including those with Locked-In Syndrome.

An Opto-electrophysiology Neural Probe with Photoelectric Artifact-Free for Advanced Single-Neuron Analysis.

Opto-electrophysiology neural probes targeting single-cell levels offer an important avenue for elucidating the intrinsic mechanisms of the nervous system using different physical quantities, representing a significant future direction for brain-computer interface (BCI) devices. However, the highly integrated structure poses significant challenges to fabrication processes and the presence of photoelectric artifacts complicates the extraction and analysis of target signals. Here, we propose a highly miniaturized and integrated opto-electrophysiology neural probe for electrical recording and optical stimulation at the single-cell/subcellular level. The design of a total internal reflection layer addresses the photoelectric artifacts that are more pronounced in single-cell devices compared to conventional implantable BCI devices. Finite element simulations and electrical signal tests demonstrate that the opto-electrophysiology neural probe eliminates the photoelectric artifacts in the time domain, which represents a significant breakthrough for optoelectrical integrated BCI devices. Our proposed opto-electrophysiology neural probe holds substantial potential for promoting the development of in vivo BCI devices and developing advanced therapeutic strategies for neurological disorders.

EEG-Based Methods for Diagnosing Color Vision Deficiency: A Comprehensive Review

Color vision deficiency (CVD) is one of the most common disorders related to visual impairment. Individuals with this condition are unable to differentiate between colors due to the absence or impairment of one or more color photoreceptors in their retinas. This disorder can be diagnosed through multiple approaches. This review paper provides a comprehensive summary of studies on applying Brain–Computer Interface (BCI) technology for diagnosing CVD. The main purpose of this review is to help researchers understand how BCI can be further developed and utilized for diagnosing CVD in the future.

Bridging minds and machines in Industry 5.0: neurobiological approach.

In transitioning from Industry 4.0 to the forthcoming Industry 5.0, this research explores the fusion of the humanistic view and technological developments to redefine Continuing Engineering Education (CEE). Industry 5.0 introduces concepts like biomanufacturing and human-centricity, embodying the integration of sustainability and resiliency principles in CEE, thereby shaping the upskilling and reskilling initiatives for the future workforce. The interaction of sophisticated concepts such as Human-Machine Interface and Brain-Computer Interface (BCI) forms a conceptual bridge toward the approaching Fifth Industrial Revolution, allowing one to understand human beings and the impact of their biological development across diverse and changing workplace settings. Our research is based on recent studies into Knowledge, Skills, and Abilities taxonomies, linking these elements with dynamic labor market profiles. This work intends to integrate a biometric perspective to conceptualize and describe how cognitive abilities could be represented by linking a Neuropsychological test and a biometric assessment. We administered the brief Neuropsychological Battery in Spanish (Neuropsi Breve). At the same time, 15 engineering students used the Emotiv insight device that allowed the EEG recollection to measure performance metrics such as attention, stress, engagement, and excitement. The findings of this research illustrate a methodology that allowed the first approach to the cognitive abilities of engineering students to be from neurobiological and behavioral perspectives. Additionally, two profiles were extracted from the results. The first illustrates the Neuropsi test areas, its most common mistakes, and its performance ratings regarding the students' sample. The second profile shows the interaction between the EEG and Neuropsi test, showing engineering students' cognitive and emotional states based on biometric levels. The study demonstrates the potential of integrating neurobiological assessment into engineering education, highlighting a significant advancement in addressing the skills requirements of Industry 5.0. The results suggest that obtaining a comprehensive understanding of students' cognitive abilities is possible, and educational interventions can be adapted by combining neuropsychological approaches with EEG data collection. In the future, it is essential to refine these evaluation methods further and explore their applicability in different engineering disciplines. Additionally, it is necessary to investigate the long-term impact of these methods on workforce preparation and performance.

Tissue-like interfacing of planar electrochemical organic neuromorphic devices

Abstract Electrochemical organic neuromorphic devices (ENODes) are rapidly developing as platforms for computing, automation, and biointerfacing. Resembling short- and long-term synaptic plasticity is a key characteristic in creating functional neuromorphic interfaces that showcase spiking activity and learning capabilities. This potentially enables ENODes to couple with biological systems, such as living neuronal cells and ultimately the brain. Before coupling ENODes with the brain, it is worth investigating the neuromorphic behavior of ENODes when they interface with electrolytes that have a consistency similar to brain tissue in mechanical properties, as this can affect the modulation of ion and neurotransmitter diffusion. Here, we present ENODEs based on different PEDOT:PSS formulations with various geometries interfacing with gel-electrolytes loaded with a neurotransmitter to mimic brain-chip interfacing. Short-term plasticity and neurotransmitter-mediated long-term plasticity have been characterized in contact with diverse gel electrolytes. We found that both the composition of the electrolyte and the PEDOT:PSS formulation used as gate and channel material play a crucial role in the diffusion and trapping of cations that ultimately modulate the conductance of the transistor channels. It was shown that paired pulse facilitation can be achieved in both devices, while long-term plasticity can be achieved with a tissue-like soft electrolyte, such as agarose gel electrolyte, on spin-coated ENODes. Our work on ENODe-gel coupling could pave the way for effective brain interfacing for computing and neuroelectronic applications.

Improved motor imagery training for subject's self-modulation in EEG-based brain-computer interface.

For the electroencephalogram- (EEG-) based motor imagery (MI) brain-computer interface (BCI) system, more attention has been paid to the advanced machine learning algorithms rather than the effective MI training protocols over past two decades. However, it is crucial to assist the subjects in modulating their active brains to fulfill the endogenous MI tasks during the calibration process, which will facilitate signal processing using various machine learning algorithms. Therefore, we propose a trial-feedback paradigm to improve MI training and introduce a non-feedback paradigm for comparison. Each paradigm corresponds to one session. Two paradigms are applied to the calibration runs of corresponding sessions. And their effectiveness is verified in the subsequent testing runs of respective sessions. Different from the non-feedback paradigm, the trial-feedback paradigm presents a topographic map and its qualitative evaluation in real time after each MI training trial, so the subjects can timely realize whether the current trial successfully induces the event-related desynchronization/event-related synchronization (ERD/ERS) phenomenon, and then they can adjust their brain rhythm in the next MI trial. Moreover, after each calibration run of the trial-feedback session, a feature distribution is visualized and quantified to show the subjects' abilities to distinguish different MI tasks and promote their self-modulation in the next calibration run. Additionally, if the subjects feel distracted during the training processes of the non-feedback and trial-feedback sessions, they can execute the blinking movement which will be captured by the electrooculogram (EOG) signals, and the corresponding MI training trial will be abandoned. Ten healthy participants sequentially performed the non-feedback and trial-feedback sessions on the different days. The experiment results showed that the trial-feedback session had better spatial filter visualization, more beneficiaries, higher average off-line and on-line classification accuracies than the non-feedback session, suggesting the trial-feedback paradigm's usefulness in subject's self-modulation and good ability to perform MI tasks.