Exploring Motor Imagery EEG: Enhanced EEG Microstate Analysis with GMD-Driven Density Canopy Method

The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity.Traditional methods such as Atomic Agglomerative Hierarchical Clustering(AAHC),K-means clustering,Principal Component Analysis(PCA),and Independent Component Analysis(ICA)are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction.Tackling these limitations,this study introduces a Global Map Dissimilarity(GMD)-driven density canopy K-means clustering algorithm.This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for dynamic modeling of EEG data.Utilizing this advanced algorithm,the study analyzes the Motor Imagery(MI)dataset from the GigaScience database,GigaDB.The findings reveal six distinct microstates during actual right-hand movement and five microstates across other task conditions,with microstate C showing superior performance in all task states.During imagined movement,microstate A was significantly enhanced.Comparison with existing algorithms indicates a significant improvement in clustering performance by the refined method,with an average Calinski-Harabasz Index(CHI)of 35517.29 and a Davis-Bouldin Index(DBI)average of 2.57.Furthermore,an information-theoretical analysis of the microstate sequences suggests that imagined movement exhibits higher complexity and disorder than actual movement.By utilizing the extracted microstate sequence parameters as features,the improved algorithm achieved a classification accuracy of 98.41%in EEG signal categorization for motor imagery.A performance of 78.183%accuracy was achieved in a four-class motor imagery task on the BCI-IV-2a dataset.These results demonstrate the potential of the advanced algorithm in microstate analysis,offering a more effective tool for a deeper understanding of the spatiotemporal features of EEG signals.

Application of a hospital–community–family trinity rehabilitation nursing model combined with motor imagery therapy in patients with cerebral infarction

BACKGROUND Rehabilitation nursing is considered an indispensable part of the cerebral infarction treatment system.The hospital–community–family trinity rehabilitation nursing model can provide continuous nursing services across hospitals,communities,and families for patients.AIM To explore the application of a hospital–community–family rehabilitation nursing model combined with motor imagery therapy in patients with cerebral infarction.METHODS From January 2021 to December 2021,88 patients with cerebral infarction were divided into a study(n=44)and a control(n=44)group using a simple random number table.The control group received routine nursing and motor imagery therapy.The study group was given hospital–community–family trinity rehabilitation nursing based on the control group.Motor function(FMA),balance ability(BBS),activities of daily living(BI),quality of life(SS-QOL),activation status of the contralateral primary sensorimotor cortical area to the affected side,and nursing satisfaction were evaluated before and after intervention in both groups.RESULTS Before intervention,FMA and BBS were similar(P>0.05).After 6 months’intervention,FMA and BBS were significantly higher in the study than in the control group(both P<0.05).Before intervention,BI and SS-QOL scores were not different between the study and control group(P>0.05).However,after 6months’intervention,BI and SS-QOL were higher in the study than in the control group(P<0.05).Before intervention,activation frequency and volume were similar between the study and the control group(P>0.05).After 6 months’intervention,the activation frequency and volume were higher in the study than in the control group(P<0.05).The reliability,empathy,reactivity,assurance,and tangibles scores for quality of nursing service were higher in the study than in the control group(P<0.05).CONCLUSION Combining a hospital–community–family trinity rehabilitation nursing model and motor imagery therapy enhances the motor function and balance ability of patients with cerebral infarction,improving their quality of life.

Corticospinal excitability during motor imagery is diminished by continuous repetition-induced fatigue

Application of continuous repetition of motor imagery can improve the performance of exercise tasks.However,there is a lack of more detailed neurophysiological evidence to support the formulation of clear standards for interventions using motor imagery.Moreover,identification of motor imagery intervention time is necessary because it exhibits possible central fatigue.Therefore,the purpose of this study was to elucidate the development of fatigue during continuous repetition of motor imagery through objective and subjective evaluation.The study involved two experiments.In experiment 1,14 healthy young volunteers were required to imagine grasping and lifting a 1.5-L plastic bottle using the whole hand.Each participant performed the motor imagery task 100 times under each condition with 48 hours interval between two conditions:500 mL or 1500 mL of water in the bottle during the demonstration phase.Mental fatigue and a decrease in pinch power appeared under the 1500-mL condition.There were changes in concentration ability or corticospinal excitability,as assessed by motor evoked potentials,between each set with continuous repetition of motor imagery also under the 1500-mL condition.Therefore,in experiment 2,12 healthy volunteers were required to perform the motor imagery task 200 times under the 1500-mL condition.Both concentration ability and corticospinal excitability decreased.This is the first study to show that continuous repetition of motor imagery can decrease corticospinal excitability in addition to producing mental fatigue.This study was approved by the Institutional Ethics Committee at the Nagasaki University Graduate School of Biomedical and Health Sciences(approval No.18121302)on January 30,2019.

Motor imagery training induces changes in brain neural networks in stroke patients

Motor imagery is the mental representation of an action without overt movement or muscle activation. However, the effects of motor imagery on stroke-induced hand dysfunction and brain neural networks are still unknown. We conducted a randomized controlled trial in the China Rehabilitation Research Center. Twenty stroke patients, including 13 males and 7 females, 32–51 years old, were recruited and randomly assigned to the traditional rehabilitation treatment group（PP group, n = 10） or the motor imagery training combined with traditional rehabilitation treatment group（MP group, n = 10）. All patients received rehabilitation training once a day, 45 minutes per session, five times per week, for 4 consecutive weeks. In the MP group, motor imagery training was performed for 45 minutes after traditional rehabilitation training, daily. Action Research Arm Test and the Fugl-Meyer Assessment of the upper extremity were used to evaluate hand functions before and after treatment. Transcranial magnetic stimulation was used to analyze motor evoked potentials in the affected extremity. Diffusion tensor imaging was used to assess changes in brain neural networks. Compared with the PP group, the MP group showed better recovery of hand function, higher amplitude of the motor evoked potential in the abductor pollicis brevis, greater fractional anisotropy of the right dorsal pathway, and an increase in the fractional anisotropy of the bilateral dorsal pathway. Our findings indicate that 4 weeks of motor imagery training combined with traditional rehabilitation treatment improves hand function in stroke patients by enhancing the dorsal pathway. This trial has been registered with the Chinese Clinical Trial Registry（registration number： Chi CTR-OCH-12002238）.

Motor Imagery and Error Related Potential Induced Position Control of a Robotic Arm

The paper introduces an electroencephalography(EEG) driven online position control scheme for a robot arm by utilizing motor imagery to activate and error related potential(ErrP) to stop the movement of the individual links, following a fixed(pre-defined) order of link selection. The right(left)hand motor imagery is used to turn a link clockwise(counterclockwise) and foot imagery is used to move a link forward. The occurrence of ErrP here indicates that the link under motion crosses the visually fixed target position, which usually is a plane/line/point depending on the desired transition of the link across 3D planes/around 2D lines/along 2D lines respectively. The imagined task about individual link's movement is decoded by a classifier into three possible class labels: clockwise, counterclockwise and no movement in case of rotational movements and forward, backward and no movement in case of translational movements. One additional classifier is required to detect the occurrence of the ErrP signal, elicited due to visually inspired positional link error with reference to a geometrically selected target position. Wavelet coefficients and adaptive autoregressive parameters are extracted as features for motor imagery and ErrP signals respectively. Support vector machine classifiers are used to decode motor imagination and ErrP with high classification accuracy above 80%. The average time taken by the proposed scheme to decode and execute control intentions for the complete movement of three links of a robot is approximately33 seconds. The steady-state error and peak overshoot of the proposed controller are experimentally obtained as 1.1% and4.6% respectively.

Discrimination of Motor Imagery Patterns by Electroencephalogram Phase Synchronization Combined With Frequency Band Energy

Central nerve signal evoked by thoughts can be directly used to control a robot or prosthetic devices without the involvement of the peripheral nerve and muscles.This is a new strategy of human-computer interaction.A method of electroencephalogram(EEG) phase synchronization combined with band energy was proposed to construct a feature vector for pattern recognition of brain-computer interaction based on EEG induced by motor imagery in this paper,rhythm and beta rhythm were first extracted from EEG by band pass filter and then the frequency band energy was calculated by the sliding time window;the instantaneous phase values were obtained using Hilbert transform and then the phase synchronization feature was calculated by the phase locking value(PLV) and the best time interval for extracting the phase synchronization feature was searched by the distribution of the PLV value in the time domain.Finally,discrimination of motor imagery patterns was performed by the support vector machine(SVM).The results showed that the phase synchronization feature more effective in4s-7s and the correct classification rate was 91.4%.Compared with the results achieved by a single EEG feature related to motor imagery,the correct classification rate was improved by 3.5 and4.3 percentage points by combining phase synchronization with band energy.These indicate that the proposed method is effective and it is expected that the study provides a way to improve the performance of the online real-time brain-computer interaction control system based on EEG related to motor imagery.

Enhanced Accuracy for Motor Imagery Detection Using Deep Learning for BCI

Brain-Computer Interface(BCI)is a system that provides a link between the brain of humans and the hardware directly.The recorded brain data is converted directly to the machine that can be used to control external devices.There are four major components of the BCI system:acquiring signals,preprocessing of acquired signals,features extraction,and classification.In traditional machine learning algorithms,the accuracy is insignificant and not up to the mark for the classification of multi-class motor imagery data.The major reason for this is,features are selected manually,and we are not able to get those features that give higher accuracy results.In this study,motor imagery(MI)signals have been classified using different deep learning algorithms.We have explored two different methods:Artificial Neural Network(ANN)and Long Short-Term Memory(LSTM).We test the classification accuracy on two datasets:BCI competition III-dataset IIIa and BCI competition IV-dataset IIa.The outcome proved that deep learning algorithms provide greater accuracy results than traditional machine learning algorithms.Amongst the deep learning classifiers,LSTM outperforms the ANN and gives higher classification accuracy of 96.2%.

A method for using video presentation to increase the vividness and activity of cortical regions during motor imagery tasks

In recent years,mental practice(MP)using laterally inverted video of a subject’s non-paralyzed upper limb to improve the vividness of presented motor imagery(MI)has been shown to be effective for improving the function of a paralyzed upper limb.However,no studies have yet assessed the activity of cortical regions engaged during MI task performance using inverse video presentations and neurophysiological indicators.This study sought to investigate changes in MI vividness and hemodynamic changes in the cerebral cortex during MI performance under the following three conditions in near-infrared spectroscopy:MI-only without inverse video presentation(MI-only),MI with action observation(AO)of an inverse video presentation of another person’s hand(AO+MI(other hand)),and MI with AO of an inverse video presentation of a participant’s own hand(AO+MI(own hand)).Participants included 66 healthy right-handed adults(41 men and 25 women;mean age:26.3±4.3 years).There were 23 patients in the MI-only group(mean age:26.4±4.1 years),20 in the AO+MI(other hand)group(mean age:25.9±5.0 years),and 23 in the AO+MI(own hand)group(mean age:26.9±4.1 years).The MI task involved transferring 1 cm×1 cm blocks from one plate to another,once per second,using chopsticks held in the non-dominant hand.Based on a visual analog scale(VAS),MI vividness was significantly higher in the AO+MI(own hand)group than in the MI-only group and the AO+MI(other hand)group.A main effect of condition was revealed in terms of MI vividness,as well as regions of interest(ROIs)in certain brain areas associated with motor processing.The data suggest that inverse video presentation of a person’s own hand enhances the MI vividness and increases the activity of motor-related cortical areas during MI.This study was approved by the Institutional Ethics Committee of Nagasaki University Graduate School of Biomedical and Health Sciences(approval No.18121303)on January 18,2019.

Analysis of Brain Activation during Motor Imagery Based on fMRI

Brain activation during motor imagery （MI） has been studied extensively for years. Based on studies of brain activations of MI, in present study, a complex finger tapping imagery and execution experi- ment is designed to test the brain activation during MI. The experiment results show that during MI, brain activation exists mainly in the supplementary motor area （SMA） and precentral area where the dorsal premotor area （PMd） and the primary motor area （M1） mainly located; and some activation can be also observed in the primary and secondary somatosensory cortex （S1）, the inferior parietal lobule （IPL） and the superior parietal lobule （SPL）. Additionally, more brain activation can be observed during left-hand MI than during right-hand MI, this difference probably is caused by asymmetry of brain.

Transfer Learning Algorithm Design for Feature Transfer Problem in Motor Imagery Brain-computer Interface

The non-stationary of the motor imagery electroencephalography(MI-EEG)signal is one of the main limitations for the development of motor imagery brain-computer interfaces(MI-BCI).The nonstationary of the MI-EEG signal and the changes of the experimental environment make the feature distribution of the testing set and training set deviates,which reduces the classification accuracy of MI-BCI.In this paper,we propose a Kullback–Leibler divergence(KL)-based transfer learning algorithm to solve the problem of feature transfer,the proposed algorithm uses KL to measure the similarity between the training set and the testing set,adds support vector machine(SVM)classification probability to classify and weight the covariance,and discards the poorly performing samples.The results show that the proposed algorithm can significantly improve the classification accuracy of the testing set compared with the traditional algorithms,especially for subjects with medium classification accuracy.Moreover,the algorithm based on transfer learning has the potential to improve the consistency of feature distribution that the traditional algorithms do not have,which is significant for the application of MI-BCI.

BCI+VR Rehabilitation Design of Closed-Loop Motor Imagery Based on the Degree of Drug Addiction

After abusing drugs for long,drug users will experience deteriorated self-control cognitive ability,and poor emotional regulation.This paper designs a closed-loop virtual-reality(VR),motorimagery(MI)rehabilitation training system based on brain-computer interface(BCI)(MI-BCI+VR),aiming to enhance the self-control,cognition,and emotional regulation of drug addicts via personalized rehabilitation schemes.This paper is composed of two parts.In the first part,data of 45 drug addicts(mild:15;moderate:15;and severe:15)is tested with electroencephalogram(EEG)and near-infrared spectroscopy(NIRS)equipment(EEG-NIRS)under the dual-mode,synchronous signal collection paradigm.Using these data sets,a dual-modal signal convolutional neural network(CNN)algorithm is then designed based on decision fusion to detect and classify the addiction degree.In the second part,the MIBCI+VR rehabilitation system is designed,optimizing the Filter Bank Common Spatial Pattern(FBCSP)algorithm used in MI,and realizing MI-EEG intention recognition.Eight VR rehabilitation scenes are devised,achieving the communication between MI-BCI and VR scene models.Ten subjects are selected to test the rehabilitation system offline and online,and the test accuracy verifies the feasibility of the system.In future,it is suggested to develop personalized rehabilitation programs and treatment cycles based on the addiction degree.

Spectral entropy analysis of different alpha band rhythms in relation to hand motor imagery

The event-related desynchronization/synchronization(ERD/ERS) time courses of lower and upper alpha band rhythms during hand motor imagery are investigated respectively by Fourier Sectral Entropy (FSE) in this paper. By analyzing one group of BCI competition data, it was found that FSE within upper alpha band displays a pronounced increase and decrease over contralateral and ipsilateral brain areas respectively at the onset of hand motor imagery, which is corresponding to the antagonistic ERD/ERS patterns in previous studies. Different from the upper alpha activity pattern, FSE within lower alpha band displays a consistent increase over both two hemispheres hand representative areas. The preliminary results show that FSE could disclose the different behaviors of the upper and lower alpha band rhythms so that a new idea with the complexity measure is provided to characterize functional dissociation of lower and upper frequency alpha rhythms in relation to hand motor imagery.

Estimation of the Hemodynamic Response during Motor Imagery Using Bayesian RBF Neural Network

Hemodynamic response during motor imagery （MI） is studied extensively by functional magnetic resonance imaging （fMRI） technologies. To further understand the human brain functions under MI, a more precise classification of the brain regions corresponding to each brain function is desired. In this study, a Bayesian trained radial basis function （RBF） neural network, which determines the weights and regularization parameters automatically by Bayesian learning, is applied to make a precise classification of the hemodynamic response to the tasks during the MI experiment. To illustrate the proposed method, data with MI task performance from 1 subject was used. The results demonstrate that this approach splits the hemodynamic response to different tasks successfully.

Combining Motor Imagery and Action Observation with Vibratory Stimulation Increases Corticomotor Excitability in Healthy Young Adults

Vibratory stimulation but also motor imagery and action observation can induce corticomotor modulation, as a bottom-up stimulus and top-down stimuli, respectively. However, it remains unknown whether the combination of motor imagery, action observation, and vibratory stimulation can effectively increase corticomotor excitability. This study aimed to investigate the effect of motor imagery and/or action observation, in the presence or absence of vibratory stimulation, on the corticomotor excitability of healthy young adults. Vibratory stimulation was provided to the palm of the right hand. Action observation consisted in viewing a movie of someone else’s finger flexion and extension movements. The imagery condition required the participants to imagine they were moving their fingers while viewing the movie and attempting to move their fingers in accordance with the movie. Eleven right-handed healthy young adults were asked to perform six conditions randomly: 1) vibratory stimulation, imagery, and action observation, 2) vibratory stimulation and action observation, 3) vibratory stimulation and viewing of a blank screen, 4) imagery and action observation, 5) action observation, and 6) viewing of a blank screen. Single-pulse transcranial magnetic stimulation was conducted to assess corticomotor excitability and the peak-to-peak amplitude of the motor evoked potentials. The results showed that vibratory stimulation increases corticospinal excitability. The findings further revealed that performing motor imagery while viewing finger movement is more effective at inducing an augmentation of corticomotor excitability compared to action observation alone. Thus, the combination of motor imagery, action observation, and vibratory stimulation can effectively augment corticomotor excitability.

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.

A novel residual shrinkage block-based convolutional neural network for improving the recognition of motor imagery EEG signals

Purpose-Recently,the convolutional neural network(ConvNet)has a wide application in the classification of motor imagery EEG signals,However,the low sigalto-noise electroencephalogram(EEG)signals are ollectede under the interference of noises.However,the conventional ConvNet model cannot directly solve this problem.This study aims to discuss the aforementioned issues.Design/methodology/approach-To solve this problem,this paper adopted a novel residual shrinkage block(RSB)to construct the ComvNet model(RSBConvNet).During the feature extraction from EEG simnals,the proposed RSBConvNet prevented the noise component in EEG signals,and improved the classification accuracy of motor imagery.In the construction of RSBConvNet,the author applied the soft thresholding strategy to prevent the non-related.motor imagery features in EEG sigmals.The soft thresholding was inserted into the residual block(RB),and the suitable threshold for the curent EEG signals distribution can be learned by minimizing the loss function.Therefore,during the feature extraction of motor imagery,the proposed RSBConvNet de noised the EEG signals and improved the discriminative of dassifiation features.Findings-Comparative experiments and ablation studies were done on two public benchumark datasets.Compared with conventionalConvNet models,the proposed RSBConvNet model has olbvious improvements in motor imagery classification accuracy and Kappa officient.Ablation studies have also shown the de noised abilities of the RSBConvNet modeL Morbover,different parameters and computational methods of the RSBConvNet model have been tested om the dassificatiton of motor imagery.Originality/value-Based ou the experimental results,the RSBComvNet constructed in this paper has an excellent reogmition accuracy of M-BCI which can be used for further appications for the online MI-BCI.

A mutli-scale spatial-temporal convolutional neural network with contrastive learning for motor imagery EEG classification

Motor imagery(MI)based Brain-computer interfaces(BCIs)have a wide range of applications in the stroke rehabilitation field.However,due to the low signal-to-noise ratio and high cross-subject variation of the electroencephalogram(EEG)signals generated by motor imagery,the classification performance of the existing methods still needs to be improved to meet the need of real practice.To overcome this problem,we propose a multi-scale spatial-temporal convolutional neural network called MSCNet.We introduce the contrastive learning into a multi-temporal convolution scale backbone to further improve the robustness and discrimination of embedding vectors.Experimental results of binary classification show that MSCNet outperforms the state-of-theart methods,achieving accuracy improvement of 6.04%,3.98%,and 8.15%on BCIC IV 2a,SMR-BCI,and OpenBMI datasets in subject-dependent manner,respectively.The results show that the contrastive learning method can significantly improve the classification accuracy of motor imagery EEG signals,which provides an important reference for the design of motor imagery classification algorithms.

Recognition of motor imagery tasks for BCI using CSP and chaotic PSO twin SVM

Accurate modeling and recognition of the brain activity patterns for reliable communication and interaction are still a challenging task for the motor imagery （MI） brain-computer interface （BCI） system. In this paper, we propose a common spatial pattern （CSP） and chaotic particle swarm optimization （CPSO） twin support vector machine （TWSVM） scheme for classification of MI electroencephalography （EEG）. The self-adaptive artifact removal and CSP were used to obtain the most distinguishable features. To improve the recognition results, CPSO was employed to tune the hyper-parameters of the TWSVM classifier. The usefulness of the proposed method was evaluated using the BCI competition IV-IIa dataset. The experimental results showed that the mean recognition accuracy of our proposed method was increased by 5.35%, 4.33%, 0.78%, 1.45%, and 9.26% compared with the CPSO support vector machine （SVM）, particle swarm optimization （PSO） TWSVM, linear discriminant analysis （LDA）, back propagation （BP） and probabilistic neural network （PNN）, respectively. Furthermore, it achieved a faster or comparable central processing unit （CPU） running time over the traditional SVM methods.

Multiband decomposition and spectral discriminative analysis for motor imagery BCI via deep neural network

Human limb movement imagery,which can be used in limb neural disorders rehabilitation and brain-controlled external devices,has become a significant control paradigm in the domain of brain-computer interface(BCI).Although numerous pioneering studies have been devoted to motor imagery classification based on electroencephalography(EEG)signal,their performance is somewhat limited due to insufficient analysis of key effective frequency bands of EEG signals.In this paper,we propose a model of multiband decomposition and spectral discriminative analysis for motor imagery classification,which is called variational sample-long short term memory(VS-LSTM)network.Specifically,we first use a channel fusion operator to reduce the signal channels of the raw EEG signal.Then,we use the variational mode decomposition(VMD)model to decompose the EEG signal into six band-limited intrinsic mode functions(BIMFs)for further signal noise reduction.In order to select discriminative frequency bands,we calculate the sample entropy(SampEn)value of each frequency band and select the maximum value.Finally,to predict the classification of motor imagery,a LSTM model is used to predict the class of frequency band with the largest SampEn value.An open-access public data is used to evaluated the effectiveness of the proposed model.In the data,15 subjects performed motor imagery tasks with elbow flexion/extension,forearm supination/pronation and hand open/close of right upper limb.The experiment results show that the average classification result of seven kinds of motor imagery was 76.2%,the average accuracy of motor imagery binary classification is 96.6%(imagery vs.rest),respectively,which outperforms the state-of-the-art deep learning-based models.This framework significantly improves the accuracy of motor imagery by selecting effective frequency bands.This research is very meaningful for BCIs,and it is inspiring for end-to-end learning research.

Subject inefficiency phenomenon of motor imagery brain-computer interface: Influence factors and potential solutions

Motor imagery brain–computer interfaces(MI-BCIs)have great potential value in prosthetics control,neurorehabilitation,and gaming;however,currently,most such systems only operate in controlled laboratory environments.One of the most important obstacles is the MI-BCI inefficiency phenomenon.The accuracy of MI-BCI control varies significantly(from chance level to 100%accuracy)across subjects due to the not easily induced and unstable MI-related EEG features.An MI-BCI inefficient subject is defined as a subject who cannot achieve greater than 70%accuracy after sufficient training time,and multiple survey results indicate that inefficient subjects account for 10%–50%of the experimental population.The widespread use of MI-BCI has been seriously limited due to these large percentages of inefficient subjects.In this review,we summarize recent findings of the cause of MI-BCI inefficiency from resting-state brain function,task-related brain activity,brain structure,and psychological perspectives.These factors help understand the reasons for inter-subject MI-BCI control performance variability,and it can be concluded that the lower resting-state sensorimotor rhythm(SMR)is the key factor in MI-BCI inefficiency,which has been confirmed by multiple independent laboratories.We then propose to divide MI-BCI inefficient subjects into three categories according to the resting-state SMR and offline/online accuracy to apply more accurate approaches to solve the inefficiency problem.The potential solutions include developing transfer learning algorithms,new experimental paradigms,mindfulness meditation practice,novel training strategies,and identifying new motor imagery-related EEG features.To date,few studies have focused on improving the control accuracy of MI-BCI inefficient subjects;thus,we appeal to the BCI community to focus more on this research area.Only by reducing the percentage of inefficient subjects can we create the opportunity to expand the value and influence of MI-BCI.