Motor cortex activation during visuomotor transformations: evoked potentials during overt and imagined movements.

Despite the prevalence of visuomotor transformations in our motor skills, their mechanisms remain incompletely understood, especially when imagery actions are considered such as mentally picking up a cup or pressing a button. Here, we used a stimulus-response task to directly compare the visuomotor transformation underlying overt and imagined button presses. Electroencephalographic activity was recorded while participants responded to highlights of the target button while ignoring the second, non-target button. Movement-related potentials (MRPs) and event-related desynchronization occurred for both overt movements and motor imagery (MI), with responses present even for non-target stimuli. Consistent with the activity accumulation model where visual stimuli are evaluated and transformed into the eventual motor response, the timing of MRPs matched the response time on individual trials. Activity-accumulation patterns were observed for MI, as well. Yet, unlike overt movements, MI-related MRPs were not lateralized, which appears to be a neural marker for the distinction between generating a mental image and transforming it into an overt action. Top-down response strategies governing this hemispheric specificity should be accounted for in future research on MI, including basic studies and medical practice.

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

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

Arm cooling selectively impacts sensorimotor control.

The benefits of cold have long been recognized in sport and medicine. However, it also brings costs, which have more rarely been investigated, notably in terms of sensorimotor control. We hypothesized that, in addition to peripheral effects, cold slows down the processing of proprioceptive cues, which has an impact on both feedback and feedforward control. We therefore compared the performances of participants whose right arm had been immersed in either cold water (arm temperature: 14°C) or lukewarm water (arm temperature: 34°C). In experiment 1, we administered a Fitts's pointing task and performed a kinematic analysis to determine whether sensorimotor control processes were affected by the cold. Results revealed 1) modifications in late kinematic parameters, suggesting changes in the use of proprioceptive feedback, and 2) modifications in early kinematic parameters, suggesting changes in action representations and/or feedforward processes. To explore our hypothesis further, we ran a second experiment in which no physical movement was involved, and thus no peripheral effects. Participants were administrated a hand laterality task, known to involve implicit motor imagery and assess the internal representation of the hand. They were shown left- and right-hand images randomly displayed in different orientations in the picture plane and had to identify as quickly and as accurately as possible whether each image was of the left hand or the right hand. Results revealed slower responses and more errors when participants had to mentally rotate the cooled hand in the extreme orientation of 160°, further suggesting the impact of cold on action representations.NEW & NOTEWORTHY We investigated how arm cooling modulates sensorimotor representations and sensorimotor control. Arm cooling induced changes in early kinematic parameters of pointing, suggesting an impact on feedforward processes or hand representation. Arm cooling induced changes in late kinematic parameters of pointing, suggesting an impact on feedback processes. Arm cooling also affected performance on a hand laterality task, suggesting that action representations were modified.

Improving motor imagery through a mirror box for BCI users.

The aim of this study was to evaluate mirror visual feedback (MVF) as a training tool for brain-computer interface (BCI) users. This is because approximately 20-30% of subjects require more training to operate a BCI system using motor imagery. Electroencephalograms (EEGs) were recorded from 18 healthy subjects, using event-related desynchronization (ERD) to observe the responses during the movement or movement intention of the hand for the conditions of control, imagination, and the MVF with the mirror box. We constituted two groups: group 1: control, imagination, and MVF; group 2: control, MVF, and imagination. There were significant differences in imagination conditions between groups using MVF before or after imagination (right-hand, P = 0.0403; left-hand, P = 0.00939). The illusion of movement through MVF is not possible in all subjects, but even in those cases, we found an increase in imagination when the subject used the MVF previously. The increase in the r2s of imagination in the right and left hands suggests cross-learning. The increase in motor imagery recorded with EEG after MVF suggests that the mirror box made it easier to imagine movements. Our results provide evidence that the MVF could be used as a training tool to improve motor imagery.NEW & NOTEWORTHY The increase in motor imagery recorded with EEG after MVF (mirror visual feedback) suggests that the mirror box made it easier to imagine movements. Our results demonstrate that MVF could be used as a training tool to improve motor imagery.

Changes in the excitability of anterior horn cells in a mental rotation task of body parts.

INTRODUCTION/AIMS: Mental rotation (MR), a tool of implicit motor imagery, is the ability to rotate mental representations of two- or three-dimensional objects. Although many reports have described changes in brain activity during MR tasks, it is not clear whether the excitability of anterior horn cells in the spinal cord can be changed. In this study, we examined whether MR tasks of hand images affect the excitability of anterior horn cells using F-wave analysis. METHODS: Right-handed, healthy participants were recruited for this study. F-waves of the right abductor pollicis brevis were recorded after stimulation of the right median nerve at rest, during a non-MR task, and during an MR task. The F-wave persistence and the F/M amplitude ratio were calculated and analyzed. RESULTS: Twenty participants (11 men and 9 women; mean age, 29.2 ± 4.4 years) were initially recruited, and data from the 18 that met the inclusion criteria were analyzed. The F-wave persistence was significantly higher in the MR task than in the resting condition (p = .001) or the non-MR task (p = .012). The F/M amplitude ratio was significantly higher in the MR task than in the resting condition (p = .019). DISCUSSION: The MR task increases the excitability of anterior horn cells corresponding to the same body part. MR tasks may have the potential for improving motor function in patients with reduced excitability of the anterior horn cells, although this methodology must be further verified in a clinical setting.

The lateralized effects of Parkinson's Disease on motor imagery: Evidence from mental chronometry.

Alterations to the content of action representations may contribute to the movement challenges that characterize Parkinson's Disease (PD). One way to investigate action representations is through motor imagery. As PD motor symptoms typically have a unilateral onset, disease-related deficits related to action representations may follow a similarly lateralized pattern. The present study examined if temporal accuracy of motor imagery in individuals with PD differed according to the side of the body involved in the task. Thirty-eight participants with PD completed a mental chronometry task using their more affected and less affected side. Participants had significantly shorter mental versus physical movement times for the more affected. Higher imagery vividness in the kinaesthetic domain predicted shorter mental versus physical movement times for the more affected side, as did lower imagery vividness in the visual domain and poorer cognitive function. These results indicate that people with PD imagine movements differently when the target actions their more affected versus less affected side. It is additionally possible that side-specific deficits in the accurate processing of kinaesthetic information lead to an increased reliance on visual processes and cognitive resources to successfully execute motor imagery involving the more affected side.

EEG rhythm separation and time-frequency analysis of fast multivariate empirical mode decomposition for motor imagery BCI.

Motor imagery electroencephalogram (EEG) is widely employed in brain-computer interface (BCI) systems. As a time-frequency analysis method for nonlinear and non-stationary signals, multivariate empirical mode decomposition (MEMD) and its noise-assisted version (NA-MEMD) has been widely used in the preprocessing step of BCI systems for separating EEG rhythms corresponding to specific brain activities. However, when applied to multichannel EEG signals, MEMD or NA-MEMD often demonstrate low robustness to noise and high computational complexity. To address these issues, we have explored the advantages of our recently proposed fast multivariate empirical mode decomposition (FMEMD) and its noise-assisted version (NA-FMEMD) for analyzing motor imagery data. We emphasize that FMEMD enables a more accurate estimation of EEG frequency information and exhibits a more noise-robust decomposition performance with improved computational efficiency. Comparative analysis with MEMD on simulation data and real-world EEG validates the above assertions. The joint average frequency measure is employed to automatically select intrinsic mode functions that correspond to specific frequency bands. Thus, FMEMD-based classification architecture is proposed. Using FMEMD as a preprocessing algorithm instead of MEMD can improve the classification accuracy by 2.3% on the BCI Competition IV dataset. On the Physiobank Motor/Mental Imagery dataset and BCI Competition IV Dataset 2a, FMEMD-based architecture also attained a comparable performance to complex algorithms. The results indicate that FMEMD proficiently extracts feature information from small benchmark datasets while mitigating dimensionality constraints resulting from computational complexity. Hence, FMEMD or NA-FMEMD can be a powerful time-frequency preprocessing method for BCI.

Neuroimaging prognostic factors for treatment response to motor imagery training after stroke.

The efficacy of motor imagery training for motor recovery is well acknowledged, but with substantial inter-individual variability in stroke patients. To help optimize motor imagery training therapy plans and screen suitable patients, this study aimed to explore neuroimaging biomarkers explaining variability in treatment response. Thirty-nine stroke patients were randomized to a motor imagery training group (n = 22, received a combination of conventional rehabilitation therapy and motor imagery training) and a control group (n = 17, received conventional rehabilitation therapy and health education) for 4 weeks of interventions. Their demography and clinical information, brain lesion from structural MRI, spontaneous brain activity and connectivity from rest fMRI, and sensorimotor brain activation from passive motor task fMRI were acquired to identify prognostic factors. We found that the variability of outcomes from sole conventional rehabilitation therapy could be explained by the reserved sensorimotor neural function, whereas the variability of outcomes from motor imagery training + conventional rehabilitation therapy was related to the spontaneous activity in the ipsilesional inferior parietal lobule and the local connectivity in the contralesional supplementary motor area. The results suggest that additional motor imagery training treatment is also efficient for severe patients with damaged sensorimotor neural function, but might be more effective for patients with impaired motor planning and reserved motor imagery.

Effects of motor imagery using virtual reality on pain sensitivity and affect in healthy individuals: A prospective randomized crossover study.

OBJECTIVE: Exercise induces a hypoalgesic response and improves affect. However, some individuals are unable to exercise for various reasons. Motor imagery, involving kinesthetic and visual imagery without physical movement, activates brain regions associated with these benefits and could be an alternative for those unable to exercise. Virtual reality also enhances motor imagery performance because of its illusion and embodiment. Therefore, we examined the effects of motor imagery combined with virtual reality on pain sensitivity and affect in healthy individuals. DESIGN: Randomized crossover study. SETTING: Laboratory. SUBJECTS: Thirty-six participants (women: 18) were included. METHODS: Each participant completed three 10-min experimental sessions, comprising actual exercise, motor imagery only, and motor imagery combined with virtual reality. Hypoalgesic responses and affective improvement were assessed using the pressure-pain threshold and the Positive and Negative Affect Schedule, respectively. RESULTS: All interventions significantly increased the pressure-pain threshold at the thigh (P<0.001). Motor imagery combined with virtual reality increased the pressure-pain threshold more than motor imagery alone, but the threshold was similar to that of actual exercise (both P≥0.05). All interventions significantly decreased the negative affect of the Positive and Negative Affect Schedule (all P<0.05). CONCLUSIONS: Motor imagery combined with virtual reality exerted hypoalgesic and affective-improvement effects similar to those of actual exercise.

Effects of virtual reality versus motor imagery versus routine physical therapy in patients with parkinson's disease: a randomized controlled trial.

BACKGROUND: Parkinson's Disease (PD) is the second most common progressive neurodegenerative disorder, mostly affecting balance and motor function caused mainly by a lack of dopamine in the brain. The use of virtual reality (VR) and motor imagery (MI) is emerging as an effective method of rehabilitation for people with Parkinson's disease. Motor imagery and virtual reality have not been compared in patients with Parkinson's disease. This randomized clinical trial is unique to compare the effects of virtual reality with routine physical therapy, motor imagery with routine physical therapy, and routine physical therapy alone on balance, motor function, and activities of daily living in patients with Parkinson's disease. METHODS: A total of sixty patients with Parkinson's disease were randomized into three groups using lottery method; twenty with virtual reality therapy in addition to physical therapy (group A = VR + RPT), twenty with imagery therapy in addition to physical therapy (group B = MI + RPT), and twenty were treated with only routine physical therapy (group C = RPT). All patients were evaluated using the Unified Parkinson's Disease Rating Scale (UPDRS) for motor function and activities of daily living, the Berg balance scale (BBS) for balance, and the Activities-specific Balance Confidence Scale (ABCs) for balance confidence at baseline, six and twelve weeks, and one month after treatment discontinuation. The one-way ANOVA was used to compare the outcomes between three groups, and the repeated measures ANOVA was used to compare the outcomes within each of the three groups at a significance level of p-value = 0.05. RESULTS: According to UPDRS III, the VR + RPT group showed significant improvement in motor function, compared to the MI + RPT and RPT groups, as the Mean ± SD at baseline was 33.95 ± 3.501 and at the 12-week assessment was 17.20 ± 9.451 with a p-value = 0.001. In the VR + RPT group, the BBS score at baseline was 37.15 ± 3.437 and at 12th week was 50.10 ± 4.897 with a p-value = 0.019. Among the VR + RPT group, the ABCS score showed significant improvement as the M ± SD at baseline was 57.95 ± 4.629, and at the 12th week was 78.59 ± 6.386 with a p-value = 0.010. At baseline, the UPDRS II for activities of daily living in the VR + RPT group was 25.20 ± 3.036 and at 12th week it was 15.30 ± 2.364 with p-value of 0.000. CONCLUSION: The current study found that the combination of VR and RPT proved to be the most effective treatment method for improving balance, motor function, and activities of daily living in patients with Parkinson's disease when compared to MI + RPT or RPT alone.

Enhancing endurance performance with combined imagined and actual physical practice.

PURPOSE: The perception of effort exerts influence in determining task failure during endurance performance. Training interventions blending physical and cognitive tasks yielded promising results in enhancing performance. Motor imagery can decrease the perception of effort. Whether combining motor imagery and physical training improves endurance remains to be understood, and this was the aim of this study. METHODS: Participants (24 ± 3 year) were assigned to a motor imagery (n = 16) or a control (n = 17) group. Both groups engaged in physical exercises targeting the knee extensors (i.e., wall squat, 12 training sessions, 14-days), with participants from the motor imagery group also performing motor imagery. Each participant visited the laboratory Pre and Post-training, during which we assessed endurance performance through a sustained submaximal isometric knee extension contraction until task failure, at either 20% or 40% of the maximal voluntary contraction peak torque. Perceptions of effort and muscle pain were measured during the exercise. RESULTS: We reported no changes in endurance performance for the control group. Endurance performance in the motor imagery group exhibited significant improvements when the intensity of the sustained isometric exercise closely matched that used in training. These enhancements were less pronounced when considering the higher exercise intensity. No reduction in perception of effort was observed in both groups. There was a noticeable decrease in muscle pain perception within the motor imagery group Post training. CONCLUSION: Combining motor imagery and physical training may offer a promising avenue for enhancing endurance performance and managing pain in various contexts.

Classification of Motor Imagery Tasks Derived from Unilateral Upper Limb based on a Weight-optimized Learning Model.

BACKGROUND: The accuracy of decoding fine motor imagery (MI) tasks remains relatively low due to the dense distribution of active areas in the cerebral cortex. METHODS: To enhance the decoding of unilateral fine MI activity in the brain, a weight-optimized EEGNet model is introduced that recognizes six types of MI for the right upper limb, namely elbow flexion/extension, wrist pronation/supination and hand opening/grasping. The model is trained with augmented electroencephalography (EEG) data to learn deep features for MI classification. To address the sensitivity issue of the initial model weights to classification performance, a genetic algorithm (GA) is employed to determine the convolution kernel parameters for each layer of the EEGNet network, followed by optimization of the network weights through backpropagation. RESULTS: The algorithm's performance on the three joint classification is validated through experiment, achieving an average accuracy of 87.97%. The binary classification recognition rates for elbow joint, wrist joint, and hand joint are respectively 93.92%, 90.2%, and 94.64%. Thus, the product of the two-step accuracy value is obtained as the overall capability to distinguish the six types of MI, reaching an average accuracy of 81.74%. Compared to commonly used neural networks and traditional algorithms, the proposed method outperforms and significantly reduces the average error of different subjects. CONCLUSIONS: Overall, this algorithm effectively addresses the sensitivity of network parameters to initial weights, enhances algorithm robustness and improves the overall performance of MI task classification. Moreover, the method is applicable to other EEG classification tasks; for example, emotion and object recognition.

Fatigue in children using motor imagery and P300 brain-computer interfaces.

BACKGROUND: Brain-computer interface (BCI) technology offers children with quadriplegic cerebral palsy unique opportunities for communication, environmental exploration, learning, and game play. Research in adults demonstrates a negative impact of fatigue on BCI enjoyment, while effects on BCI performance are variable. To date, there have been no pediatric studies of BCI fatigue. The purpose of this study was to assess the effects of two different BCI paradigms, motor imagery and visual P300, on the development of self-reported fatigue and an electroencephalography (EEG) biomarker of fatigue in typically developing children. METHODS: Thirty-seven typically-developing school-aged children were recruited to a prospective, crossover study. Participants attended three sessions: (A) motor imagery-BCI, (B) visual P300-BCI, and (C) video viewing (control). The motor imagery task involved an imagined left- or right-hand squeeze. The P300 task involved attending to one square on a 3 × 3 grid during a random single flash sequence. Each paradigm had respective calibration periods and a similar visual counting game. Primary outcomes were self-reported fatigue and the power of the EEG alpha band both collected during resting-state periods pre- and post-task. Self-reported fatigue was measured using a 10-point visual analog scale. EEG alpha band power was calculated as the integrated power spectral density from 8 to 12 Hz of the EEG spectrum. RESULTS: Thirty-two children completed the protocol (age range 7-16, 63% female). Self-reported fatigue and EEG alpha band power increased across all sessions (F(1,155) = 33.9, p < 0.001; F = 5.0(1,149), p = 0.027 respectively). No differences in fatigue development were observed between session types. There was no correlation between self-reported fatigue and EEG alpha band power change. BCI performance varied between participants and paradigms as expected but was not associated with self-reported fatigue or EEG alpha band power. CONCLUSION: Short periods (30-mintues) of BCI use can increase self-reported fatigue and EEG alpha band power to a similar degree in children performing motor imagery and P300 BCI paradigms. Performance was not associated with our measures of fatigue; the impact of fatigue on useability and enjoyment is unclear. Our results reflect the variability of fatigue and the BCI experience more broadly in children and warrant further investigation.

Motor imagery-based brain-computer interface rehabilitation programs enhance upper extremity performance and cortical activation in stroke patients.

BACKGROUND: The most challenging aspect of rehabilitation is the repurposing of residual functional plasticity in stroke patients. To achieve this, numerous plasticity-based clinical rehabilitation programs have been developed. This study aimed to investigate the effects of motor imagery (MI)-based brain-computer interface (BCI) rehabilitation programs on upper extremity hand function in patients with chronic hemiplegia. DESIGN: A 2010 Consolidated Standards for Test Reports (CONSORT)-compliant randomized controlled trial. METHODS: Forty-six eligible stroke patients with upper limb motor dysfunction participated in the study, six of whom dropped out. The patients were randomly divided into a BCI group and a control group. The BCI group received BCI therapy and conventional rehabilitation therapy, while the control group received conventional rehabilitation only. The Fugl-Meyer Assessment of the Upper Extremity (FMA-UE) score was used as the primary outcome to evaluate upper extremity motor function. Additionally, functional magnetic resonance imaging (fMRI) scans were performed on all patients before and after treatment, in both the resting and task states. We measured the amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), z conversion of ALFF (zALFF), and z conversion of ReHo (ReHo) in the resting state. The task state was divided into four tasks: left-hand grasping, right-hand grasping, imagining left-hand grasping, and imagining right-hand grasping. Finally, meaningful differences were assessed using correlation analysis of the clinical assessments and functional measures. RESULTS: A total of 40 patients completed the study, 20 in the BCI group and 20 in the control group. Task-related blood-oxygen-level-dependent (BOLD) analysis showed that when performing the motor grasping task with the affected hand, the BCI group exhibited significant activation in the ipsilateral middle cingulate gyrus, precuneus, inferior parietal gyrus, postcentral gyrus, middle frontal gyrus, superior temporal gyrus, and contralateral middle cingulate gyrus. When imagining a grasping task with the affected hand, the BCI group exhibited greater activation in the ipsilateral superior frontal gyrus (medial) and middle frontal gyrus after treatment. However, the activation of the contralateral superior frontal gyrus decreased in the BCI group relative to the control group. Resting-state fMRI revealed increased zALFF in multiple cerebral regions, including the contralateral precentral gyrus and calcarine and the ipsilateral middle occipital gyrus and cuneus, and decreased zALFF in the ipsilateral superior temporal gyrus in the BCI group relative to the control group. Increased zReHo in the ipsilateral cuneus and contralateral calcarine and decreased zReHo in the contralateral middle temporal gyrus, temporal pole, and superior temporal gyrus were observed post-intervention. According to the subsequent correlation analysis, the increase in the FMA-UE score showed a positive correlation with the mean zALFF of the contralateral precentral gyrus (r = 0.425, P < 0.05), the mean zReHo of the right cuneus (r = 0.399, P < 0.05). CONCLUSION: In conclusion, BCI therapy is effective and safe for arm rehabilitation after severe poststroke hemiparesis. The correlation of the zALFF of the contralateral precentral gyrus and the zReHo of the ipsilateral cuneus with motor improvements suggested that these values can be used as prognostic measures for BCI-based stroke rehabilitation. We found that motor function was related to visual and spatial processing, suggesting potential avenues for refining treatment strategies for stroke patients. TRIAL REGISTRATION: The trial is registered in the Chinese Clinical Trial Registry (number ChiCTR2000034848, registered July 21, 2020).

Anodal transcranial direct current stimulation does not enhance the effects of motor imagery training of a sequential finger-tapping task in young adults.

When applied over the primary motor cortex (M1), anodal transcranial direct current stimulation (a-tDCS) could enhance the effects of a single motor imagery training (MIt) session on the learning of a sequential finger-tapping task (SFTT). This study aimed to investigate the effect of a-tDCS on the learning of an SFTT during multiple MIt sessions. Two groups of 16 healthy young adults participated in three consecutive MIt sessions over 3 days, followed by a retention test 1 week later. They received active or sham a-tDCS during a MIt session in which they mentally rehearsed an eight-item complex finger sequence with their left hand. Before and after each session, and during the retention test, they physically repeated the sequence as quickly and accurately as possible. Both groups (i) improved their performance during the first two sessions, showing online learning; (ii) stabilised the level they reached during all training sessions, reflecting offline consolidation; and (iii) maintained their performance level one week later, showing retention. However, no significant difference was found between the groups, regardless of the MSL stage. These results emphasise the importance of performing several MIt sessions to maximise performance gains, but they do not support the additional effects of a-tDCS.

How Integration of a Brain-Machine Interface and Obstacle Detection System Can Improve Wheelchair Control via Movement Imagery.

This study presents a human-computer interaction combined with a brain-machine interface (BMI) and obstacle detection system for remote control of a wheeled robot through movement imagery, providing a potential solution for individuals facing challenges with conventional vehicle operation. The primary focus of this work is the classification of surface EEG signals related to mental activity when envisioning movement and deep relaxation states. Additionally, this work presents a system for obstacle detection based on image processing. The implemented system constitutes a complementary part of the interface. The main contributions of this work include the proposal of a modified 10-20-electrode setup suitable for motor imagery classification, the design of two convolutional neural network (CNNs) models employed to classify signals acquired from sixteen EEG channels, and the implementation of an obstacle detection system based on computer vision integrated with a brain-machine interface. The models developed in this study achieved an accuracy of 83% in classifying EEG signals. The resulting classification outcomes were subsequently utilized to control the movement of a mobile robot. Experimental trials conducted on a designated test track demonstrated real-time control of the robot. The findings indicate the feasibility of integration of the obstacle detection system for collision avoidance with the classification of motor imagery for the purpose of brain-machine interface control of vehicles. The elaborated solution could help paralyzed patients to safely control a wheelchair through EEG and effectively prevent unintended vehicle movements.

Motor Imagery EEG Signal Classification Using Distinctive Feature Fusion with Adaptive Structural LASSO.

A motor imagery brain-computer interface connects the human brain and computers via electroencephalography (EEG). However, individual differences in the frequency ranges of brain activity during motor imagery tasks pose a challenge, limiting the manual feature extraction for motor imagery classification. To extract features that match specific subjects, we proposed a novel motor imagery classification model using distinctive feature fusion with adaptive structural LASSO. Specifically, we extracted spatial domain features from overlapping and multi-scale sub-bands of EEG signals and mined discriminative features by fusing the task relevance of features with spatial information into the adaptive LASSO-based feature selection. We evaluated the proposed model on public motor imagery EEG datasets, demonstrating that the model has excellent performance. Meanwhile, ablation studies and feature selection visualization of the proposed model further verified the great potential of EEG analysis.

Adaptive Time-Frequency Segment Optimization for Motor Imagery Classification.

Motor imagery (MI)-based brain-computer interface (BCI) has emerged as a crucial method for rehabilitating stroke patients. However, the variability in the time-frequency distribution of MI-electroencephalography (EEG) among individuals limits the generalizability of algorithms that rely on non-customized time-frequency segments. In this study, we propose a novel method for optimizing time-frequency segments of MI-EEG using the sparrow search algorithm (SSA). Additionally, we apply a correlation-based channel selection (CCS) method that considers the correlation coefficient of features between each pair of EEG channels. Subsequently, we utilize a regularized common spatial pattern method to extract effective features. Finally, a support vector machine is employed for signal classification. The results on three BCI datasets confirmed that our algorithm achieved better accuracy (99.11% vs. 94.00% for BCI Competition III Dataset IIIa, 87.70% vs. 81.10% for Chinese Academy of Medical Sciences dataset, and 87.94% vs. 81.97% for BCI Competition IV Dataset 1) compared to algorithms with non-customized time-frequency segments. Our proposed algorithm enables adaptive optimization of EEG time-frequency segments, which is crucial for the development of clinically effective motor rehabilitation.

Optimal Channel Selection of Multiclass Motor Imagery Classification Based on Fusion Convolutional Neural Network with Attention Blocks.

The widely adopted paradigm in brain-computer interfaces (BCIs) involves motor imagery (MI), enabling improved communication between humans and machines. EEG signals derived from MI present several challenges due to their inherent characteristics, which lead to a complex process of classifying and finding the potential tasks of a specific participant. Another issue is that BCI systems can result in noisy data and redundant channels, which in turn can lead to increased equipment and computational costs. To address these problems, the optimal channel selection of a multiclass MI classification based on a Fusion convolutional neural network with Attention blocks (FCNNA) is proposed. In this study, we developed a CNN model consisting of layers of convolutional blocks with multiple spatial and temporal filters. These filters are designed specifically to capture the distribution and relationships of signal features across different electrode locations, as well as to analyze the evolution of these features over time. Following these layers, a Convolutional Block Attention Module (CBAM) is used to, further, enhance EEG signal feature extraction. In the process of channel selection, the genetic algorithm is used to select the optimal set of channels using a new technique to deliver fixed as well as variable channels for all participants. The proposed methodology is validated showing 6.41% improvement in multiclass classification compared to most baseline models. Notably, we achieved the highest results of 93.09% for binary classes involving left-hand and right-hand movements. In addition, the cross-subject strategy for multiclass classification yielded an impressive accuracy of 68.87%. Following channel selection, multiclass classification accuracy was enhanced, reaching 84.53%. Overall, our experiments illustrated the efficiency of the proposed EEG MI model in both channel selection and classification, showing superior results with either a full channel set or a reduced number of channels.

Development and Validation of the Combined Action Observation and Motor Imagery Ability Questionnaire.

Combined use of action observation and motor imagery (AOMI) is an increasingly popular motor-simulation intervention, which involves observing movements on video while simultaneously imagining the feeling of movement execution. Measuring and reporting participant imagery-ability characteristics are essential in motor-simulation research, but no measure of AOMI ability currently exists. Accordingly, the AOMI Ability Questionnaire (AOMI-AQ) was developed to address this gap in the literature. In Study 1, two hundred eleven participants completed the AOMI-AQ and the kinesthetic imagery subscales of the Movement Imagery Questionnaire-3 and Vividness of Motor Imagery Questionnaire-2. Following exploratory factor analysis, an 8-item AOMI-AQ was found to correlate positively with existing motor-imagery measures. In Study 2, one hundred seventy-four participants completed the AOMI-AQ for a second time after a period of 7-10 days. Results indicate a good test-retest reliability for the AOMI-AQ. The new AOMI-AQ measure provides a valid and reliable tool for researchers and practitioners wishing to assess AOMI ability.