Imagined Movements Research Articles

A Landscape of Words: Ireland, Britain and the Poetics of Space, 700–1250

A Landscape of Words: Ireland, Britain and the Poetics of Space, 700–1250

Comparison of variational mode decomposition and empirical wavelet transform methods on EEG signals for motor imaginary applications

Devising a reliable method for implementing brain computer interface (BCI) systems using electroencephalogram (EEG) signals is proposed. Applicability of two modal decomposition methods, variational mode decomposition (VMD) and empirical wavelet transform (EWT) on EEG signals for identifying the four different motor imaginary movements by the investigation of event-related desynchronisation (ERD) activity in the Mu-beta rhythm of EEG signals is analysed and compared. The EEG signals from each electrode corresponding to the sensorimotor cortex area of the brain are decomposed using VMD and EWT methods. Each decomposed modes are modelled using auto regressive (AR) modeling and feature vector is formed using the AR model parameters. On classification, better accuracy is perceived for VMD method in comparison with EWT and common spatial pattern (CSP) methods developed on the same dataset.

Imaginary Control of a Mobile Vehicle Using Deep Learning Algorithm: A Brain Computer Interface Study

Controlling a remote mobile vehicle using electroencephalograph (EEG) signals is still a challenge specially achieving a high degree of accuracy and precision. In the present study, the focus is on implementing an efficient feature space in a deep-based learning (DL) algorithm for a single trial application. More specifically, a boosting feature algorithm by means of long short-term memory (LSTM) networks are implemented in a deep auto-encoder (DAE) algorithm for producing an effective feature space tp identify event related desynchronization/event related synchronization (ERD/ERS) patters in EEG signals. For this purpose, three different DL-based algorithms are implemented that the models are based on a convolutional neural network (CNN), DAE, and LSTM networks to extract and boost the main features. In addition, our previous improved support vector machine (SVM)-based algorithm is employed to consider the potential of SVM and implemented DL-based algorithms for a two classes identification. To consider the efficiency of our implemented methods, algorithms are employed for control of a remote mobile vehicle in an imaginary right-hand opening and making a right-hand fist task. In our experiment, eleven subjects participated in an imaginary movement task. In the experiment, the displayed movement pictures were colored in yellow and red colors for stimulating brain to generate stronger ERD/ERS patterns. Results showed that the proposed algorithm by using the boosting technique significantly increased the accuracy with a higher precision of 73.31% ± 0.03. The proposed method enabled the DL algorithm to be used in single trial experiments.

A multi-feature fusion decoding study for unilateral upper-limb fine motor imagery.

To address the fact that the classical motor imagination paradigm has no noticeable effect on the rehabilitation training of upper limbs in patients after stroke and the corresponding feature extraction algorithm is limited to a single domain, this paper describes the design of a unilateral upper-limb fine motor imagination paradigm and the collection of data from 20 healthy people. It presents a feature extraction algorithm for multi-domain fusion and compares the common spatial pattern (CSP), improved multiscale permutation entropy (IMPE) and multi-domain fusion features of all participants through the use of decision tree, linear discriminant analysis, naive Bayes, a support vector machine, k-nearest neighbor and ensemble classification precision algorithms in the ensemble classifier. For the same subject, the average classification accuracy improvement of the same classifier for multi-domain feature extraction relative to CSP feature results went up by 1.52%. The average classification accuracy improvement of the same classifier went up by 32.87% relative to the IMPE feature classification results. This study's unilateral fine motor imagery paradigm and multi-domain feature fusion algorithm provide new ideas for upper limb rehabilitation after stroke.

The Effect of Motor Imagery on the Excitability of Spinal Segmentary Reflexes in Restless Legs Syndrome Patients

Context: Restless legs syndrome (RLS) is a neurological sleep disorder which causes an overwhelming urge to move the legs. However, this spinal excitability can be decreased through the use some motor movements such as walking or stretching. Aims: This study aimed to investigate the effect of motor imagery (MI) on spinal excitability in relation to the H reflex (HR). Settings and Design: In this study, 11 patients diagnosed with RLS (3 males and 8 females, mean age: 41.2) and 14 controls (8 males and 7 females, mean age: 38.4) were tested. HR was studied while participants in the supine position were imagining walking and also while imagining both dorsiflexion (DF) and plantar flexion (PF). Results: There was significant decrease in the Hmax/Mmax at 90° DF in both groups (p = 0.002, p = 0.001). There was no significant decrease in the RLS group on imagery; however, there was a significant decrease in the control group with movement imagination compared to the resting state (p = 0.021). There was no significant increase in Hmax/Mmax at 135° PF in both groups on movement and imagery. There was a decrease in the ratio on the imagery of walking in the RLS group (p = 0.038), but the same ratio increased in the control group (p = 0.010). Conclusion: As motor movements decrease corticospinal excitability in RLS, the imagery of movement mimicking the actmovement can relieve the symptoms of RLS. As a conclusion, further electrophysiological studies can be useful to gauge the effects of MI on spinal excitability in RLS.

The Role of Embodied Simulation and Visual Imagery in Emotional Contagion with Music

Emotional contagion has been explained as arising from embodied simulation. The two most accepted theories of music-induced emotions presume a mechanism of internal mimicry: the BRECVEMA framework proposes that the melodic aspect of music elicits internal mimicry leading to the induction of basic emotions in the listener, and the Multifactorial Process Model proposes that the observation or imagination of motor expressions of the musicians elicits muscular and neural mimicry, and emotional contagion. Two behavioral studies investigated whether, and to what extent, mimicry is responsible for emotion contagion, and second, to what extent context for affective responses in the form of visual imagery moderates emotional responses. Experiment 1 tested whether emotional contagion is influenced by mimicry by manipulating explicit vocal and motor mimicry. In one condition, participants engaged in mimicry of the melodic aspects of the music by singing along with the music, and in another, participants engaged in mimicry of the musician’s gestures when producing the music, by playing along (“air guitar”-style). The experiment did not find confirmatory evidence for either hypothesized simulation mechanism, but it did provide evidence of spontaneous visual imagery consistent with the induced and perceived emotions. Experiment 2 used imagined rather than performed mimicry, but found no association between imagined motor simulation and emotional intensity. Emotional descriptions read prior to hearing the music influenced the type of perceived and induced emotions and support the prediction that visual imagery and associated semantic knowledge shape listeners’ affective experiences with music. The lack of evidence for the causal role of embodied simulation suggests that current theorization of emotion contagion by music needs refinement to reduce the role of simulation relative to other mechanisms. Evidence for induction of affective states that can be modulated by contextual and semantic associations suggests a model of emotion induction consistent with constructionist accounts.

Comparing between Different Sets of Preprocessing, Classifiers, and Channels Selection Techniques to Optimise Motor Imagery Pattern Classification System from EEG Pattern Recognition

The ability to control external devices through thought is increasingly becoming a reality. Human beings can use the electrical signals of their brain to interact or change the surrounding environment and more. The development of this technology called brain-computer interface (BCI) will increasingly allow people with motor disabilities to communicate or use assistive devices to walk, manipulate objects and communicate. Using data from the PhysioNet database, this study implemented a pattern classification system for use in a BCI on 109 healthy volunteers during real movement activities and motor imagery recorded by 64-channels electroencephalography (EEG) system. Different classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Trees (TREE) were applied on different combinations of EEG channels. Starting from two channels (C3, C4 and CP3 and CP4) positioned on the contralateral and ipsilateral sensorimotor cortex, the Region of Interest (RoI) centred on C3/Cp3 and C4/Cp4 and, finally, a data-driven automatic channels selection was tested to explore the best channel combination able to increase the classification accuracy. The results showed that the proposed automatic channels selection was able to significantly improve the performance of each classifier achieving 98% of accuracy for classification of real and imagined hand movement (sensitivity = 97%, specificity = 99%, AUC = 0.99) by SVM. While the accuracy of the classification between the imagery of hand and foot movements was 91% (sensitivity = 87%, specificity = 86%, AUC = 0.93) also with SVM. In the proposed approach, the data-driven automatic channels selection outperforms classical a priori channel selection models such as C3/C4, Cp3/Cp4, or RoIs around those channels with the utmost accuracy to help remove the boundaries of human communication and improve the quality of life of people with disabilities.

Durağan Durum Görsel Uyarılmış Potansiyellerin Uyarı Frekansını Kestirmek için Wigner-Ville Dağılım Özniteliklerinin Değerlendirilmesi

Brain Computer Interface (BCI) is a system that enables people to communicate with the outside world and control various electronic devices by interpreting only brain activity (motor movement imagination, emotional state, any focused visual or auditory stimulus, etc.). The visual stimulation based recording is one of the most popular methods among various electroencephalography (EEG) recording methods. Steady-state visual-evoked potentials (SSVEPs) where visual objects are blinking at a fixed frequency play an important role due to their high signal-to-noise ratio and higher information transfer rate in BCI applications. However, the design of multiple (more than 3) commands systems in SSVEPs based BCI systems is limited. The different approaches are recommended to overcome these problems. In this study, an approach based on machine learning is proposed to determine stimulating frequency in SSVEP signals. The data set (AVI SSVEP Dataset) is obtained through open access from the internet for simulations. The dataset includes EEG signals that was recorded when subjects looked at a flickering frequency at seven different frequencies (6-6.5-7-7.5-8.2-9.3-10Hz). In the machine learning-based approach Wigner-Ville Distribution (WVD) is used and features are extracted using Time-Frequency (TF) representations of EEG signals. These features are classified by Decision Tree, Linear Discriminant Analysis (LDA), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Naive Bayes, Ensemble Learning classifiers. Simulation results demonstrate that the proposed approach achieved promising accuracy rates for 7 command SSVEP systems. As a consequence, the maximum accuracy is achieved in the Ensemble Learning classifier with 47.60%.

Ampirik Dalgacık Dönüşümünde Zaman-Frekans Özniteliklerini Kullanarak EEG tabanlı Uzamsal Dikkat Kayması Tespiti

The human nervous system has over 100b nerve cells, of which the majority are located in the brain. Electrical alterations, Electroencephalogram (EEG), occur through the interaction of the nerves. EEG is utilized to evaluate event-related potentials, imaginary motor tasks, neurological disorders, spatial attention shifts, and more. In this study, We experimented with 29-channel EEG recordings from 18 healthy individuals. Each recording was decomposed using Empirical Wavelet Transform, a time-frequency domain analysis technique at the feature extraction stage. The statistical features of the modulations were calculated to feed the conventional machine learning algorithms. The proposal model achieved the best spatial attention shifts detection accuracy using the Decision Tree algorithm with a rate of 89.24%.

Ampirik Dalgacık Dönüşümünde Zaman-Frekans Özniteliklerini Kullanarak EEG tabanlı Uzamsal Dikkat Kayması Tespiti

The human nervous system has over 100b nerve cells, of which the majority are located in the brain. Electrical alterations, Electroencephalogram (EEG), occur through the interaction of the nerves. EEG is utilized to evaluate event-related potentials, imaginary motor tasks, neurological disorders, spatial attention shifts, and more. In this study, We experimented with 29-channel EEG recordings from 18 healthy individuals. Each recording was decomposed using Empirical Wavelet Transform, a time-frequency domain analysis technique at the feature extraction stage. The statistical features of the modulations were calculated to feed the conventional machine learning algorithms. The proposal model achieved the best spatial attention shifts detection accuracy using the Decision Tree algorithm with a rate of 89.24%.

Motor Imagination of Lower Limb Movements at Different Frequencies.

Motor imagination (MI) is the mental process of only imagining an action without an actual movement. Research on MI has made significant progress in feature information detection and machine learning decoding algorithms, but there are still problems, such as a low overall recognition rate and large differences in individual execution effects, which make the development of MI run into a bottleneck. Aiming at solving this bottleneck problem, the current study optimized the quality of the MI original signal by “enhancing the difficulty of imagination tasks,” conducted the qualitative and quantitative analyses of EEG rhythm characteristics, and used quantitative indicators, such as ERD mean value and recognition rate. Research on the comparative analysis of the lower limb MI of different tasks, namely, high-frequency motor imagination (HFMI) and low-frequency motor imagination (LFMI), was conducted. The results validate the following: the average ERD of HFMI (−1.827) is less than that of LFMI (−1.3487) in the alpha band, so did (−3.4756 < −2.2891) in the beta band. In the alpha and beta characteristic frequency bands, the average ERD of HFMI is smaller than that of LFMI, and the ERD values of the two are significantly different (p=0.0074 < 0.01; r = 0.945). The ERD intensity STD values of HFMI are less than those of LFMI. which suggests that the ERD intensity individual difference among the subjects is smaller in the HFMI mode than in the LFMI mode. The average recognition rate of HFMI is higher than that of LFMI (87.84% > 76.46%), and the recognition rate of the two modes is significantly different (p=0.0034 < 0.01; r = 0.429). In summary, this research optimizes the quality of MI brain signal sources by enhancing the difficulty of imagination tasks, achieving the purpose of improving the overall recognition rate of the lower limb MI of the participants and reducing the differences of individual execution effects and signal quality among the subjects.

EEG Classification of Imaginary Left and Right Leg Movements based on Multivariate Empirical Mode Decomposition and Non-Linear Support Vector Machine

Motor-imagery brain-computer interfaces, as rehabilitation tools for motor-disabled individuals, could inherently enrich neuroplasticity and subsequently restore mobility. However, this endeavour's significant challenge is classifying left and right leg motor imagery tasks from non-stationary EEG signals. A subject-independent feature extraction method is essential in a BCI system, and this work involves developing a subject-independent algorithm to classify left/right leg motion intention. The Multivariate Empirical Mode Decomposition was used to decompose EEG during left and right foot movements during imagery tasks. We validated our proposed algorithm using open-access motor imagery data to detect the user's mental intention from EEG. Five subjects of various performance categories with almost 150 trials for each left/right leg MI of hand/leg/tongue, HaLT Paradigm, utilizing C3, C4, and Cz channels were examined to generalize this study to all subjects. A set of statistical features were extracted from the intrinsic mode functions, and the most relevant features were selected for classification using Sequential Floating Feature Selection. Different classifiers were trained using extracted features, and their performances' were evaluated. The findings suggest that the non-linear support vector machine is the best classification model, resulting in the mean classification sensitivity, specificity, precision, negative predictive value, F-measure, 98.15%, 90.74%, 91.97%, 98.33%, 94.72%, 94.44%, respectively. The proposed subject-independent signal processing method significantly improved the offline calibration mode by eliminating the frequency selection step, making it the common-used method for different types of MI-based BCI participants. Offline evaluations suggest that it can lead to significant increases in classification accuracy in comparison to current approaches.

Imaginary Finger Movements Decoding Using Empirical Mode Decomposition and a Stacked BiLSTM Architecture

Motor Imagery Electroencephalogram (MI-EEG) signals are widely used in Brain-Computer Interfaces (BCI). MI-EEG signals of large limbs movements have been explored in recent researches because they deliver relevant classification rates for BCI systems. However, smaller and noisy signals corresponding to hand-finger imagined movements are less frequently used because they are difficult to classify. This study proposes a method for decoding finger imagined movements of the right hand. For this purpose, MI-EEG signals from C3, Cz, P3, and Pz sensors were carefully selected to be processed in the proposed framework. Therefore, a method based on Empirical Mode Decomposition (EMD) is used to tackle the problem of noisy signals. At the same time, the sequence classification is performed by a stacked Bidirectional Long Short-Term Memory (BiLSTM) network. The proposed method was evaluated using k-fold cross-validation on a public dataset, obtaining an accuracy of 82.26%.

Time to imagine moving: Simulated motor activity affects time perception

Sensing the passage of time is important for countless daily tasks, yet time perception is easily influenced by perception, cognition, and emotion. Mechanistic accounts of time perception have traditionally regarded time perception as part of central cognition. Since proprioception, action execution, and sensorimotor contingencies also affect time perception, perception-action integration theories suggest motor processes are central to the experience of the passage of time. We investigated whether sensory information and motor activity may interactively affect the perception of the passage of time. Two prospective timing tasks involved timing a visual stimulus display conveying optical flow at increasing or decreasing velocity. While doing the timing tasks, participants were instructed to imagine themselves moving at increasing or decreasing speed, independently of the optical flow. In the direct-estimation task, the duration of the visual display was explicitly judged in seconds while in the motor-timing task, participants were asked to keep a constant pace of tapping. The direct-estimation task showed imagining accelerating movement resulted in relative overestimation of time, or time dilation, while decelerating movement elicited relative underestimation, or time compression. In the motor-timing task, imagined accelerating movement also accelerated tapping speed, replicating the time-dilation effect. The experiments show imagined movement affects time perception, suggesting a causal role of simulated motor activity. We argue that imagined movements and optical flow are integrated by temporal unfolding of sensorimotor contingencies. Consequently, as physical time is relative to spatial motion, so too is perception of time relative to imaginary motion.

F-Value Time-Frequency Analysis: Between-Within Variance Analysis.

Studies on brain mechanisms enable us to treat various brain diseases and develop diverse technologies for daily life. Therefore, an analysis method of neural signals is critical, as it provides the basis for many brain studies. In many cases, researchers want to understand how neural signals change according to different conditions. However, it is challenging to find distinguishing characteristics, and doing so requires complex statistical analysis. In this study, we propose a novel analysis method, FTF (F-value time-frequency) analysis, that applies the F-value of ANOVA to time-frequency analysis. The proposed method shows the statistical differences among conditions in time and frequency. To evaluate the proposed method, electroencephalography (EEG) signals were analyzed using the proposed FTF method. The EEG signals were measured during imagined movement of the left hand, right hand, foot, and tongue. The analysis revealed the important characteristics which were different among different conditions and similar within the same condition. The FTF analysis method will be useful in various fields, as it allows researchers to analyze how frequency characteristics vary according to different conditions.

A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface

Objective. Brain-computer interface (BCI) aims to establish communication paths between the brain processes and external devices. Different methods have been used to extract human intentions from electroencephalography (EEG) recordings. Those based on motor imagery (MI) seem to have a great potential for future applications. These approaches rely on the extraction of EEG distinctive patterns during imagined movements. Techniques able to extract patterns from raw signals represent an important target for BCI as they do not need labor-intensive data pre-processing. Approach. We propose a new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a ‘baseline’ class) using a data augmentation algorithm and a limited number of EEG channels. In addition, we present a transfer learning method used to extract critical features from the EEG group dataset and then to customize the model to the single individual by training its late layers with only 12-min individual-related data. Main results. The model tested with the ‘EEG Motor Movement/Imagery Dataset’ outperforms the current state-of-the-art models by achieving a accuracy at the group level. In addition, the transfer learning approach we present achieves an average accuracy of . Significance. The proposed methods could foster the development of future BCI applications relying on few-channel portable recording devices and individual-based training.

Review: The Palestinian Idea: Film, Media and the Radical Imagination, by Greg A. Burris

Review: The Palestinian Idea: Film, Media and the Radical Imagination, by Greg A. Burris

Effect of gait motor imagination therapy on improving motor function of stroke patients with hemiplegia

Effect of gait motor imagination therapy on improving motor function of stroke patients with hemiplegia

Affective EEG-Based Person Identification Using Channel Attention Convolutional Neural Dense Connection Network

In the biometric recognition mode, the use of electroencephalogram (EEG) for biometric recognition has many advantages such as anticounterfeiting and nonsteal ability. Compared with traditional biometrics, EEG biometric recognition is safer and more concealed. Generally, EEG-based biometric recognition is to perform person identification (PI) through EEG signals collected by performing motor imagination and visual evoked tasks. The aim of this paper is to improve the performance of different affective EEG-based PI using a channel attention mechanism of convolutional neural dense connection network (CADCNN net) approach. Channel attention mechanism (CA) is used to handle the channel information from the EEG, while convolutional neural dense connection network (DCNN net) extracts the unique biological characteristics information for PI. The proposed method is evaluated on the state-of-the-art affective data set HEADIT. The results indicate that CADCNN net can perform PI from different affective states and reach up to 95%-96% mean correct recognition rate. This significantly outperformed a random forest (RF) and multilayer perceptron (MLP). We compared our method with the state-of-the-art EEG classifiers and models of EEG biometrics. The results show that the further extraction of the feature matrix is more robust than the direct use of the feature matrix. Moreover, the CADCNN net can effectively and efficiently capture discriminative traits, thus generalizing better over diverse human states.

Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks.

The classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems. Aiming to achieve intelligent classification of motor imagery EEG types with high accuracy, a classification methodology using the wavelet packet decomposition (WPD) and the proposed deep residual convolutional networks (DRes-CNN) is proposed. Firstly, EEG waveforms are segmented into sub-signals. Then the EEG signal features are obtained through the WPD algorithm, and some selected wavelet coefficients are retained and reconstructed into EEG signals in their respective frequency bands. Subsequently, the reconstructed EEG signals were utilized as input of the proposed deep residual convolutional networks to classify EEG signals. Finally, EEG types of motor imagination are classified by the DRes-CNN classifier intelligently. The datasets from BCI Competition were used to test the performance of the proposed deep learning classifier. Classification experiments show that the average recognition accuracy of this method reaches 98.76%. The proposed method can be further applied to the BCI system of motor imagination control.