Motion Imagery Research Articles

Humans underestimate the movement range of their own hands

Motor planning and motor imagery are assumed to use veridical internal representations of the biomechanical properties of our limbs. Here, we report that people underestimate their hands’ range of motion. We used two tasks probing representations of own motion range, estimation and imagery, in which participants were supposed to judge their rotational hand movement ranges. In both tasks participants’ judgments were underestimated in three out of four cardinal directions. We suggest that this representational bias provides an optimal balance between movement efficiency and safety in face of the inherently stochastic nature of movement execution.

Depicting Humans, Animals, and Objects in Motion: The Effect of Implied Motion on Engagement and Persuasion in Advertising

Over the past several decades, visual imagery has become the dominant element in modern advertising. A common content strategy involves depicting humans, animals, or objects in the midst of motion. Whereas previous research indicates that implied motion images enhance persuasion, it is unclear whether this effect is unique to depictions of moving humans or if it also applies to depictions of moving animals (e.g., a dolphin jumping out of the water) and moving objects (e.g., a car driving on a street, a burger being tossed in the air). Across a set of seven experimental studies, we provide robust evidence that images depicting animate and inanimate motion increase the persuasiveness of an advertisement and that this effect occurs through enhanced engagement. Our findings further indicate that the level of engagement is influenced by the complexity of the depicted motion, with more complex, nonlinear movements eliciting greater engagement than simpler, linear movements. Overall, this research contributes to the advertising literature by providing an empirically grounded account of implied motion imagery and by helping marketers create more effective advertising.

The Study of Stylistics in the Novel Phosphorus, the Man Who Finds the Most Beautiful Place of Retno Mawarni’s Work

The aim of the research is to analyze the use of language styles in literature. The research method uses descriptive methods. The data source used is the novel Phosphor, Men Who Look for the Most Beautiful Places by Retno Mawarni, published by Sabil Yogyakarta in July 2013, with 370 pages. Based on the research results, it is known that there are eight types of imaging available; All types of imagery are used by the author in the novel Phosphorus, the man who found the most beautiful place by Retno Mawarni, namely visual imagery, auditory imagery, olfactory imagery, taste imagery, thought imagery, motion imagery, tactile imagery, and feeling imagery. Apart from imagery, a stylistic study based on rhetorical elements, namely the language style in the phosphorus novel, The Man Who Found the Most Beautiful Place by Retno Mawarni, shows that the comparative language style (figure of speech) used is the simile style (smile), the personification language style, and the allegorical language style. Contradictory language styles are hyperbole, language style, and irony. The overall style of language used by the author aims to increase the effect of the speech. The things that the author can put forward are: (1) for teaching literature in the results, additional teaching materials should be used in implementing Indonesian language and literature learning in schools; (2) for fans and lovers of literature, research results should be used as additional information as well as a reference in conducting literary research, especially regarding stylistics.

Diksi, Gaya Bahasa, dan Citraan dalam Cerkak Pacarku Aneh Karya Lina Maisaroh (Kajian Stilistika)

This study discusses diction, language style, and imagery in a story entitled “Pacarku Aneh” by Lina Maisaroh. The theory used is a stylistic study. The purpose of this study is to: describe the diction, style, and imagery contained in the cerkak. The method used in this study is a qualitative descriptive method, with the analysis technique carried out as reading and understanding the contents of the ingenuity. The research data is in the form of a short text sourced from the kompasiana web published in 2021 with the theme of love. The data collection technique used is a reading and recording technique. Data analysis techniques use interactive descriptive techniques that include data reduction, data classification, and conclusion. The result of this study is in a story entitled My Strange Boyfriend by Lina Maisaroh using denotative diction, connotative diction, auditory imagery, visual imagery, motion imagery, allegory language style, and affirmative language style.

The celestial ferry: Elena Shvarts’s ekphrasis as the sum of readerly experience

The article analyzes Elena Shvarts's late poem "Chinese Toy", where the poet describes the Kunstkamera exhibit in an unusual way, turning the mechanical principles of the ship motion and Chinese mythological imagery into a tool for blurring the boundaries between conception and givenness. The aim is to find how this magical realism, which reconceptualizes reading, is embedded in the understanding of reading that Elena Shvarts offers in the post-Soviet period of her work. With methodological reference to classical semiotic positions, Lotman's theory of oppositions and Jakobson's theory of shifters, it is ascertained that we are facing a reverse ekphrasis: the artifact itself demonstrates the principles of the animation of the static. Turning to Chinese mythology and Chinese mechanics allows us to reject the Western understanding of ekphrasis as a situative enlivening of the depicted and to accept the reinvention of cosmogony in China, which is expressed in the artifact mentioned in the poem. Shvarts performs a reduction of the sublime experience in order to overcome the persistent ideas about reading as constructive imagination and to show alternative mechanisms of reading. The new way of reading, implied by the poem, turns out to be a metacriticism of the former figure of the reader as a flaneur, an observer of the various fatal mechanics of the universe. Both in the lyrics and prose of the late Shvarts, a unified mechanics of the universe appears, in relation to which the reader's experience looks like a game, and the reader's imaginary is one of the toys in this game.

Optimizing motion imagery classification with limited channels using the common spatial pattern-based integrated algorithm.

The extraction of effective classification features from electroencephalogram (EEG) signals in motor imagery is a popular research topic. The Common Spatial Pattern (CSP) algorithm is widely employed in this field. However, the performance of the traditional CSP method depends significantly on the choice of a specific frequency band and channel number of EEG data. Furthermore, inter-class variance among these frequency bands and the limited number of available EEG channels can adversely affect the CSP algorithm's ability to extract meaningful features from the relevant signal frequency bands. We hypothesize that multiple Intrinsic Mode Functions (IMFS), into which the raw EEG signal is decomposed, can better capture the non-Gaussian characteristics of the signal, thus compensating for the limitations of the CSP algorithm when dealing with nonlinear and non-Gaussian distributed data with few channels. Therefore, this paper proposes a novel method that integrates Variational Mode Decomposition (VMD), Phase Space Reconstruction (PSR), and the CSP algorithm to address these issues. VMD is used to filter and enhance the quality of the collected data, PSR is employed to increase the effective data channels (data augmentation), and the subsequent CSP filtering can obtain signals with spatial features, which are decoded by Convolutional Neural Networks (CNN) for action decoding. This study utilizes self-collected EEG data to demonstrate that the new method can achieve a good classification accuracy of 82.30% on average, confirming the improved algorithm's effectiveness and feasibility. Furthermore, this study conducted validation on the publicly available BCI Competition IV dataset 2b, demonstrating an average classification accuracy of 87.49%.

Effect of Motion Imagery on Visual Motor Perception in Children with Autism Spectrum Disorder: A Randomized Control Trial

Background: Comprehension and visual-motor integration skills are still widely used in the psychological assessments of children. Recent evidence suggests that children with autism spectrum disorder (ASD) might have a general impairment in visual motion perception. Objectives: This study aimed to evaluate the effects of motion imagery on visual motor perception in children with ASD. Methods: In a before and after clinical trial study, 30 children with ASD were divided into two groups of 15 subjects, test and control. The intervention group received 3 sessions of 60 minutes per week for 15 weeks with movement visualization exercises. Before and after the intervention and after the follow-up period, visual motor perception and motor imagery questionnaires were completed and implemented. The data were analyzed using SPSS22 software (P ≤ 0.05). The Kolmogorov-Smirnov test was used to determine the normality of the data, and variance analysis was used to compare the averages of the two groups before and after the intervention and follow-up. Results: The results obtained in all subscales of motor imagery with P = 0.001 indicated that there was a statistically significant difference between the two groups in the pre-test and post-test scores. Additionally, the results obtained concerning visual motor perception showed that in all the subscales with P < 0.001, statistically significant differences could be observed after the intervention and in the 3 months of follow-up. Conclusions: Motion visualization is an easy and accessible method that has positive and effective effects on visual-motor perception skills. Treatment practitioners can use this method to improve ASD children.

A delayed matching task-based study on action sequence of motor imagery.

The way people imagine greatly affects performance of brain-computer interface (BCI) based on motion imagery (MI). Action sequence is a basic unit of imitation, learning, and memory for motor behavior. Whether it influences the MI-BCI is unknown, and how to manifest this influence is difficult since the MI is a spontaneous brain activity. To investigate the influence of the action sequence, this study proposes a novel paradigm named action sequences observing and delayed matching task to use images and videos to guide people to observe, match and reinforce the memory of sequence. Seven subjects' ERPs and MI performance are analyzed under four different levels of complexities or orders of the sequence. Results demonstrated that the action sequence in terms of complexity and sequence order significantly affects the MI. The complex action in positive order obtains stronger ERD/ERS and more pronounced MI feature distributions, and yields an MI classification accuracy that is 12.3% higher than complex action in negative order (p < 0.05). In addition, the ERP amplitudes derived from the supplementary motor area show a positive correlation to the MI. This study demonstrates a new perspective of improving imagery in the MI-BCI by considering the complexity and order of the action sequences, and provides a novel index for manifesting the MI performance by ERP.

Prefrontal cortex activity of active motion, cyclic electrical muscle stimulation, assisted motion, and imagery of wrist extension in stroke using fNIRS

ObjectivesThis study aimed to determine whether the prefrontal cortex (PFC) was activated during four training approaches for wrist extension in patients with stroke, including active motion, cyclic electrical muscle stimulation (EMS), assisted motion, and motor imagery (MI). Materials and MethodsWe conducted a cross-sectional study involving 16 patients with stroke, and adopted functional near-infrared spectroscopy (fNIRS) to observe PFC activity during four treatment paradigms. The beta value of 53 channels in fNIRS under each paradigm, compared to the baseline, was evaluated using single sample t-test. The one-way analysis of variance with post hoc analysis was employed to compare the difference of significantly activated channels among four treatment paradigms. ResultsThis study revealed that the active motion (t values ranging from 2.399 to 4.368, p values <0.05), as well as MI of wrist extension (t values ranging from 2.161 to 4.378, p values <0.05), significantly increased HBO concentration across the entire PFC. The cyclic EMS enhanced the activation of Broca's area and frontal pole (FP) (t values ranging from -2.540 to 2.303, p values <0.05). The assisted motion induced significant activation in Broca's area, dorsolateral prefrontal cortex, and FP (t values ranging from -2.226 to 3.056, p values <0.05). The difference in ΔHBO among the four tasks was seen in Broca's area, FP, and frontal eye field. ConclusionsActive wrist extension and MI activate most PFC areas, whereas assisted motion and single-use of cyclic EMS have limited effectiveness for PFC activation in stroke patients.

Graph Convolutional Neural Network with Multi-Scale Attention Mechanism for EEG-Based Motion Imagery Classification

Recently, deep learning has been widely used in the classification of EEG signals and achieved satisfactory results. However, the correlation between EEG electrodes is rarely considered, which has been proved that there are indeed connections between different brain regions. After considering the connections between EEG electrodes, the graph convolutional neural network is applied to detect human motor intents from EEG signals, where EEG data are transformed into graph data through phase lag index, time-domain and frequency-domain features with different signal bands. Meanwhile, a multi-scale attention mechanism is proposed to the network to improve the accuracy of classification. By using the multi-scale attention-based graph convolutional neural network, the accuracy of 93.22% is achieved with 10-fold cross-validation, which is higher than the compared methods which ignore the spatial correlations of EEG signals.

Wide Area Motion Imagery and the Colonial Antecedents of Surveillance

Wide Area Motion Imagery and the Colonial Antecedents of Surveillance

Multi-objective squirrel search algorithm for EEG feature selection

Feature selection plays a critical role in the application of Brain Computer Interface (BCI) systems. Many methods have been used to solve the feature selection problem, but they model it as a single-objective problem, considering only classification accuracy or number of features. To close this critical gap, we improve the squirrel search algorithm by combining it with the grid method, and propose a Multi-Objective Squirrel Search Algorithm (MOSSA) to solve the feature selection problem in BCI. We conduct experiments on three publicly available motion imagery datasets, and the experimental results reveal the best classification results of the method on dataset 1. The average classification accuracy of dataset 2 is 96.71%, with the number of selected features reduced to 18 on average. The highest classification accuracy of dataset 3 is 83.57% on the training set and 82.86% on the test set. In addition, we compare MOSSA with other algorithms and the results show the superiority of our proposed method in solving the feature selection problem. Finally, we combine MOSSA with an online application of BCI, where subjects visualize controlling the robot to perform the corresponding actions by the left and right hand movements. The average recognition rate of the three subjects is approximately 70%. In summary, the MOSSA is an effective method for solving the feature selection problem and is useful for the development of online applications of BCI.

Analisis Citraan Kumpulan Puisi Surat Kopi Karya Joko Pinurbo

This study aims to analyze the imagery of Joko Pinurbo's collection of Surat Kopi poetry. This type of research is a qualitative research using descriptive analysis method. The data sources in this study were 10 poetry titles contained in Joko Pinurbo's collection of Surat Kopi poems. Data collection techniques were carried out through documentation studies. Data analysis techniques were carried out through descriptive analysis. The results of this study, namely the imagery contained in the 10 poetry titles in the poetry ontology of Joko Pinurbo's Surat Kopi are 3 imagery, namely visual, auditory, and motion imagery. From all these findings, it can be concluded that the most dominant element of imagery in this poem is visual imagery.

Crowd-aware Thresholded Loss for Object Detection in Wide Area Motion Imagery

Crowd-aware Thresholded Loss for Object Detection in Wide Area Motion Imagery

Subject-Independent EEG Classification of Motor Imagery Based on Dual-Branch Feature Fusion.

A brain computer interface (BCI) system helps people with motor dysfunction interact with the external environment. With the advancement of technology, BCI systems have been applied in practice, but their practicability and usability are still greatly challenged. A large amount of calibration time is often required before BCI systems are used, which can consume the patient's energy and easily lead to anxiety. This paper proposes a novel motion-assisted method based on a novel dual-branch multiscale auto encoder network (MSAENet) to decode human brain motion imagery intentions, while introducing a central loss function to compensate for the shortcomings of traditional classifiers that only consider inter-class differences and ignore intra-class coupling. The effectiveness of the method is validated on three datasets, namely BCIIV2a, SMR-BCI and OpenBMI, to achieve zero calibration of the MI-BCI system. The results show that our proposed network displays good results on all three datasets. In the case of subject-independence, the MSAENet outperformed the other four comparison methods on the BCIIV2a and SMR-BCI datasets, while achieving F1_score values as high as 69.34% on the OpenBMI dataset. Our method maintains better classification accuracy with a small number of parameters and short prediction times, and the method achieves zero calibration of the MI-BCI system.

Towards neuromorphic brain-computer interfaces: Model and circuit Co-design of the spiking EEGNet

The compact end-to-end deep neural network model, EEGNet, scalable to various brain-computer interface (BCI) paradigms, has been realized in hardware but with low efficiency. Neuromorphic circuit with high efficiency has not yet been applied to BCI. In this paper, the activation-quantization-based (AQB) method that converts EEGNet to spiking neural network is proposed and optimized on the standard motion imagery dataset BCIC-IV-2a with an accuracy of 63.35%. The proposed circuit of the AQB EEGNet is implemented in FPGA, and shows an energy efficiency of 318μJ/inference, which is comparable to that of a low power MCU at 337μJ/inference. Estimation of the energy efficiency in neuromorphic hardware shows the high efficiency of 34μJ/inference, which is a huge improvement compared to previous works, and benefits the BCI for long-term use.

Decoding the Continuous Motion Imagery Trajectories of Upper Limb Skeleton Points for EEG-Based Brain–Computer Interface

In the field of brain–computer interface (BCI), brain decoding using electroencephalography (EEG) is an essential direction, and motion imagery EEG-based BCI can not only help rehabilitation of patients with physical disabilities, but also enhance the endurance and power of people. Most of the existing MI-based BCI studies are limited to discrete EEG classification or 3-D directional limb trajectory reconstruction. To suitable for the requirements of BCI systems in practical applications, here, we explored the decoding of trajectories of continuous nondirectional motion imagination in 3-D space based on Chinese sign language. We propose a motion imagery trajectory reconstruction Transformer (MITRT) model to decode the EEG signals of the subjects performing motion imagery, and obtain the positional changes in the 3-D space of the shoulder, elbow, and wrist skeleton points in the neural activity. We add the geometric constraint features of upper limb skeleton points to the model, and the MITRT decoding model can obtain prior knowledge to improve the reconstruction accuracy of spatial positions. To verify the decoding performance of our proposed model, we collected motor imagery (MI) EEG signals of 20 subjects based on Chinese sign language for experiments. The experimental results show that the average Pearson correlation coefficient of the six skeleton points was 0.975, which was significantly higher than the contrast models. This study is the first attempt to reconstruct multidirectional continuous nondirectional upper limb MI trajectories based on Chinese sign language. The experimental results show that it is feasible to decode and reconstruct imagined 3-D trajectories of human upper limb skeleton points from scalp EEG.

Joint Filter-Band-Combination and Multi-view CNN for Electroencephalogram Decoding.

Motor imagery (MI) electroencephalogram (EEG) signals have an important role in brain-computer interface (BCI) research. However, effectively decoding these signals remains a problem to be solved. Traditional EEG signal decoding algorithms rely on parameter design to extract features, whereas deep learning algorithms represented by convolution neural network (CNN) can automatically extract features, which is more suitable for BCI applications. However, when EEG data is taken as input in raw time series, traditional 1D-CNNs are unable to acquire both frequency domain and channel association information. To solve this problem, this study proposes a novel algorithm by inserting two modules into CNN. One is the Filter Band Combination (FBC) Module, which preserves as many frequency domain features as possible while maintaining the time domain characteristics of EEG. Another module is Multi-View structure that can extract features from the output of FBC module. To prevent over fitting, we used a cosine annealing algorithm with restart strategy to update the learning rate. The proposed algorithm was validated on the BCI competition dataset and the experiment dataset, using accuracy, standard deviation, and kappa coefficient. Compared with traditional decoding algorithms, our proposed algorithm achieved an improvement of the maximum average correct rate of 6.6% on the motion imagery 4-classes recognition mission and 11.3% on the 2-classes classification task.

Strengthening Continuity Coaching Goals (Sustainable Development Goals) through the Facility Development Plan and Poetry Image Extension to Elementary School Students in Dolok Ilir I (Bah Bolon) Dolok Batu Nanggar District, Simalungun Regency

The Simalungun Assisted Village Team conducted a preliminary survey based on secondary and primary data. The survey is primarily carried out through direct observation and in-depth discussions with stakeholders such as village heads and other elements of village leadership. The result of the discussion is the problem of wall fence facilities and understanding the image of poetry for students. The solution is to improve sustainable development goals. The USU Assisted Village Team carried out activity methods in the form of building wall fences and providing counseling to elementary school students about understanding the image of poetry, namely. (1) Vision Imagery; (2) Auditory Imagery; (3) Motion Imagery; (4) Tactile Imagery; (5) Olfactory Imagery; (6) Tasting Imagery; (7) Image of Feeling. Furthermore, it is hoped that several activity outcomes will be achieved, namely, (1) Being able to encourage the growth of community initiative and creativity to overcome problems through improving facilities and counseling on the understanding of poetry image (2) Produce scientific publications and publications to the public on Sustainable Development Goals (SDGS) through the Assisted Village in Dolok Ilir I Village, Dolok Batu Nanggar District, Simalungun Regency.

Citraan dalam Antologi Puisi Langit Air Langit Basah karya Akhmad T Bacco

The background of this research is the presence of motion imagery and auditory imagery in the poetry anthology of Langit Air Langit Basah by Akhmad T Bacco which raises the value of beauty and poetic poetry. The purpose of this study was to describe motion and auditory imagery in the anthology of the poem Langit Air Langit Basah by Akhmad T Bacco. This type of research is a qualitative research using descriptive method. This research was found 25 auditory imagery data and 36 motion data. The most dominant imagery is found in the poetry anthology Langit Air Langit Basah Akhmad T Bacco's literature is a motion image with 36 data because the themes raised in the poem are more about expressing sadness, criticism of injustice, arbitrariness, disappointment, and regret for life. Poets express all ideas, thoughts, and feelings in looking at something through words that are not clearly expressed or words that have connotations. While the imagery that was found the least was the gustatory image, which was 4 data. It can be concluded that the poet of the anthology of poetry Langit Air Langit Basah impressionism line. Impressionism is a literary genre that tries to describe a momentary impression of something the poem observes. Impressionism depicts something like a sketch, everything is not clearly depicted.