المؤلفون: Woo Hyung Lee, Min Gu Kang, Han Gil Seo, Moon Suk Bang, Yoon Jae Kim, Hyung Seok Nam, Byung Mo Oh, Sungwan Kim, Eunkyung Kim, Hyun Haeng Lee

المصدر: Scientific Reports, Vol 9, Iss 1, Pp 1-14 (2019)
Scientific Reports

مصطلحات موضوعية: Adult, Male, 0301 basic medicine, medicine.medical_specialty, Premotor cortex, lcsh:Medicine, Brain mapping, Article, 03 medical and health sciences, 0302 clinical medicine, Physical medicine and rehabilitation, Motor imagery, Mental chronometry, medicine, Humans, lcsh:Science, Brain–computer interface, Brain Mapping, Multidisciplinary, Hand Strength, medicine.diagnostic_test, Supplementary motor area, lcsh:R, Brain, Inferior parietal lobule, Magnetic Resonance Imaging, 030104 developmental biology, medicine.anatomical_structure, Cognitive control, Orbitofrontal cortex, lcsh:Q, Functional magnetic resonance imaging, Psychology, Psychomotor Performance, 030217 neurology & neurosurgery

الوصف: Motor imagery (MI) for target-oriented movements, which is a basis for functional activities of daily living, can be more appropriate than non-target-oriented MI as tasks to promote motor recovery or brain-computer interface (BCI) applications. This study aimed to explore different characteristics of brain activation among target-oriented kinesthetic imagery (KI) and visual imagery (VI) in the first-person (VI-1) and third-person (VI-3) perspectives. Eighteen healthy volunteers were evaluated for MI ability, trained for the three types of target-oriented MIs, and scanned using 3 T functional magnetic resonance imaging (fMRI) under MI and perceptual control conditions, presented in a block design. Post-experimental questionnaires were administered after fMRI. Common brain regions activated during the three types of MI were the left premotor area and inferior parietal lobule, irrespective of the MI modalities or perspectives. Contrast analyses showed significantly increased brain activation only in the contrast of KI versus VI-1 and KI versus VI-3 for considerably extensive brain regions, including the supplementary motor area and insula. Neural activity in the orbitofrontal cortex and cerebellum during VI-1 and KI was significantly correlated with MI ability measured by mental chronometry and a self-reported questionnaire, respectively. These results can provide a basis in developing MI-based protocols for neurorehabilitation to improve motor recovery and BCI training in severely paralyzed individuals.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e72c80f3ddfcc57b98ec837616127d11Test
http://link.springer.com/article/10.1038/s41598-019-49254-2Test

المؤلفون: Yang Zhang, Gege Dong, Jiali Xu, Fenqi Rong, Miao Yunjing, Yuandong Wang, Yanan Sun, Jiancai Leng, Fangzhou Xu, Han Li, Jincheng Li, Dongju Guo

المصدر: Scientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
Scientific Reports

مصطلحات موضوعية: Data Analysis, Imagery, Psychotherapy, Computer science, Transfer, Psychology, Science, medicine.medical_treatment, Interface (computing), Speech recognition, Electroencephalography, Convolutional neural network, Article, Deep Learning, Motor imagery, medicine, Humans, Brain–computer interface, Multidisciplinary, Rehabilitation, medicine.diagnostic_test, business.industry, Deep learning, Stroke Rehabilitation, Models, Theoretical, Brain-Computer Interfaces, Medicine, Artificial intelligence, business, Transfer of learning, Biomedical engineering, Algorithms, Neuroscience

الوصف: Deep learning networks have been successfully applied to transfer functions so that the models can be adapted from the source domain to different target domains. This study uses multiple convolutional neural networks to decode the electroencephalogram (EEG) of stroke patients to design effective motor imagery (MI) brain-computer interface (BCI) system. This study has introduced ‘fine-tune’ to transfer model parameters and reduced training time. The performance of the proposed framework is evaluated by the abilities of the models for two-class MI recognition. The results show that the best framework is the combination of the EEGNet and ‘fine-tune’ transferred model. The average classification accuracy of the proposed model for 11 subjects is 66.36%, and the algorithm complexity is much lower than other models.These good performance indicate that the EEGNet model has great potential for MI stroke rehabilitation based on BCI system. It also successfully demonstrated the efficiency of transfer learning for improving the performance of EEG-based stroke rehabilitation for the BCI system.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::293abf9ec28ad89d4504a5077a5a6b50Test
https://doi.org/10.1038/s41598-021-99114-1Test

المؤلفون: Nobuo Usui, Kenji Fueki, Noriyuki Wakabayashi, Masato Taira, Yuka Inamochi

المصدر: Scientific Reports
Scientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)

مصطلحات موضوعية: Adult, Male, medicine.medical_specialty, Brain activity and meditation, Science, medicine.medical_treatment, Movement, Precuneus, Dental diseases, Audiology, Motor Activity, Article, Angular gyrus, 03 medical and health sciences, 0302 clinical medicine, Motor imagery, Tongue, Motor control, medicine, Humans, Brain Mapping, Multidisciplinary, medicine.diagnostic_test, business.industry, Palate, Brain, 030206 dentistry, Adaptation, Physiological, Magnetic Resonance Imaging, medicine.anatomical_structure, Medicine, Female, Dentures, business, Functional magnetic resonance imaging, 030217 neurology & neurosurgery

الوصف: Successful adaptation to wearing dentures with palatal coverage may be associated with cortical activity changes related to tongue motor control. The purpose was to investigate the brain activity changes during tongue movement in response to a new oral environment. Twenty-eight fully dentate subjects (mean age: 28.6-years-old) who had no experience with removable dentures wore experimental palatal plates for 7 days. We measured tongue motor dexterity, difficulty with tongue movement, and brain activity using functional magnetic resonance imaging during tongue movement at pre-insertion (Day 0), as well as immediately (Day 1), 3 days (Day 3), and 7 days (Day 7) post-insertion. Difficulty with tongue movement was significantly higher on Day 1 than on Days 0, 3, and 7. In the subtraction analysis of brain activity across each day, activations in the angular gyrus and right precuneus on Day 1 were significantly higher than on Day 7. Tongue motor impairment induced activation of the angular gyrus, which was associated with monitoring of the tongue’s spatial information, as well as the activation of the precuneus, which was associated with constructing the tongue motor imagery. As the tongue regained the smoothness in its motor functions, the activation of the angular gyrus and precuneus decreased.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d954645d19dd18b1216564d51fd0b32dTest
http://europepmc.org/articles/PMC8260614Test

المؤلفون: Paweł Augustynowicz, Dariusz Zapała, Paulina Iwanowicz, Piotr Francuz

المصدر: Scientific Reports
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)

مصطلحات موضوعية: Adult, Male, medicine.medical_specialty, Science, Electroencephalography, Audiology, Alpha wave, 050105 experimental psychology, Article, Functional Laterality, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Rhythm, Motor imagery, medicine, Psychology, Humans, 0501 psychology and cognitive sciences, Kinesthesis, Neurorehabilitation, Multidisciplinary, medicine.diagnostic_test, 05 social sciences, Perspective (graphical), Kinesthetic learning, Visual cortex, medicine.anatomical_structure, Imagination, Medicine, Female, 030217 neurology & neurosurgery, Psychomotor Performance, Neuroscience

الوصف: Recent studies show that during a simple movement imagery task, the power of sensorimotor rhythms differs according to handedness. However, the effects of motor imagery perspectives on these differences have not been investigated yet. Our study aimed to check how handedness impacts the activity of alpha (8–13 Hz) and beta (15–30 Hz) oscillations during creating a kinesthetic (KMI) or visual-motor (VMI) representation of movement. Forty subjects (20 right-handed and 20 left-handed) who participated in the experiment were tasked with imagining sequential finger movement from a visual or kinesthetic perspective. Both the electroencephalographic (EEG) activity and behavioral correctness of the imagery task performance were measured. After the registration, we used independent component analysis (ICA) on EEG data to localize visual- and motor-related EEG sources of activity shared by both motor imagery conditions. Significant differences were obtained in the visual cortex (the occipital ICs cluster) and the right motor-related area (right parietal ICs cluster). In comparison to right-handers who, regardless of the task, demonstrated the same pattern in the visual area, left-handers obtained higher power in the alpha waves in the VMI task and better performance in this condition. On the other hand, only the right-handed showed different patterns in the alpha waves in the right motor cortex during the KMI condition. The results indicate that left-handers imagine movement differently than right-handers, focusing on visual experience. This provides new empirical evidence on the influence of movement preferences on imagery processes and has possible future implications for research in the area of neurorehabilitation and motor imagery-based brain–computer interfaces (MI-BCIs).

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5189f431f27e43728b901abc37530244Test
http://europepmc.org/articles/PMC8222290Test

المؤلفون: Tianjun Liu, Deling Yang

المصدر: Scientific Reports
Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)

مصطلحات موضوعية: Computer science, Science, Movement, 0206 medical engineering, 02 engineering and technology, Electroencephalography, Convolutional neural network, Article, Motor imagery, 0202 electrical engineering, electronic engineering, information engineering, medicine, Humans, Representation (mathematics), Spatial analysis, Multidisciplinary, medicine.diagnostic_test, business.industry, Deep learning, Pattern recognition, Hand, 020601 biomedical engineering, Class (biology), Data set, ComputingMethodologies_PATTERNRECOGNITION, Neurology, Brain-Computer Interfaces, Medicine, 020201 artificial intelligence & image processing, Artificial intelligence, Neural Networks, Computer, business, Neuroscience

الوصف: Motor Imagery is a classical method of Brain Computer Interaction, in which electroencephalogram (EEG) signal features evoked by the imaginary body movements are recognized, and relevant information is extracted. Recently, various deep learning methods are being focused on finding an easy-to-use EEG representation method that can preserve both temporal information as well as spatial information. To further utilize the spatial and temporal features of EEG signals, we proposed a 3D representation of EEG and an end-to-end EEG three-branch 3D convolutional neural network, to address the class imbalance problem (dataset show unequal distribution among their classes), we proposed a class balance cropped strategy. Experimental results indicated that there are also a problem of the different classification difficulty for different classes in motor stages classification tasks, we introduce focal loss to address problem of ‘easy-hard’ examples, when trained with the focal loss, the three-branch 3D-CNN network achieve good performance (relatively more balanced classification accuracy of binary classifications) on the WAY-EEG-GAL data set. Experimental results show that the proposed method is a good method, which can improve classification effect of different motor stages classification.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::44497086a53beb4e44155cf220b92722Test
http://europepmc.org/articles/PMC8144431Test

المؤلفون: Ursula Debarnot, Guillaume Legendre, Virginie Sterpenich, Aymeric Guillot, Sophie Schwartz, Chieko Huber, A.A. Perrault

المصدر: Scientific Reports
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Scientific Reports, Vol. 11, No 1 (2021) P. 8928

مصطلحات موضوعية: Adult, Male, medicine.medical_specialty, Imagery, Psychotherapy, medicine.medical_treatment, Science, Sleep spindle, Polysomnography, 050105 experimental psychology, Article, 03 medical and health sciences, Immobilization, 0302 clinical medicine, Physical medicine and rehabilitation, Motor imagery, Sensorimotor processing, Motor control, medicine, Humans, 0501 psychology and cognitive sciences, Beneficial effects, Multidisciplinary, Hand laterality, medicine.diagnostic_test, business.industry, 05 social sciences, Motor Cortex, Evoked Potentials, Motor, Sleep in non-human animals, Transcranial Magnetic Stimulation, ddc:616.8, ddc:128.37, Transcranial magnetic stimulation, Arm, Medicine, Female, business, 030217 neurology & neurosurgery

الوصف: Motor imagery (MI) is known to engage motor networks and is increasingly used as a relevant strategy in functional rehabilitation following immobilization, whereas its effects when applied during immobilization remain underexplored. Here, we hypothesized that MI practice during 11 h of arm-immobilization prevents immobilization-related changes at the sensorimotor and cortical representations of hand, as well as on sleep features. Fourteen participants were tested after a normal day (without immobilization), followed by two 11-h periods of immobilization, either with concomitant MI treatment or control tasks, one week apart. At the end of each condition, participants were tested on a hand laterality judgment task, then underwent transcranial magnetic stimulation to measure cortical excitability of the primary motor cortices (M1), followed by a night of sleep during which polysomnography data was recorded. We show that MI treatment applied during arm immobilization had beneficial effects on (1) the sensorimotor representation of hands, (2) the cortical excitability over M1 contralateral to arm-immobilization, and (3) sleep spindles over both M1s during the post-immobilization night. Furthermore, (4) the time spent in REM sleep was significantly longer, following the MI treatment. Altogether, these results support that implementing MI during immobilization may limit deleterious effects of limb disuse, at several levels of sensorimotor functioning.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::98b446b273bf318ab40cef510cfce3a4Test
http://europepmc.org/articles/PMC8076317Test

المؤلفون: Eun-Jeong Jeon, June Sic Kim, Chun Kee Chung, Yu Jin Yang

المصدر: Scientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
Scientific Reports

مصطلحات موضوعية: medicine.medical_specialty, Wilcoxon signed-rank test, Science, Sensory system, Electroencephalography, Article, 050105 experimental psychology, Visual motor, 03 medical and health sciences, Sensorimotor processing, 0302 clinical medicine, Motor imagery, Physical medicine and rehabilitation, medicine, 0501 psychology and cognitive sciences, Multidisciplinary, Proprioception, medicine.diagnostic_test, 05 social sciences, Kinesthetic learning, Visualization, Computational neuroscience, Medicine, Psychology, 030217 neurology & neurosurgery

الوصف: Motor imagery (MI) is the only way for disabled subjects to robustly use a robot arm with a brain-machine interface. There are two main types of MI. Kinesthetic motor imagery (KMI) is proprioceptive (OR somato-) sensory imagination and Visual motor imagery (VMI) represents a visualization of the corresponding movement incorporating the visual network. Because these imagery tactics may use different networks, we hypothesized that the connectivity measures could characterize the two imageries better than the local activity. Electroencephalography data were recorded. Subjects performed different conditions, including motor execution (ME), KMI, VMI, and visual observation (VO). We tried to classify the KMI and VMI by conventional power analysis and by the connectivity measures. The mean accuracies of the classification of the KMI and VMI were 98.5% and 99.29% by connectivity measures (alpha and beta, respectively), which were higher than those by the normalized power (p

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf8046b410ed8b4b9bfaf2cbed335d7fTest
https://doi.org/10.1038/s41598-021-82241-0Test

المؤلفون: Hanna-Leena Halme, Lauri Parkkonen

المساهمون: Department of Neuroscience and Biomedical Engineering, Aalto-yliopisto, Aalto University

المصدر: Scientific Reports, Vol 8, Iss 1, Pp 1-12 (2018)
Scientific Reports

مصطلحات موضوعية: Adult, Male, 030506 rehabilitation, Imagery, Psychotherapy, Computer science, Movement, Speech recognition, lcsh:Medicine, 02 engineering and technology, Electroencephalography, Article, 03 medical and health sciences, Passive movements, 0302 clinical medicine, Motor imagery, 0202 electrical engineering, electronic engineering, information engineering, medicine, Humans, General, lcsh:Science, Brain–computer interface, Multidisciplinary, medicine.diagnostic_test, business.industry, lcsh:R, 3112 Neurosciences, Magnetoencephalography, Pattern recognition, Neurofeedback, Neurophysiology, Hand, Publisher Correction, Brain-Computer Interfaces, Calibration, Female, 020201 artificial intelligence & image processing, lcsh:Q, Artificial intelligence, 0305 other medical science, business, Algorithms, 030217 neurology & neurosurgery, Decoding methods

الوصف: Long calibration time hinders the feasibility of brain-computer interfaces (BCI). If other subjects’ data were used for training the classifier, BCI-based neurofeedback practice could start without the initial calibration. Here, we compare methods for inter-subject decoding of left- vs. right-hand motor imagery (MI) from MEG and EEG.Six methods were tested on data involving MEG and EEG measurements of healthy participants. Only subjects with good within-subject accuracies were selected for inter-subject decoding. Three methods were based on the Common Spatial Patterns (CSP) algorithm, and three others on logistic regression with l1 - or l2,1 -norm regularization. The decoding accuracy was evaluated using 1) MI and 2) passive movements (PM) for training, separately for MEG and EEG.When the classifier was trained by MI, the best accuracies across subjects (mean 70.6% for MEG, 67.7% for EEG) were obtained using multi-task learning (MTL) with logistic regression and l2,1-norm regularization. MEG yielded slightly better average accuracies than EEG. When PM were used for training, none of the inter-subject methods yielded above chance level (58.7%) accuracy.In conclusion, MTL and training with other subject’s MI is efficient for inter-subject decoding of MI. Passive movements of other subjects are likely suboptimal for training the MI classifiers.

وصف الملف: application/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::61a8899394d6eab1f227d7bc3c4184a1Test
http://link.springer.com/article/10.1038/s41598-018-28295-zTest

المؤلفون: Paweł Augustynowicz, Marta Jaśkiewicz, Dariusz Zapała, Piotr Francuz, Marta Szewczyk, Emilia Zabielska-Mendyk, Andrzej Cudo, Natalia Kopiś

المصدر: Scientific Reports
Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)

مصطلحات موضوعية: Adult, Male, medicine.medical_specialty, Adolescent, Brain activity and meditation, lcsh:Medicine, Electroencephalography, Audiology, 050105 experimental psychology, Lateralization of brain function, Functional Laterality, Article, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Motor imagery, Feedback, Sensory, Human behaviour, medicine, Humans, 0501 psychology and cognitive sciences, Control (linguistics), lcsh:Science, Motor skill, Brain–computer interface, Multidisciplinary, medicine.diagnostic_test, 05 social sciences, lcsh:R, Brain, Brain-machine interface, Sensorimotor rhythm, Brain-Computer Interfaces, lcsh:Q, Female, Psychology, 030217 neurology & neurosurgery, Psychomotor Performance

الوصف: Brain–computer interfaces (BCIs) allow control of various applications or external devices solely by brain activity, e.g., measured by electroencephalography during motor imagery. Many users are unable to modulate their brain activity sufficiently in order to control a BCI. Most of the studies have been focusing on improving the accuracy of BCI control through advances in signal processing and BCI protocol modification. However, some research suggests that motor skills and physiological factors may affect BCI performance as well. Previous studies have indicated that there is differential lateralization of hand movements’ neural representation in right- and left-handed individuals. However, the effects of handedness on sensorimotor rhythm (SMR) distribution and BCI control have not been investigated in detail yet. Our study aims to fill this gap, by comparing the SMR patterns during motor imagery and real-feedback BCI control in right- (N = 20) and left-handers (N = 20). The results of our study show that the lateralization of SMR during a motor imagery task differs according to handedness. Left-handers present lower accuracy during BCI performance (single session) and weaker SMR suppression in the alpha band (8–13 Hz) during mental simulation of left-hand movements. Consequently, to improve BCI control, the user’s training should take into account individual differences in hand dominance.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4622ff63b67c0de87eab035e256cfbecTest
https://pubmed.ncbi.nlm.nih.gov/32034277Test

المؤلفون: Sheng Yuan, Zhaohui Wu, Shaomin Zhang, Xiaoxiang Zheng, Kedi Xu, Lipeng Huang, Gang Pan

المصدر: Scientific Reports, Vol 9, Iss 1, Pp 1-12 (2019)
Scientific Reports

مصطلحات موضوعية: 0301 basic medicine, Computer science, Interface (computing), lcsh:Medicine, Electroencephalography, Cognitive neuroscience, Article, 03 medical and health sciences, 0302 clinical medicine, Motor imagery, medicine, Animals, Humans, Longitudinal Studies, lcsh:Science, Brain–computer interface, Multidisciplinary, medicine.diagnostic_test, lcsh:R, Brain, Motor control, Human brain, Mind control, Electric Stimulation, Rats, 030104 developmental biology, medicine.anatomical_structure, Brain-Computer Interfaces, Brain stimulation, lcsh:Q, Microelectrodes, Neuroscience, Locomotion, 030217 neurology & neurosurgery

الوصف: Brain-machine interfaces (BMIs) provide a promising information channel between the biological brain and external devices and are applied in building brain-to-device control. Prior studies have explored the feasibility of establishing a brain-brain interface (BBI) across various brains via the combination of BMIs. However, using BBI to realize the efficient multidegree control of a living creature, such as a rat, to complete a navigation task in a complex environment has yet to be shown. In this study, we developed a BBI from the human brain to a rat implanted with microelectrodes (i.e., rat cyborg), which integrated electroencephalogram-based motor imagery and brain stimulation to realize human mind control of the rat’s continuous locomotion. Control instructions were transferred from continuous motor imagery decoding results with the proposed control models and were wirelessly sent to the rat cyborg through brain micro-electrical stimulation. The results showed that rat cyborgs could be smoothly and successfully navigated by the human mind to complete a navigation task in a complex maze. Our experiments indicated that the cooperation through transmitting multidimensional information between two brains by computer-assisted BBI is promising.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bcf0db4e16820efe4235a77cddd1e317Test
https://doi.org/10.1038/s41598-018-36885-0Test