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1.
Medicine (Baltimore) ; 100(3): e24351, 2021 Jan 22.
Artigo em Inglês | MEDLINE | ID: mdl-33546067

RESUMO

PURPOSE: Although several types of occupational therapy for motor recovery of the upper limb in patients with chronic stroke have been investigated, most treatments are performed in a hospital or clinic setting. We investigated the effect of graded motor imagery (GMI) training, as a home exercise program, on upper limb motor recovery and activities of daily living (ADL) in patients with stroke. METHODS: This prospective randomized controlled trial recruited 42 subjects with chronic stroke. The intervention group received instruction regarding the GMI program and performed it at home over 8 weeks (30 minutes a day). The primary outcome measure was the change in motor function between baseline and 8 weeks, assessed the Manual Function Test (MFT) and Fugl-Meyer Assessment (FMA). The secondary outcome measure was the change in ADL, assessed with the Modified Barthel Index (MBI). RESULTS: Of the 42 subjects, 37 completed the 8-week program (17 in the GMI group and 20 controls). All subjects showed significant improvements in the MFT, FMA, and MBI over time (P < .05). However, the improvements in the total scores for the MFT, FMA, and MBI did not differ between the GMI and control groups. The MFT arm motion score for the GMI group was significantly better than that of the controls (P < .05). CONCLUSIONS: The GMI program may be useful for improving upper extremity function as an adjunct to conventional rehabilitation for patients with chronic stroke.


Assuntos
Terapia por Exercício/normas , Imagens, Psicoterapia/normas , Acidente Vascular Cerebral/complicações , Extremidade Superior/inervação , Adulto , Idoso , Distribuição de Qui-Quadrado , Terapia por Exercício/instrumentação , Terapia por Exercício/métodos , Feminino , Humanos , Imagens, Psicoterapia/métodos , Masculino , Pessoa de Meia-Idade , Estudos Prospectivos , Recuperação de Função Fisiológica , Estatísticas não Paramétricas , Acidente Vascular Cerebral/fisiopatologia , Extremidade Superior/fisiopatologia
2.
Complement Ther Clin Pract ; 42: 101303, 2021 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-33434758

RESUMO

BACKGROUND: This study aims to measure the effect of guided imagery and hand massage on self-rated wellbeing and pain for palliative care patients. METHODS: This study adopted a quasi-experimental one-group pre-test post-test design. The sample consisted of n = 20 adult palliative care patients who received one session of guided imagery and hand massage. Self-reported levels of wellbeing and pain were measured on a scale of 0-10 before and after the intervention. Results were analyzed using a one-tailed sign test in SPSS Software. RESULTS: The intervention elicited a statistically significant improvement in self-reported levels of wellbeing (p = .029) and pain (p = .001). Feedback from participants showed the intervention was helpful and relaxing. CONCLUSION: The intervention had an immediate positive effect on wellbeing and pain among palliative care patients. Considering the promising results of this pilot study, guided imagery and hand massage should be studied further in the palliative care setting.


Assuntos
Imagens, Psicoterapia , Cuidados Paliativos , Humanos , Massagem , Dor , Projetos Piloto , Resultado do Tratamento
4.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2865-2868, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018604

RESUMO

We propose a new approach that utilizes the dynamic state of cortical functional connectivity for the classification of task-based electroencephalographic (EEG) data. We introduce a novel feature extraction framework that locates functional networks in the cortex as they convene at different time intervals across different frequency bands. The framework starts by applying the wavelet transform to isolate, then augment, EEG frequency bands. Next, the time intervals of stationary functional states, within the augmented data, are identified using the source-informed segmentation algorithm. Functional networks are localized in the brain, during each segment, using a singular value decomposition-based approach. For feature selection, we propose a discriminative-associative algorithm, and use it to find the sub-networks showing the highest recurrence rate differences across the target tasks. The sequences of augmented functional networks are projected onto the identified sub-networks, for the final sequences of features. A dynamic recurrent neural network classifier is then used for classification. The proposed approach is applied to experimental EEG data to classify motor execution and motor imagery tasks. Our results show that an accuracy of 90% can be achieved within the first 500 msec of the cued task-planning phase.


Assuntos
Algoritmos , Eletroencefalografia , Imagens, Psicoterapia , Redes Neurais de Computação , Análise de Ondaletas
5.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2869-2872, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018605

RESUMO

The goal of this paper is to investigate whether motor imagery tasks, performed under pain-free versus pain conditions, can be discriminated from electroencephalography (EEG) recordings. Four motor imagery classes of right hand, left hand, foot, and tongue are considered. A functional connectivity-based feature extraction approach along with a long short-term memory (LSTM) classifier are employed for classifying pain-free versus under-pain classes. Moreover, classification is performed in different frequency bands to study the significance of each band in differentiating motor imagery data associated with pain-free and under-pain states. When considering all frequency bands, the average classification accuracy is in the range of 77:86-80:04%. Our frequency-specific analysis shows that the gamma band results in a notably higher accuracy than other bands, indicating the importance of this band in discriminating pain/no-pain conditions during the execution of motor imagery tasks. In contrast, functional connectivity graphs extracted from delta and theta bands do not seem to provide discriminatory information between pain-free and under-pain conditions. This is the first study demonstrating that motor imagery tasks executed under pain and without pain conditions can be discriminated from EEG recordings. Our findings can provide new insights for developing effective brain computer interface-based assistive technologies for patients who are in real need of them.


Assuntos
Interfaces Cérebro-Computador , Imaginação , Eletroencefalografia , Humanos , Imagens, Psicoterapia , Dor/diagnóstico
6.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2977-2980, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018631

RESUMO

A large amount of calibration data is typically needed to train an electroencephalogram (EEG)-based brain-computer interfaces (BCI) due to the non-stationary nature of EEG data. This paper proposes a novel weighted transfer learning algorithm using a dynamic time warping (DTW) based alignment method to alleviate this need by using data from other subjects. DTW-based alignment is first applied to reduce the temporal variations between a specific subject data and the transfer learning data from other subjects. Next, similarity is measured using Kullback Leibler divergence (KL) between the DTW aligned data and the specific subject data. The other subjects' data are then weighted based on their KL similarity to each trials of the specific subject data. This weighted data from other subjects are then used to train the BCI model of the specific subject. An experiment was performed on publicly available BCI Competition IV dataset 2a. The proposed algorithm yielded an average improvement of 9% compared to a subject-specific BCI model trained with 4 trials, and the results yielded an average improvement of 10% compared to naive transfer learning. Overall, the proposed DTW-aligned KL weighted transfer learning algorithm show promise to alleviate the need of large amount of calibration data by using only a short calibration data.


Assuntos
Interfaces Cérebro-Computador , Algoritmos , Eletroencefalografia , Humanos , Imagens, Psicoterapia , Aprendizado de Máquina
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2991-2994, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018634

RESUMO

Electroencephalogram (EEG) data during motor imagery tasks regarding small-scale physical dynamics such as finger motions have low discriminability because capturing the spatial difference of the motions is difficult. We assumed that more discriminative features can be captured if spatial filters maximize the independence of each class data. This study constructed spatial filters named multiclass common spatial pattern (CSP), which maximize an approximation of mutual in-formation of extracted components and class labels, and applied them to a five-class motor-imagery dataset containing finger motion tasks. By applying multiclass CSP, the classification accuracies were improved (Mean SD: 40.6 ± 10.1%) compared with classical CSP (21.8 ± 2.5%) and no spatial filtering case (38.7±10.0%). In addition, we visualized learned spatial filters to assess the trend of discriminative features of finger motions. For these results, it was clear that multiclass CSP captured task-specific spatial maps for each finger motion and outperformed multiclass motor-imagery classification performance about 2% even when the tasks are small-scale physical dynamics.


Assuntos
Interfaces Cérebro-Computador , Algoritmos , Eletroencefalografia , Dedos , Imagens, Psicoterapia
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3040-3045, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018646

RESUMO

The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for classification of electroencephalographic (EEG) recordings has been restricted by the lack of large datasets. Privacy concerns associated with EEG signals limit the possibility of constructing a large EEG-BCI dataset by the conglomeration of multiple small ones for jointly training machine learning models. Hence, in this paper, we propose a novel privacy-preserving DL architecture named federated transfer learning (FTL) for EEG classification that is based on the federated learning framework. Working with the single-trial covariance matrix, the proposed architecture extracts common discriminative information from multi-subject EEG data with the help of domain adaptation techniques. We evaluate the performance of the proposed architecture on the PhysioNet dataset for 2-class motor imagery classification. While avoiding the actual data sharing, our FTL approach achieves 2% higher classification accuracy in a subject-adaptive analysis. Also, in the absence of multi-subject data, our architecture provides 6% better accuracy compared to other state-of-the-art DL architectures.


Assuntos
Interfaces Cérebro-Computador , Eletroencefalografia , Imagens, Psicoterapia , Aprendizado de Máquina , Privacidade
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3062-3065, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018651

RESUMO

Electroencephalogram (EEG) based brain-computer interfaces (BCIs) enable communication by interpreting the user intent based on measured brain electrical activity. Such interpretation is usually performed by supervised classifiers constructed in training sessions. However, changes in cognitive states of the user, such as alertness and vigilance, during test sessions lead to variations in EEG patterns, causing classification performance decline in BCI systems. This research focuses on effects of alertness on the performance of motor imagery (MI) BCI as a common mental control paradigm. It proposes a new protocol to predict MI performance decline by alertness-related pre-trial spatio-spectral EEG features. The proposed protocol can be used for adapting the classifier or restoring alertness based on the cognitive state of the user during BCI applications.


Assuntos
Interfaces Cérebro-Computador , Imaginação , Atenção , Eletroencefalografia , Imagens, Psicoterapia
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3889-3892, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018850

RESUMO

Speech imagery based brain-computer interface (BCI) has the potential to assist patients with communication disorders to recover their speech communication abilities. Mandarin is a tonal language, and its tones play an important role in language perception and semantic understanding. This work studied the electroencephalogram (EEG) based classification of Mandarin tones based on speech imagery, and also compared the classification performance of speech imagery based BCIs at two test conditions with visual-only and combined audio-visual stimuli, respectively. Participants imagined 4 Mandarin tones at each condition. Common spatial patterns were applied to extract feature vectors, and support vector machine was used to classify different Mandarin tones from EEG data. Experimental results showed that the tonal articulation imagination task achieved a higher classification accuracy at the combined audio-visual condition (i.e., 80.1%) than at the visual-only condition (i.e., 67.7%). The results in this work supported that Mandarin tone information could be decoded from EEG data recorded in a speech imagery task, particularly under the combined audio-visual condition.


Assuntos
Interfaces Cérebro-Computador , Fala , Eletroencefalografia , Humanos , Imagens, Psicoterapia , Imaginação
11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4051-4054, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018888

RESUMO

The purpose of this study was to discriminate between left- and right-hand motor imagery tasks. We recorded the brain signals from two participants using a fNIRS system and compared different feature extraction (mean, peak, minimum, skewness and kurtosis) and classification techniques (linear (LDA) and quadratic discriminant analysis (QDA), support vector machine (SVM), logistic regression, K-nearest-neighbor (KNN), and neural networks with Levenberg-Marquardt (LMA), Bayesian Regularization (BRANN) and Scaled Conjugate Gradient (SCGA) training algorithms). The results showed poor classification accuracies (<; 58%) when skewness and kurtosis were used. When mean, peak, and minimum were used as features, QDA, SVM and KNN produced higher classification accuracies relative to LDA and logistic regression. Overall, BRANN led to the highest accuracies (>98%) when mean, peak and minimum were used as features.


Assuntos
Interfaces Cérebro-Computador , Teorema de Bayes , Análise Discriminante , Humanos , Imagens, Psicoterapia , Espectroscopia de Luz Próxima ao Infravermelho
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 124-127, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017946

RESUMO

In this paper the classification of motor imagery brain signals is addressed. The innovative idea is to use both temporal and spatial knowledge of the input data to increase the performance. Definitely, the electrode locations on the scalp is as important as the acquired temporal signals from every individual electrode. In order to incorporate this knowledge, a deep neural network is employed in this work. Both motor-imagery EEG and bi-modal EEG-fNIRS datasets were used for this purpose. The results are compared for different scenarios and using different methods. The achieved results are promising and imply that combining both temporal and spatial information of the brain signals could be really effective and increases the performance.


Assuntos
Interfaces Cérebro-Computador , Aprendizado Profundo , Eletroencefalografia , Imagens, Psicoterapia , Redes Neurais de Computação
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 192-195, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017962

RESUMO

The brain-computer interface (BCI) based on electroencephalography (EEG) converts the subject's intentions into control signals. For the BCI, the study of motor imagery has been widely used. In recent years, a classification method based on a convolutional neural network (CNNs) has been proposed. However, most of the existing methods use a single convolution scale on CNN, and another problem that affects the results is limited training data. To solve these problems, we propose a mixed-scale CNN architecture, and a data augmentation method is used to classify the EEG of motor imagery. After classifying the BCI competition IV dataset 2b, the average classification accuracy is 81.52%. Compared with the existing methods, our method has a better classification result. This method effectively solves the problems existing in the existing CNN-based motor imagery classification methods, and it improves the classification accuracy.


Assuntos
Interfaces Cérebro-Computador , Algoritmos , Eletroencefalografia , Imagens, Psicoterapia , Imaginação
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 196-199, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017963

RESUMO

We have uncovered serious flaws in handling EEG signals with a decreased rank in implementations of the common spatial patterns (CSP). The CSP algorithm assumes covariance matrices of the signal to have full rank. However, preprocessing techniques, such as artifact removal using independent component analysis, may decrease the rank of the signal, leading to potential errors in the CSP decomposition. We inspect what could go wrong when CSP implementations do not take this into consideration on a binary motor imagery classification task. We review CSP implementations in open-source toolboxes for EEG signal analysis (FieldTrip, BBCI Toolbox, BioSig, EEGLAB, BCILAB, and MNE). We show that unprotected implementations decreased mean classification accuracy by up to 32%, with spatial filters resulting in complex numbers, for which corresponding spatial patterns do not have a clear interpretation. We encourage researchers to check their implementations and analysis pipelines.


Assuntos
Interfaces Cérebro-Computador , Algoritmos , Eletroencefalografia , Imagens, Psicoterapia
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 498-501, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018036

RESUMO

The electroencephalogram (EEG) records a summed mixture of multiple sources of neural activity distributed throughout the brain. Source separation methods aim to un-mix the EEG in order to recover activity generated by the original sources. However, most current state-of-the-art source separation methods do not take into account the physical locations of sources of EEG activity.We present a new source separation method which uses an accurate model of the head to un-mix the EEG into individual sources based on their physical locations.We apply our method to an EEG dataset recorded during motor imagery and show that it is able to identify sources that are located in distinct physical regions of the brain. We compare our method to independent component analysis and show that our sources have higher spatial specificity and, furthermore, allow higher classification accuracies (a mean improvement in accuracy of 8.6% was achieved p =0.039).


Assuntos
Interfaces Cérebro-Computador , Imaginação , Algoritmos , Eletroencefalografia , Imagens, Psicoterapia
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 502-505, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018037

RESUMO

Electroencephalogram (EEG) signals are important to study the activities of human brains. The independent component analysis (ICA) algorithm is a practical blind source separation (BSS) technique that can separate EEG sources from artifacts effectively. However, most traditional ICA algorithms assume that the mixing process is instantaneous and off-line. In this paper, a novel framework based on the extension of the online recursive ICA algorithm (ORICA) is proposed to apply for motor imagery (MI) EEG recording. The contributions are as follows. Firstly, we show ORICA's adaptability to accurate and effective source separation used for artifact-contaminated MI EEG recording. Secondly, to identify EOG signals on the output of source separation, the topographic map is presented to distinguish the target signals. The experimental results show that the proposed framework is able to be applied to process MI EEG recording in real-time situations.


Assuntos
Algoritmos , Eletroencefalografia , Artefatos , Encéfalo , Humanos , Imagens, Psicoterapia
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 506-509, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018038

RESUMO

We use random matrix theory (RMT) to investigate the statistical properties of brain functional networks in lower limb motor imagery. Functional connectivity was calculated by Pearson correlation coefficient (PCC), mutual information (MTI) and phase locking value (PLV) extracted from EEG signals. We found that when the measured subjects imagined the movements of their lower limbs the spectral density as well as the level spacings displayed deviations from the random matrix prediction. In particular, a significant difference between the left and right foot imaginary movements was observed in the maximum eigenvalue from the PCC, which can provide a theoretical basis for further study on the classification of unilateral movement of lower limbs.


Assuntos
Eletroencefalografia , Imaginação , Encéfalo/diagnóstico por imagem , Humanos , Imagens, Psicoterapia , Movimento
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 519-522, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018041

RESUMO

Recently, there is an increasing recognition that sensory feedback is critical for proper motor control. With the help of BCI, people with motor disabilities can communicate with their environments or control things around them by using signals extracted directly from the brain. The widely used non-invasive EEG based BCI system require that the brain signals are first preprocessed, and then translated into significant features that could be converted into commands for external control. To determine the appropriate information from the acquired brain signals is a major challenge for a reliable classification accuracy due to high data dimensions. The feature selection approach is a feasible technique to solving this problem, however, an effective selection method for determining the best set of features that would yield a significant classification performance has not yet been established for motor imagery (MI) based BCI. This paper explored the effectiveness of bio-inspired algorithms (BIA) such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), Cuckoo Search Algorithm (CSA), and Modified Particle Swarm Optimization (M-PSO) on EEG and ECoG data. The performance of SVM classifier showed that M-PSO is highly efficacious with the least selected feature (SF), and converges at an acceptable speed in low iterations.


Assuntos
Interfaces Cérebro-Computador , Algoritmos , Eletroencefalografia , Humanos , Imagens, Psicoterapia , Imaginação
19.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1600-1603, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018300

RESUMO

In this study we develop a proof of concept of using generative adversarial neural networks in hyperspectral skin cancer imagery production. Generative adversarial neural network is a neural network, where two neural networks compete. The generator tries to produce data that is similar to the measured data, and the discriminator tries to correctly classify the data as fake or real. This is a reinforcement learning model, where both models get reinforcement based on their performance. In the training of the discriminator we use data measured from skin cancer patients. The aim for the study is to develop a generator for augmenting hyperspectral skin cancer imagery.


Assuntos
Redes Neurais de Computação , Neoplasias Cutâneas , Humanos , Imagens, Psicoterapia , Aprendizagem , Aprendizado de Máquina
20.
Cochrane Database Syst Rev ; 9: CD013019, 2020 09 24.
Artigo em Inglês | MEDLINE | ID: mdl-32970328

RESUMO

BACKGROUND: Motor imagery (MI) is defined as a mentally rehearsed task in which movement is imagined but is not performed. The approach includes repetitive imagined body movements or rehearsing imagined acts to improve motor performance. OBJECTIVES: To assess the treatment effects of MI for enhancing ability to walk among people following stroke. SEARCH METHODS: We searched the Cochrane Stroke Group registry, CENTRAL, MEDLINE, Embase and seven other databases. We also searched trial registries and reference lists. The last searches were conducted on 24 February 2020. SELECTION CRITERIA: Randomized controlled trials (RCTs) using MI alone or associated with action observation or physical practice to improve gait in individuals after stroke. The critical outcome was the ability to walk, assessed using either a continuous variable (walking speed) or a dichotomous variable (dependence on personal assistance). Important outcomes included walking endurance, motor function, functional mobility, and adverse events. DATA COLLECTION AND ANALYSIS: Two review authors independently selected the trials according to pre-defined inclusion criteria, extracted the data, assessed the risk of bias, and applied the GRADE approach to evaluate the certainty of the evidence. The review authors contacted the study authors for clarification and missing data. MAIN RESULTS: We included 21 studies, involving a total of 762 participants. Participants were in the acute, subacute, or chronic stages of stroke, and had a mean age ranging from 50 to 78 years. All participants presented at least some gait deficit. All studies compared MI training versus other therapies. Most of the included studies used MI associated with physical practice in the experimental groups. The treatment time for the experimental groups ranged from two to eight weeks. There was a high risk of bias for at least one assessed domain in 20 of the 21 included studies. Regarding our critical outcome, there was very low-certainty evidence that MI was more beneficial for improving gait (walking speed) compared to other therapies at the end of the treatment (pooled standardized mean difference (SMD) 0.44; 95% confidence interval (CI) 0.06 to 0.81; P = 0.02; six studies; 191 participants; I² = 38%). We did not include the outcome of dependence on personal assistance in the meta-analysis, because only one study provided information regarding the number of participants that became dependent or independent after interventions. For our important outcomes, there was very low-certainty evidence that MI was no more beneficial than other interventions for improving motor function (pooled mean difference (MD) 2.24, 95% CI -1.20 to 5.69; P = 0.20; three studies; 130 participants; I² = 87%) and functional mobility at the end of the treatment (pooled SMD 0.55, 95% CI -0.45 to 1.56; P = 0.09; four studies; 116 participants; I² = 64.2%). No adverse events were observed in those studies that reported this outcome (seven studies). We were unable to pool data regarding walking endurance and all other outcomes at follow-up. AUTHORS' CONCLUSIONS: We found very low-certainty evidence regarding the short-term benefits of MI on walking speed in individuals who have had a stroke, compared to other therapies. Evidence was insufficient to estimate the effect of MI on the dependence on personal assistance and walking endurance. Compared with other therapies, the evidence indicates that MI does not improve motor function and functional mobility after stroke (very low-certainty evidence). Evidence was also insufficient to estimate the effect of MI on gait, motor function, and functional mobility after stroke compared to placebo or no intervention. Motor Imagery and other therapies used for gait rehabilitation after stroke do not appear to cause significant adverse events.


Assuntos
Transtornos Neurológicos da Marcha/reabilitação , Imagens, Psicoterapia/métodos , Reabilitação do Acidente Vascular Cerebral/métodos , Acidente Vascular Cerebral/complicações , Idoso , Viés , Feminino , Transtornos Neurológicos da Marcha/etiologia , Humanos , Masculino , Pessoa de Meia-Idade , Ensaios Clínicos Controlados Aleatórios como Assunto , Velocidade de Caminhada
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