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1.
Public Health ; 192: 68-71, 2021 Mar.
Artículo en Inglés | MEDLINE | ID: mdl-33647787

RESUMEN

OBJECTIVES: The COVID-19 pandemic has caused unexpected disruption to the operation of many museums. However, the disruption also presents an opportunity for local museums to explore new modes of audience engagement that could also help to mitigate the negative health impact of COVID-19 through the imaginative use of technology. This article provides a snapshot of the various digital initiatives that were developed by museums in Singapore during the most challenging time of COVID-19 to exemplify the expanded role of museums as a public health resource. It will also offer a brief reflection on the challenges and benefits of curating wellbeing with digital technologies. STUDY DESIGN: A review of creative responses to COVID-19 by museums in Singapore. METHODS: Scoping search. RESULTS: Several local museums have stepped up efforts to support the wellbeing of people by exploring possibilities with digital virtual platforms. Their swift response to develop online contents following an abrupt closure due to the pandemic exemplifies the caring role of museums in offering people a much-needed respite from social isolation by connecting and interacting with others from a safe distance. Moving forward, it is also important for the museums to be mindful of the barriers that digital virtual platforms might present; since access to technology differs amongst population groups, as do digital competency, and literacy. Museums can benefit from further partnerships with sector experts and organisations to learn about the needs and challenges of different groups in future planning and design. This will help them to gather a holistic overview and help ensure inclusionary strategy and practice. CONCLUSIONS: COVID-19 has challenged museums to adapt their programme and keep the public engaged through virtual programmes on online spaces. Online initiatives have offered opportunities for people to remain socially active and meaningfully engaged despite the stringent measures imposed in response to the viral situation. Postpandemic, we can continue to anticipate a highly connected and inclusive society brought together by virtual technologies.


Asunto(s)
Terapia con Arte , Imaginación , Museos , Aislamiento Social/psicología , /epidemiología , Humanos , Salud Mental , Pandemias , Salud Pública , Singapur/epidemiología
2.
Nat Commun ; 12(1): 1120, 2021 02 18.
Artículo en Inglés | MEDLINE | ID: mdl-33602925

RESUMEN

The integration and interaction of vision, touch, hearing, smell, and taste in the human multisensory neural network facilitate high-level cognitive functionalities, such as crossmodal integration, recognition, and imagination for accurate evaluation and comprehensive understanding of the multimodal world. Here, we report a bioinspired multisensory neural network that integrates artificial optic, afferent, auditory, and simulated olfactory and gustatory sensory nerves. With distributed multiple sensors and biomimetic hierarchical architectures, our system can not only sense, process, and memorize multimodal information, but also fuse multisensory data at hardware and software level. Using crossmodal learning, the system is capable of crossmodally recognizing and imagining multimodal information, such as visualizing alphabet letters upon handwritten input, recognizing multimodal visual/smell/taste information or imagining a never-seen picture when hearing its description. Our multisensory neural network provides a promising approach towards robotic sensing and perception.


Asunto(s)
Biomimética , Redes Neurales de la Computación , Reconocimiento en Psicología , Humanos , Imaginación , Aprendizaje , Neuronas/fisiología , Olfato/fisiología , Gusto/fisiología , Tacto/fisiología , Visión Ocular/fisiología
4.
Neural Netw ; 133: 193-206, 2021 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-33220643

RESUMEN

Motor imagery (MI) brain-computer interface (BCI) and neurofeedback (NF) with electroencephalogram (EEG) signals are commonly used for motor function improvement in healthy subjects and to restore neurological functions in stroke patients. Generally, in order to decrease noisy and redundant information in unrelated EEG channels, channel selection methods are used which provide feasible BCI and NF implementations with better performances. Our assumption is that there are causal interactions between the channels of EEG signal in MI tasks that are repeated in different trials of a BCI and NF experiment. Therefore, a novel method for EEG channel selection is proposed which is based on Granger causality (GC) analysis. Additionally, the machine-learning approach is used to cluster independent component analysis (ICA) components of the EEG signal into artifact and normal EEG clusters. After channel selection, using the common spatial pattern (CSP) and regularized CSP (RCSP), features are extracted and with the k-nearest neighbor (k-NN), support vector machine (SVM) and linear discriminant analysis (LDA) classifiers, MI tasks are classified into left and right hand MI. The goal of this study is to achieve a method resulting in lower EEG channels with higher classification performance in MI-based BCI and NF by causal constraint. The proposed method based on GC, with only eight selected channels, results in 93.03% accuracy, 92.93% sensitivity, and 93.12% specificity, with RCSP feature extractor and best classifier for each subject, after being applied on Physionet MI dataset, which is increased by 3.95%, 3.73%, and 4.13%, in comparison with correlation-based channel selection method.


Asunto(s)
Interfaces Cerebro-Computador , Electroencefalografía/métodos , Imaginación/fisiología , Movimiento/fisiología , Neurorretroalimentación/métodos , Neurorretroalimentación/fisiología , Interfaces Cerebro-Computador/tendencias , Causalidad , Análisis Discriminante , Humanos , Máquina de Vectores de Soporte
5.
Sensors (Basel) ; 20(24)2020 Dec 19.
Artículo en Inglés | MEDLINE | ID: mdl-33352714

RESUMEN

This study aimed to develop an intuitive gait-related motor imagery (MI)-based hybrid brain-computer interface (BCI) controller for a lower-limb exoskeleton and investigate the feasibility of the controller under a practical scenario including stand-up, gait-forward, and sit-down. A filter bank common spatial pattern (FBCSP) and mutual information-based best individual feature (MIBIF) selection were used in the study to decode MI electroencephalogram (EEG) signals and extract a feature matrix as an input to the support vector machine (SVM) classifier. A successive eye-blink switch was sequentially combined with the EEG decoder in operating the lower-limb exoskeleton. Ten subjects demonstrated more than 80% accuracy in both offline (training) and online. All subjects successfully completed a gait task by wearing the lower-limb exoskeleton through the developed real-time BCI controller. The BCI controller achieved a time ratio of 1.45 compared with a manual smartwatch controller. The developed system can potentially be benefit people with neurological disorders who may have difficulties operating manual control.


Asunto(s)
Interfaces Cerebro-Computador , Electroencefalografía , Dispositivo Exoesqueleto , Humanos , Imaginación , Máquina de Vectores de Soporte
6.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 192-195, 2020 07.
Artículo en Inglés | MEDLINE | ID: mdl-33017962

RESUMEN

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.


Asunto(s)
Interfaces Cerebro-Computador , Algoritmos , Electroencefalografía , Imágenes en Psicoterapia , Imaginación
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 442-446, 2020 07.
Artículo en Inglés | MEDLINE | ID: mdl-33018023

RESUMEN

Motivated by the inconceivable capability of human brain in simultaneously processing multi-modal signals and its real-time feedback to the outer world events, there has been a surge of interest in establishing a communication bridge between the human brain and a computer, which are referred to as Brain-computer Interfaces (BCI). To this aim, monitoring the electrical activity of brain through Electroencephalogram (EEG) has emerged as the prime choice for BCI systems. To discover the underlying and specific features of brain signals for different mental tasks, a considerable number of research works are developed based on statistical and data-driven techniques. However, a major bottleneck in development of practical and commercial BCI systems is their limited performance when the number of mental tasks for classification is increased. In this work, we propose a new EEG processing and feature extraction paradigm based on Siamese neural networks, which can be conveniently merged and scaled up for multi-class problems. The idea of Siamese networks is to train a double-input neural network based on a contrastive loss-function, which provides the capability of verifying if two input EEG trials are from the same class or not. In this work, a Siamese architecture, which is developed based on Convolutional Neural Networks (CNN) and provides a binary output on the similarity of two inputs, is combined with One vs. Rest (OVR) and One vs. One (OVO) techniques to scale up for multi-class problems. The efficacy of this architecture is evaluated on a 4-class Motor Imagery (MI) dataset from BCI Competition IV2a and the results suggest a promising performance compared to its counterparts.


Asunto(s)
Interfaces Cerebro-Computador , Algoritmos , Electroencefalografía , Humanos , Imaginación , Movimiento , Redes Neurales de la Computación
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 498-501, 2020 07.
Artículo en Inglés | MEDLINE | ID: mdl-33018036

RESUMEN

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).


Asunto(s)
Interfaces Cerebro-Computador , Imaginación , Algoritmos , Electroencefalografía , Imágenes en Psicoterapia
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 506-509, 2020 07.
Artículo en Inglés | MEDLINE | ID: mdl-33018038

RESUMEN

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.


Asunto(s)
Electroencefalografía , Imaginación , Encéfalo/diagnóstico por imagen , Humanos , Imágenes en Psicoterapia , Movimiento
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 510-513, 2020 07.
Artículo en Inglés | MEDLINE | ID: mdl-33018039

RESUMEN

Electroencephalography (EEG) based Brain Computer Interface (BCI) attracts more and more attention. Motor Imagery (MI) is a popular one among all the EEG paradigms. Building a subject-independent MI EEG classification procedure is a main challenge in practical applications. Recently, Convolutional Neural Network (CNN) has been introduced and achieved state-of-the-art performance in related areas. To extract subject-independent features in MI EEG classification, we propose the MI3DNet, using a remapped signal cubic as the input. Experiments show that MI3DNet has a higher performance with fewer parameters and layers. We also give a method to plot the parameters of the dense layer, and explain its effect.


Asunto(s)
Interfaces Cerebro-Computador , Imaginación , Algoritmos , Electroencefalografía , Redes Neurales de la Computación
11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 514-518, 2020 07.
Artículo en Inglés | MEDLINE | ID: mdl-33018040

RESUMEN

Common spatial pattern (CSP) is a popular feature extraction method for electroencephalogram (EEG) motor imagery (MI). This study modifies the conventional CSP algorithm to improve the multi-class MI classification accuracy and ensure the computation process is efficient. The EEG MI data is gathered from the Brain-Computer Interface (BCI) Competition IV. At first, a bandpass filter and a timefrequency analysis are performed for each experiment trial. Then, the optimal EEG signals for every experiment trials are selected based on the signal energy for CSP feature extraction. In the end, the extracted features are classified by three classifiers, linear discriminant analysis (LDA), naïve Bayes (NVB), and support vector machine (SVM), in parallel for classification accuracy comparison.The experiment results show the proposed algorithm average computation time is 37.22% less than the FBCSP (1st winner in the BCI Competition IV) and 4.98% longer than the conventional CSP method. For the classification rate, the proposed algorithm kappa value achieved 2nd highest compared with the top 3 winners in BCI Competition IV.


Asunto(s)
Interfaces Cerebro-Computador , Teorema de Bayes , Electroencefalografía , Humanos , Imaginación , Procesamiento de Señales Asistido por Computador
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 519-522, 2020 07.
Artículo en Inglés | MEDLINE | ID: mdl-33018041

RESUMEN

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.


Asunto(s)
Interfaces Cerebro-Computador , Algoritmos , Electroencefalografía , Humanos , Imágenes en Psicoterapia , Imaginación
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2869-2872, 2020 07.
Artículo en Inglés | MEDLINE | ID: mdl-33018605

RESUMEN

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.


Asunto(s)
Interfaces Cerebro-Computador , Imaginación , Electroencefalografía , Humanos , Imágenes en Psicoterapia , Dolor/diagnóstico
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3058-3061, 2020 07.
Artículo en Inglés | MEDLINE | ID: mdl-33018650

RESUMEN

The study reports the performance of Parkinson's disease (PD) patients to operate Motor-Imagery based Brain-Computer Interface (MI-BCI) and compares three selected pre-processing and classification approaches. The experiment was conducted on 7 PD patients who performed a total of 14 MI-BCI sessions targeting lower extremities. EEG was recorded during the initial calibration phase of each session, and the specific BCI models were produced by using Spectrally weighted Common Spatial Patterns (SpecCSP), Source Power Comodulation (SPoC) and Filter-Bank Common Spatial Patterns (FBCSP) methods. The results showed that FBCSP outperformed SPoC in terms of accuracy, and both SPoC and SpecCSP in terms of the false-positive ratio. The study also demonstrates that PD patients were capable of operating MI-BCI, although with lower accuracy.


Asunto(s)
Interfaces Cerebro-Computador , Rehabilitación Neurológica , Enfermedad de Parkinson , Electroencefalografía , Humanos , Imaginación
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3062-3065, 2020 07.
Artículo en Inglés | MEDLINE | ID: mdl-33018651

RESUMEN

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.


Asunto(s)
Interfaces Cerebro-Computador , Imaginación , Atención , Electroencefalografía , Imágenes en Psicoterapia
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3889-3892, 2020 07.
Artículo en Inglés | MEDLINE | ID: mdl-33018850

RESUMEN

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.


Asunto(s)
Interfaces Cerebro-Computador , Habla , Electroencefalografía , Humanos , Imágenes en Psicoterapia , Imaginación
17.
BMC Neurol ; 20(1): 385, 2020 Oct 22.
Artículo en Inglés | MEDLINE | ID: mdl-33092554

RESUMEN

BACKGROUND: Training with brain-computer interface (BCI) technology in the rehabilitation of patients after a stroke is rapidly developing. Numerous RCT investigated the effects of BCI training (BCIT) on recovery of motor and brain function in patients after stroke. METHODS: A systematic literature search was performed in Medline, IEEE Xplore Digital Library, Cochrane library, and Embase in July 2018 and was repeated in March 2019. RCT or controlled clinical trials that included BCIT for improving motor and brain recovery in patients after a stroke were identified. Data were meta-analysed using the random-effects model. Standardized mean difference (SMD) with 95% confidence (95%CI) and 95% prediction interval (95%PI) were calculated. A meta-regression was performed to evaluate the effects of covariates on the pooled effect-size. RESULTS: In total, 14 studies, including 362 patients after ischemic and hemorrhagic stroke (cortical, subcortical, 121 females; mean age 53.0+/- 5.8; mean time since stroke onset 15.7+/- 18.2 months) were included. Main motor recovery outcome measure used was the Fugl-Meyer Assessment. Quantitative analysis showed that a BCI training compared to conventional therapy alone in patients after stroke was effective with an SMD of 0.39 (95%CI: 0.17 to 0.62; 95%PI of 0.13 to 0.66) for motor function recovery of the upper extremity. An SMD of 0.41 (95%CI: - 0.29 to 1.12) for motor function recovery of the lower extremity was found. BCI training enhanced brain function recovery with an SMD of 1.11 (95%CI: 0.64 to 1.59; 95%PI ranging from 0.33 to 1.89). Covariates such as training duration, impairment level of the upper extremity, and the combination of both did not show significant effects on the overall pooled estimate. CONCLUSION: This meta-analysis showed evidence that BCI training added to conventional therapy may enhance motor functioning of the upper extremity and brain function recovery in patients after a stroke. We recommend a standardised evaluation of motor imagery ability of included patients and the assessment of brain function recovery should consider neuropsychological aspects (attention, concentration). Further influencing factors on motor recovery due to BCI technology might consider factors such as age, lesion type and location, quality of performance of motor imagery, or neuropsychological aspects. TRIAL REGISTRATION: PROSPERO registration: CRD42018105832 .


Asunto(s)
Interfaces Cerebro-Computador , Imaginación , Recuperación de la Función , Rehabilitación de Accidente Cerebrovascular/métodos , Electroencefalografía , Femenino , Humanos , Persona de Mediana Edad , Accidente Cerebrovascular/fisiopatología
18.
Med Lav ; 111(5): 411-412, 2020 Oct 31.
Artículo en Inglés | MEDLINE | ID: mdl-33124613

RESUMEN

Two cases of asbestosis diagnosed on the basis of anamnestic, clinical, and instrumental criteria, were not confirmed by forensic autopsy ordered by the public prosecutor to ascertain the cause of death. The two cases demonstrate that a suggestive working history can be misleading, in the absence of clear radiological signs and histopathological findings, and that asbestosis must be diagnosed following the criteria consolidated in the scientific literature, as any diagnostic errors can have serious legal consequences.


Asunto(s)
Asbestosis , Asbestosis/diagnóstico por imagen , Autopsia , Humanos , Imaginación , Radiografía
19.
Nat Commun ; 11(1): 4853, 2020 09 25.
Artículo en Inglés | MEDLINE | ID: mdl-32978377

RESUMEN

In hypnotic responding, expectancies arising from imaginative suggestion drive striking experiential changes (e.g., hallucinations) - which are experienced as involuntary - according to a normally distributed and stable trait ability (hypnotisability). Such experiences can be triggered by implicit suggestion and occur outside the hypnotic context. In large sample studies (of 156, 404 and 353 participants), we report substantial relationships between hypnotisability and experimental measures of experiential change in mirror-sensory synaesthesia and the rubber hand illusion comparable to relationships between hypnotisability and individual hypnosis scale items. The control of phenomenology to meet expectancies arising from perceived task requirements can account for experiential change in psychological experiments.


Asunto(s)
Mano , Hipnosis/métodos , Ilusiones/fisiología , Manejo del Dolor/métodos , Sinestesia/terapia , Adolescente , Adulto , Femenino , Humanos , Imaginación , Masculino , Persona de Mediana Edad , Dolor , Sugestión , Adulto Joven
20.
PLoS One ; 15(8): e0237340, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32776948

RESUMEN

When voluntarily describing their past or future, older adults typically show a reduction in episodic specificity (e.g., including fewer details reflecting a specific event, time and/or place). However, aging has less impact on other types of tasks that place minimal demands on strategic retrieval such as spontaneous thoughts. In the current study, we investigated age-related differences in the episodic specificity of spontaneous thoughts using experimenter-based coding of thought descriptions. Additionally, we tested whether an episodic specificity induction, which increases episodic detail during deliberate retrieval of events in young and older adults, has the same effect under spontaneous retrieval. Twenty-four younger and 24 healthy older adults performed two counterbalanced sessions including a video, the episodic specificity or control induction, and a vigilance task. In the episodic specificity induction, participants recalled the details of the video while in the control they solved math exercises. The impact of this manipulation on the episodic specificity of spontaneous thoughts was assessed in the subsequent vigilance task, in which participants were randomly stopped to describe their thoughts and classify them as deliberate/spontaneous. We found no differences in episodic specificity between age groups in spontaneous thoughts, supporting the prediction that automatic retrieval attenuates the episodic specificity decrease in aging. The lack of age differences was present regardless of the induction, showing no interactions. For the induction, we also found no main effect, indicating that automatic retrieval bypasses event construction and accesses pre-stored events. Overall, our evidence suggests that spontaneous retrieval is a promising strategy to support episodic specificity in aging.


Asunto(s)
Envejecimiento/fisiología , Imaginación/fisiología , Memoria Episódica , Recuerdo Mental/fisiología , Adolescente , Adulto , Factores de Edad , Anciano , Femenino , Humanos , Masculino , Persona de Mediana Edad , Ensayos Clínicos Controlados no Aleatorios como Asunto , Adulto Joven
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