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1.
Med Lav ; 111(5): 411-412, 2020 Oct 31.
Artigo em Inglês | MEDLINE | ID: mdl-33124613

RESUMO

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.


Assuntos
Asbestose , Asbestose/diagnóstico por imagem , Autopsia , Humanos , Imaginação , Radiografia
2.
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
3.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 514-518, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018040

RESUMO

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.


Assuntos
Interfaces Cérebro-Computador , Teorema de Bayes , Eletroencefalografia , Humanos , Imaginação , Processamento de Sinais Assistido por Computador
4.
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
5.
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
6.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 442-446, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018023

RESUMO

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.


Assuntos
Interfaces Cérebro-Computador , Algoritmos , Eletroencefalografia , Humanos , Imaginação , Movimento , Redes Neurais de Computação
7.
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
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 510-513, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018039

RESUMO

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.


Assuntos
Interfaces Cérebro-Computador , Imaginação , Algoritmos , Eletroencefalografia , Redes Neurais de Computação
9.
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
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3058-3061, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018650

RESUMO

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.


Assuntos
Interfaces Cérebro-Computador , Reabilitação Neurológica , Doença de Parkinson , Eletroencefalografia , Humanos , Imaginação
11.
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
12.
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
13.
Nat Commun ; 11(1): 4853, 2020 09 25.
Artigo em Inglês | MEDLINE | ID: mdl-32978377

RESUMO

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.


Assuntos
Mãos , Hipnose/métodos , Ilusões/fisiologia , Manejo da Dor/métodos , Sinestesia/terapia , Adolescente , Adulto , Feminino , Humanos , Imaginação , Masculino , Pessoa de Meia-Idade , Dor , Sugestão , Adulto Jovem
14.
PLoS One ; 15(8): e0237340, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32776948

RESUMO

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.


Assuntos
Envelhecimento/fisiologia , Imaginação/fisiologia , Memória Episódica , Rememoração Mental/fisiologia , Adolescente , Adulto , Fatores Etários , Idoso , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Ensaios Clínicos Controlados não Aleatórios como Assunto , Adulto Jovem
17.
Soins Psychiatr ; 41(327): 12-15, 2020.
Artigo em Francês | MEDLINE | ID: mdl-32718454

RESUMO

Jean-Pierre Klein, honorary hospital psychiatrist and director of the French national institute of expression, creation, art and therapy, describes his discovery of art therapy or how the diversion of the imagination, through contact with the material, enables one to approach and symbolise one's problems without always being able to understand what the symbols evoke. Accompanied symbolisation in art therapy can indeed be sufficient. The evolution, from creation to creation, forms a transformation process without there necessarily being any interpretative deciphering. Interview with a pioneer of art therapy in France.


Assuntos
Terapia pela Arte , França , Humanos , Imaginação
18.
Neurology ; 95(4): e417-e426, 2020 07 28.
Artigo em Inglês | MEDLINE | ID: mdl-32675074

RESUMO

OBJECTIVE: To determine whether training with a brain-computer interface (BCI) to control an image of a phantom hand, which moves based on cortical currents estimated from magnetoencephalographic signals, reduces phantom limb pain. METHODS: Twelve patients with chronic phantom limb pain of the upper limb due to amputation or brachial plexus root avulsion participated in a randomized single-blinded crossover trial. Patients were trained to move the virtual hand image controlled by the BCI with a real decoder, which was constructed to classify intact hand movements from motor cortical currents, by moving their phantom hands for 3 days ("real training"). Pain was evaluated using a visual analogue scale (VAS) before and after training, and at follow-up for an additional 16 days. As a control, patients engaged in the training with the same hand image controlled by randomly changing values ("random training"). The 2 trainings were randomly assigned to the patients. This trial is registered at UMIN-CTR (UMIN000013608). RESULTS: VAS at day 4 was significantly reduced from the baseline after real training (mean [SD], 45.3 [24.2]-30.9 [20.6], 1/100 mm; p = 0.009 < 0.025), but not after random training (p = 0.047 > 0.025). Compared to VAS at day 1, VAS at days 4 and 8 was significantly reduced by 32% and 36%, respectively, after real training and was significantly lower than VAS after random training (p < 0.01). CONCLUSION: Three-day training to move the hand images controlled by BCI significantly reduced pain for 1 week. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that BCI reduces phantom limb pain.


Assuntos
Interfaces Cérebro-Computador , Imaginação/fisiologia , Córtex Motor/fisiopatologia , Membro Fantasma/reabilitação , Robótica , Adulto , Idoso , Estudos Cross-Over , Mãos , Humanos , Magnetoencefalografia , Masculino , Pessoa de Meia-Idade , Movimento , Membro Fantasma/fisiopatologia
19.
Proc Natl Acad Sci U S A ; 117(32): 19101-19107, 2020 08 11.
Artigo em Inglês | MEDLINE | ID: mdl-32719117

RESUMO

This research presents a nudge-based approach to promoting honest behavior. Specifically, we introduce the moral barrier hypothesis, which posits that moral violations can be inhibited by the introduction of spatial boundaries, including ones that do not physically impede the act of transgressing. We found that, as compared to a no barrier condition, children cheated significantly less often when a barrier was strategically placed to divide the space where children were seated from a place that was associated with cheating. This effect was seen both when the barrier took a physical form and when it was purely symbolic. However, the mere presence of a barrier did not reduce cheating: if it failed to separate children from a space that was associated with cheating, children cheated as much as when there was no barrier at all. Taken together, these findings support the moral barrier hypothesis and show that even seemingly unremarkable features of children's environments can nudge them to act honestly.


Assuntos
Decepção , Imaginação , Criança , Pré-Escolar , Feminino , Humanos , Masculino , Princípios Morais , Personalidade
20.
Fam Process ; 59(3): 912-921, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32663322

RESUMO

Following the format put forth by Imber-Black and Roberts, I examine daily rituals, family traditions, holidays, and life cycle rituals during the pandemic of COVID-19. Marked by symbols capable of carrying multiple meanings, symbolic actions, special time and special place, and newly invented and adapted rituals are illustrated through stories of couples, families, and communities.


Assuntos
Comportamento Ritualístico , Infecções por Coronavirus/psicologia , Família/psicologia , Pneumonia Viral/psicologia , Quarentena/psicologia , Espiritualidade , Betacoronavirus , Infecções por Coronavirus/prevenção & controle , Feminino , Humanos , Imaginação , Masculino , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle
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