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1.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 36(6): 911-915, 2019 Dec 25.
Artigo em Chinês | MEDLINE | ID: mdl-31875363

RESUMO

This paper aims to realize the decoding of single trial motor imagery electroencephalogram (EEG) signal by extracting and classifying the optimized features of EEG signal. In the classification and recognition of multi-channel EEG signals, there is often a lack of effective feature selection strategies in the selection of the data of each channel and the dimension of spatial filters. In view of this problem, a method combining sparse idea and greedy search (GS) was proposed to improve the feature extraction of common spatial pattern (CSP). The improved common spatial pattern could effectively overcome the problem of repeated selection of feature patterns in the feature vector space extracted by the traditional method, and make the extracted features have more obvious characteristic differences. Then the extracted features were classified by Fisher linear discriminant analysis (FLDA). The experimental results showed that the classification accuracy obtained by proposed method was 19% higher on average than that of traditional common spatial pattern. And high classification accuracy could be obtained by selecting feature set with small size. The research results obtained in the feature extraction of EEG signals lay the foundation for the realization of motor imagery EEG decoding.


Assuntos
Interfaces Cérebro-Computador , Eletroencefalografia , Algoritmos , Análise Discriminante , Imaginação , Processamento de Sinais Assistido por Computador
3.
Adv Exp Med Biol ; 1101: 41-65, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31729671

RESUMO

Brain-computer interfaces (BCIs) provide a direct communication channel between human brain and output devices. Due to advantages such as non-invasiveness, ease of use, and low cost, electroencephalography (EEG) is the most popular method for current BCIs. This chapter gives an overview of the current EEG-based BCIs for the main purpose of communication and control. This chapter first provides a taxonomy of the EEG-based BCI systems by categorizing them into three major groups: (1) BCIs based on event-related potentials (ERPs), (2) BCIs based on sensorimotor rhythms, and (3) hybrid BCIs. Next, this chapter describes challenges and potential solutions in developing practical BCI systems toward high communication speed, convenient system use, and low user variation. Then this chapter briefly reviews both medical and non-medical applications of current BCIs. Finally, this chapter concludes with a summary of current stage and future perspectives of the EEG-based BCI technology.


Assuntos
Interfaces Cérebro-Computador , Eletroencefalografia , Comunicação , Potenciais Evocados , Humanos
4.
Adv Exp Med Biol ; 1101: 67-89, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31729672

RESUMO

Because of high spatial-temporal resolution of neural signals obtained by invasive recording, the invasive brain-machine interfaces (BMI) have achieved great progress in the past two decades. With success in animal research, BMI technology is transferring to clinical trials for helping paralyzed people to restore their lost motor functions. This chapter gives a brief review of BMI development from animal experiments to human clinical studies in the following aspects: (1) BMIs based on rodent animals; (2) BMI based on non-human primates; and (3) pilot BMIs studies in clinical trials. In the end, the chapter concludes with a summary of potential opportunities and future challenges in BMI technology.


Assuntos
Interfaces Cérebro-Computador , Animais , Interfaces Cérebro-Computador/normas , Interfaces Cérebro-Computador/tendências , Ensaios Clínicos como Assunto , Humanos
5.
Adv Exp Med Biol ; 1101: 123-147, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31729674

RESUMO

Brain-machine interface (BMI) provides a bidirectional pathway between the brain and external facilities. The machine-to-brain pathway makes it possible to send artificial information back into the biological brain, interfering neural activities and generating sensations. The idea of the BMI-assisted bio-robotic animal system is accomplished by stimulations on specific sites of the nervous system. With the technology of BMI, animals' locomotion behavior can be precisely controlled as robots, which made the animal turning into bio-robot. In this chapter, we reviewed our lab works focused on rat-robot navigation. The principles of rat-robot system have been briefly described first, including the target brain sites chosen for locomotion control and the design of remote control system. Some methodological advances made by optogenetic technologies for better modulation control have then been introduced. Besides, we also introduced our implementation of "mind-controlled" rat navigation system. Moreover, we have presented our efforts made on combining biological intelligence with artificial intelligence, with developments of automatic control and training system assisted with images or voices inputs. We concluded this chapter by discussing further developments to acquire environmental information as well as promising applications with write-in BMIs.


Assuntos
Controle Comportamental , Interfaces Cérebro-Computador , Robótica , Animais , Encéfalo/fisiologia , Locomoção , Ratos
6.
Adv Exp Med Biol ; 1101: 225-241, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31729678

RESUMO

The technological ability to capture electrophysiological activity of populations of cortical neurons through chronic implantable devices has led to significant advancements in the field of brain-computer interfaces. Recent progress in the field has been driven by developments in integrated microelectronics, wireless communications, materials science, and computational neuroscience. Here, we review major device development landmarks in the arena of neural interfaces from FDA-approved clinical systems to prototype head-mounted and fully implantable wireless systems for multi-channel neural recording. Additionally, we provide an outlook toward next-generation, highly miniaturized technologies for minimally invasive, vastly parallel neural interfaces for naturalistic, closed-loop neuroprostheses.


Assuntos
Interfaces Cérebro-Computador , Próteses e Implantes , Interfaces Cérebro-Computador/tendências , Desenho de Equipamento/tendências , Humanos , Neurônios , Neurociências , Próteses e Implantes/tendências
7.
Nat Commun ; 10(1): 4699, 2019 10 16.
Artigo em Inglês | MEDLINE | ID: mdl-31619680

RESUMO

Regaining the function of an impaired limb is highly desirable in paralyzed individuals. One possible avenue to achieve this goal is to bridge the interrupted pathway between preserved neural structures and muscles using a brain-computer interface. Here, we demonstrate that monkeys with subcortical stroke were able to learn to use an artificial cortico-muscular connection (ACMC), which transforms cortical activity into electrical stimulation to the hand muscles, to regain volitional control of a paralysed hand. The ACMC induced an adaptive change of cortical activities throughout an extensive cortical area. In a targeted manner, modulating high-gamma activity became localized around an arbitrarily-selected cortical site controlling stimulation to the muscles. This adaptive change could be reset and localized rapidly to a new cortical site. Thus, the ACMC imparts new function for muscle control to connected cortical sites and triggers cortical adaptation to regain impaired motor function after stroke.


Assuntos
Adaptação Fisiológica/fisiologia , Interfaces Cérebro-Computador , Estimulação Elétrica , Córtex Motor/fisiopatologia , Músculo Esquelético/fisiologia , Córtex Somatossensorial/fisiopatologia , Acidente Vascular Cerebral/fisiopatologia , Animais , Braço , Córtex Cerebral/fisiologia , Córtex Cerebral/fisiopatologia , Eletrocorticografia , Mãos , Macaca mulatta , Córtex Motor/fisiologia , Vias Neurais/fisiopatologia , Paralisia , Córtex Somatossensorial/fisiologia , Reabilitação do Acidente Vascular Cerebral , Punho
8.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 36(5): 856-861, 2019 Oct 25.
Artigo em Chinês | MEDLINE | ID: mdl-31631636

RESUMO

Brain-computer interface (BCI) provides a direct communicating and controlling approach between the brain and surrounding environment, which attracts a wide range of interest in the fields of brain science and artificial intelligence. It is a core to decode the electroencephalogram (EEG) feature in the BCI system. The decoding efficiency highly depends on the feature extraction and feature classification algorithms. In this paper, we first introduce the commonly-used EEG features in the BCI system. Then we introduce the basic classical algorithms and their advanced versions used in the BCI system. Finally, we present some new BCI algorithms proposed in recent years. We hope this paper can spark fresh thinking for the research and development of high-performance BCI system.


Assuntos
Algoritmos , Interfaces Cérebro-Computador , Eletroencefalografia , Reconhecimento Automatizado de Padrão , Encéfalo/fisiologia , Humanos
9.
Nat Neurosci ; 22(10): 1554-1564, 2019 10.
Artigo em Inglês | MEDLINE | ID: mdl-31551595

RESUMO

Brain-machine interfaces (BMIs) create closed-loop control systems that interact with the brain by recording and modulating neural activity and aim to restore lost function, most commonly motor function in paralyzed patients. Moreover, by precisely manipulating the elements within the control loop, motor BMIs have emerged as new scientific tools for investigating the neural mechanisms underlying control and learning. Beyond motor BMIs, recent work highlights the opportunity to develop closed-loop mood BMIs for restoring lost emotional function in neuropsychiatric disorders and for probing the neural mechanisms of emotion regulation. Here we review significant advances toward functional restoration and scientific discovery in motor BMIs that have been guided by a closed-loop control view. By focusing on this unifying view of BMIs and reviewing recent work, we then provide a perspective on how BMIs could extend to the neuropsychiatric domain.


Assuntos
Afeto/fisiologia , Interfaces Cérebro-Computador , Movimento/fisiologia , Animais , Humanos , Aprendizagem/fisiologia , Transtornos Mentais/fisiopatologia , Transtornos Mentais/psicologia , Transtornos Mentais/terapia
10.
Sensors (Basel) ; 19(17)2019 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-31480570

RESUMO

Human inhibitory control refers to the suppression of behavioral response in real environments, such as when driving a car or riding a motorcycle, playing a game and operating a machine. The P300 wave is a neural marker of human inhibitory control, and it can be used to recognize the symptoms of attention deficit hyperactivity disorder (ADHD) in human. In addition, the P300 neural marker can be considered as a stop command in the brain-computer interface (BCI) technologies. Therefore, the present study of electroencephalography (EEG) recognizes the mindset of human inhibition by observing the brain dynamics, like P300 wave in the frontal lobe, supplementary motor area, and in the right temporoparietal junction of the brain, all of them have been associated with response inhibition. Our work developed a hierarchical classification model to identify the neural activities of human inhibition. To accomplish this goal phase-locking value (PLV) method was used to select coupled brain regions related to inhibition because this method has demonstrated the best performance of the classification system. The PLVs were used with pattern recognition algorithms to classify a successful-stop versus a failed-stop in left-and right-hand inhibitions. The results demonstrate that quadratic discriminant analysis (QDA) yielded an average classification accuracy of 94.44%. These findings implicate the neural activities of human inhibition can be utilized as a stop command in BCI technologies, as well as to identify the symptoms of ADHD patients in clinical research.


Assuntos
Eletroencefalografia/métodos , Interfaces Cérebro-Computador , Potencial Evocado P300/fisiologia , Potenciais Evocados/fisiologia , Humanos
12.
Chaos ; 29(7): 073119, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31370406

RESUMO

The steady state motion visual evoked potential (SSMVEP)-based brain computer interface (BCI), which incorporates the motion perception capabilities of the human visual system to alleviate the negative effects caused by strong visual stimulation from steady-state VEP, has attracted a great deal of attention. In this paper, we design a SSMVEP-based experiment by Newton's ring paradigm. Then, we use the canonical correlation analysis and Support Vector Machines to classify SSMVEP signals for the SSMVEP-based electroencephalography (EEG) signal detection. We find that the classification accuracy of different subjects under fatigue state is much lower than that in the normal state. To probe into this, we develop a multiplex limited penetrable horizontal visibility graph method, which enables to infer a brain network from 62-channel EEG signals. Subsequently, we analyze the variation of the average weighted clustering coefficient and the weighted global efficiency corresponding to these two brain states and find that both network measures are lower under fatigue state. The results suggest that the associations and information transfer efficiency among different brain regions become weaker when the brain state changes from normal to fatigue, which provide new insights into the explanations for the reduced classification accuracy. The promising classification results and the findings render the proposed methods particularly useful for analyzing EEG recordings from SSMVEP-based BCI system.


Assuntos
Interfaces Cérebro-Computador , Encéfalo/fisiopatologia , Eletroencefalografia , Potenciais Evocados Visuais , Modelos Neurológicos , Estimulação Luminosa , Máquina de Vetores de Suporte , Humanos
13.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 36(4): 531-540, 2019 Aug 25.
Artigo em Chinês | MEDLINE | ID: mdl-31441252

RESUMO

Individual differences of P300 potentials lead to that a large amount of training data must be collected to construct pattern recognition models in P300-based brain-computer interface system, which may cause subjects' fatigue and degrade the system performance. TrAdaBoost is a method that transfers the knowledge from source area to target area, which improves learning effect in the target area. Our research purposed a TrAdaBoost-based linear discriminant analysis and a TrAdaBoost-based support vector machine to recognize the P300 potentials across multiple subjects. This method first trains two kinds of classifiers separately by using the data deriving from a small amount of data from same subject and a large amount of data from different subjects. Then it combines all the classifiers with different weights. Compared with traditional training methods that use only a small amount of data from same subject or mixed different subjects' data to directly train, our algorithm improved the accuracies by 19.56% and 22.25% respectively, and improved the information transfer rate of 14.69 bits/min and 15.76 bits/min respectively. The results indicate that the TrAdaBoost-based method has the potential to enhance the generalization ability of brain-computer interface on the individual differences.


Assuntos
Interfaces Cérebro-Computador , Potencial Evocado P300 , Máquina de Vetores de Suporte , Algoritmos , Análise Discriminante , Eletroencefalografia , Humanos
15.
IEEE Int Conf Rehabil Robot ; 2019: 689-693, 2019 06.
Artigo em Inglês | MEDLINE | ID: mdl-31374711

RESUMO

For individuals with severe motor deficiencies, controlling external devices such as robotic arms or wheelchairs can be challenging, as many devices require some degree of motor control to be operated, e.g. when controlled using a joystick. A brain-computer interface (BCI) relies only on signals from the brain and may be used as a controller instead of muscles. Motor imagery (MI) has been used in many studies as a control signal for BCIs. However, MI may not be suitable for all control purposes, and several people cannot obtain BCI control with MI. In this study, the aim was to investigate the feasibility of decoding covert speech from single-trial EEG and compare and combine it with MI. In seven healthy subjects, EEG was recorded with twenty-five channels during six different actions: Speaking three words (both covert and overt speech), two arm movements (both motor imagery and execution), and one idle class. Temporal and spectral features were derived from the epochs and classified with a random forest classifier. The average classification accuracy was $67 \pm 9$ % and $75\pm 7$ % for covert and overt speech, respectively; this was 5-10 % lower than the movement classification. The performance of the combined movement-speech decoder was $61 \pm 9$ % and $67\pm 7$ % (covert and overt), but it is possible to have more classes available for control. The possibility of using covert speech for controlling a BCI was outlined; this is a step towards a multimodal BCI system for improved usability.


Assuntos
Interfaces Cérebro-Computador , Eletroencefalografia , Fala/fisiologia , Estudos de Viabilidade , Feminino , Humanos , Masculino , Atividade Motora/fisiologia , Movimento , Adulto Jovem
16.
IEEE Int Conf Rehabil Robot ; 2019: 1067-1072, 2019 06.
Artigo em Inglês | MEDLINE | ID: mdl-31374771

RESUMO

An assistive robotic manipulator (ARM) can provide independence and improve the quality of life for patients suffering from tetraplegia. However, to properly control such device to a satisfactory level without any motor functions requires a very high performing brain-computer interface (BCI). Steady-state visual evoked potentials (SSVEP) based BCI are among the best performing. Thus, this study investigates the design of a system for a full workspace control of a 7 degrees of freedom ARM. A SSVEP signal is elicited by observing a visual stimulus flickering at a specific frequency and phase. This study investigates the best combination of unique frequencies and phases to provide a 16-target BCI by testing three different systems off line. Furthermore, a fourth system is developed to investigate the impact of the stimulating monitor refresh rate. Experiments conducted on two subjects suggest that a 16-target BCI created by four unique frequencies and 16-unique phases provide the best performance. Subject 1 reaches a maximum estimated ITR of 235 bits/min while subject 2 reaches 140 bits/min. The findings suggest that the optimal SSVEP stimuli to generate 16 targets are a low number of frequencies and a high number of unique phases. Moreover, the findings do not suggest any need for considering the monitor refresh rate if stimuli are modulated using a sinusoidal signal sampled at the refresh rate.


Assuntos
Interfaces Cérebro-Computador , Potenciais Evocados Visuais/fisiologia , Robótica , Eletroencefalografia , Humanos , Qualidade de Vida
17.
IEEE Int Conf Rehabil Robot ; 2019: 1127-1132, 2019 06.
Artigo em Inglês | MEDLINE | ID: mdl-31374781

RESUMO

The use of robotic devices to provide active motor support and sensory feedback of ongoing motor intention, by means of a Brain Computer Interface (BCI), has received growing support by recent literature, with particular focus on neurorehabilitation therapies. At the same time, performance in the use of the BCI has become a more critical factor, since it directly influences congruency and consistency of the provided sensory feedback. As motor imagery is the mental simulation of a given movement without depending on residual function, training of patients in the use of motor imagery BCI can be extended beyond each rehabilitation session, and practiced by using simpler devices than rehabilitation robots available in the hospital. In this work, we investigated the use of haptic stimulation provided by vibrating electromagnetic motors to enhance BCI system training. The BCI is based on motor imagery of hand grasping and designed to operate a hand exoskeleton. We investigated whether haptic stimulation at fingerpads proves to be more effective than stimulation at wrist, already experimented in literature, due to the higher density of mechano-receptors. Our results did not show significant differences between the two body locations in BCI performance, yet a wider and more stable event-relateddesynchronization appeared for the finger-located stimulation. Future investigations will put in relation training with haptic feedback at fingerpads with BCI performance using the handexoskeleton, in grasping tasks that naturally involve haptic feedback at fingerpads.


Assuntos
Exoesqueleto Energizado , Mãos/fisiologia , Interfaces Cérebro-Computador , Retroalimentação Sensorial/fisiologia , Força da Mão/fisiologia , Humanos , Punho/fisiologia
18.
J Clin Neurosci ; 68: 13-19, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31375306

RESUMO

Implantable neurostimulation devices provide a direct therapeutic link to the nervous system and can be considered brain-computer interfaces (BCI). Under this definition, BCI are not simply science fiction, they are part of existing neurosurgical practice. Clinical BCI are standard of care for historically difficult to treat neurological disorders. These systems target the central and peripheral nervous system and include Vagus Nerve Stimulation, Responsive Neurostimulation, and Deep Brain Stimulation. Recent advances in clinical BCI have focused on creating "closed-loop" systems. These systems rely on biomarker feedback and promise individualized therapy with optimal stimulation delivery and minimal side effects. Success of clinical BCI has paralleled research efforts to create BCI that restore upper extremity motor and sensory function to patients. Efforts to develop closed loop motor/sensory BCI is linked to the successes of today's clinical BCI.


Assuntos
Interfaces Cérebro-Computador/tendências , Estimulação Encefálica Profunda/tendências , Doenças do Sistema Nervoso/terapia , Estimulação do Nervo Vago/tendências , Estimulação Encefálica Profunda/instrumentação , Estimulação Encefálica Profunda/métodos , Humanos , Estimulação do Nervo Vago/instrumentação , Estimulação do Nervo Vago/métodos
19.
Neural Netw ; 119: 1-9, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31376634

RESUMO

Effective frequency recognition algorithms are critical in steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). In this study, we present a hierarchical feature fusion framework which can be used to design high-performance frequency recognition methods. The proposed framework includes two primary techniques for fusing features: spatial dimension fusion (SD) and frequency dimension fusion (FD). Both SD and FD fusions are obtained using a weighted strategy with a nonlinear function. To assess our novel methods, we used the correlated component analysis (CORRCA) method to investigate the efficiency and effectiveness of the proposed framework. Experimental results were obtained from a benchmark dataset of thirty-five subjects and indicate that the extended CORRCA method used within the framework significantly outperforms the original CORCCA method. Accordingly, the proposed framework holds promise to enhance the performance of frequency recognition methods in SSVEP-based BCIs.


Assuntos
Interfaces Cérebro-Computador , Eletroencefalografia/métodos , Potenciais Evocados Visuais/fisiologia , Estimulação Luminosa/métodos , /fisiologia , Adulto , Algoritmos , Feminino , Humanos , Masculino , Distribuição Aleatória , Adulto Jovem
20.
Nat Commun ; 10(1): 3096, 2019 07 30.
Artigo em Inglês | MEDLINE | ID: mdl-31363096

RESUMO

Natural communication often occurs in dialogue, differentially engaging auditory and sensorimotor brain regions during listening and speaking. However, previous attempts to decode speech directly from the human brain typically consider listening or speaking tasks in isolation. Here, human participants listened to questions and responded aloud with answers while we used high-density electrocorticography (ECoG) recordings to detect when they heard or said an utterance and to then decode the utterance's identity. Because certain answers were only plausible responses to certain questions, we could dynamically update the prior probabilities of each answer using the decoded question likelihoods as context. We decode produced and perceived utterances with accuracy rates as high as 61% and 76%, respectively (chance is 7% and 20%). Contextual integration of decoded question likelihoods significantly improves answer decoding. These results demonstrate real-time decoding of speech in an interactive, conversational setting, which has important implications for patients who are unable to communicate.


Assuntos
Mapeamento Encefálico/métodos , Córtex Cerebral/fisiologia , Fala/fisiologia , Interfaces Cérebro-Computador , Eletrocorticografia/instrumentação , Eletrocorticografia/métodos , Eletrodos Implantados , Epilepsia/diagnóstico , Epilepsia/fisiopatologia , Feminino , Humanos , Fatores de Tempo
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