Sensory remapping: a possible therapy option / Remapeamento sensorial: uma possível opção de terapia

The human sensory receptors are morphologically specialized to transduce specific stimuli into the brain. However, when an injury occurs, mainly in the spinal cord, which can be of traumatic or non-traumatic origin, it provokes various degrees of sensory deficits, autonomic, motor and sphincter dysfunction below the level of the injury. Based on this, a new therapeutic modality is being proposed by neuroscientist Miguel Nicolelis, which is based on the brain-machine interface, that is, using other pathways so that the information can reach the cerebral cortex and thus be consciously processed (AU).

Os receptores sensoriais humanos são morfologicamente especializados para realizar a transdução de estímulos específicos para o encéfalo. Entretanto, quando ocorre uma lesão, principalmente, na medula espinal, que pode ser de origem traumática e não traumática, provocam diversos graus de déficits sensoriais, disfunção autônoma, motora e esfincteriana, abaixo do nível da lesão. Com base nisso, uma nova modalidade terapêutica está sendo proposto pelo neurocientista Miguel Nicolelis, que tem como base a interface cérebro máquina, isto é, utilizar-se de outras vias para que as informações possam chegar no córtex cerebral e assim serem processadas conscientemente.Palavras-chave: Interfaces cérebro-computador, Neurociências, Órgãos dos sentidos (AU).

Research progress in brain computer interface technology applied to motor recovery after spinal cord injury / 中华创伤杂志

Most patients with spinal cord injury suffer from limb motor dysfunction. Given drugs, surgery and other conventional treatments are often not effective, the patients can only rely on a wheelchair to move or even lie in bed for a long time, seriously affecting their quality of life. Brain computer interface (BCI) technology provides a non-muscular pathway for the recovery of motor function in patients with spinal cord injury, which allows the patients to recover partial motor function through the normal function of their own non-diseased spinal cord or external mechanical devices. After decades of development of BCI technology, signal collection devices can identify and collect the motor signals of the brain more accurately, transform the signal by characteristic analysis, and implement the brain command by using the output device. A large number of experimental and clinical studies have also proved that the application of BCI technology in patients with spinal cord injury can partially improve the motor function of upper and lower limbs. Therefore, BCI technology has attracted more and more attention. The authors summarized the BCI technology and its influence on motor function rehabilitation in patients with spinal cord injury, so as to provide a reference for the rehabilitation of motor function in patients with spinal cord injury.

The research status and development trends of brain-computer interfaces in medicine / 生物医学工程学杂志

Brain-computer interfaces (BCIs) have become one of the cutting-edge technologies in the world, and have been mainly applicated in medicine. In this article, we sorted out the development history and important scenarios of BCIs in medical application, analyzed the research progress, technology development, clinical transformation and product market through qualitative and quantitative analysis, and looked forward to the future trends. The results showed that the research hotspots included the processing and interpretation of electroencephalogram (EEG) signals, the development and application of machine learning algorithms, and the detection and treatment of neurological diseases. The technological key points included hardware development such as new electrodes, software development such as algorithms for EEG signal processing, and various medical applications such as rehabilitation and training in stroke patients. Currently, several invasive and non-invasive BCIs are in research. The R&D level of BCIs in China and the United State is leading the world, and have approved a number of non-invasive BCIs. In the future, BCIs will be applied to a wider range of medical fields. Related products will develop shift from a single mode to a combined mode. EEG signal acquisition devices will be miniaturized and wireless. The information flow and interaction between brain and machine will give birth to brain-machine fusion intelligence. Last but not least, the safety and ethical issues of BCIs will be taken seriously, and the relevant regulations and standards will be further improved.

Efectos negativos en la investigación y el quehacer médico en Chile de la Ley 20.584 y la Ley de Neuroderechos en discusión: la urgente necesidad de aprender de nuestros errores / Negative effects of the patients' rights law and neuro-rights bill in Chile

Recently, the Chilean Senate approved the main ideas of a constitutional reform and a Neuro-rights bill. This bill aims to protect people from the potential abusive use of "neuro-technologies". Unfortunately, a literal interpretation of this law can produce severe negative effects both in the development of neuroscience research and medical practice in Chile, interfering with current treatments in countless patients suffering from neuropsychiatric diseases. This fear stems from the observation of the negative effects that recent Chilean legislations have produced, which share with the Neuro-Rights Law the attempt to protect vulnerable populations from potential abuse from certain medical interventions. In fact, Law 20,584 promulgated in 2012, instead of protecting the most vulnerable patients "incapacitated to consent", produced enormous, and even possibly irreversible, damage to research in Chile in pathologies that require urgent attention, such as many neuropsychiatric diseases. This article details the effects that Law 20.584 had on research in Chile, how it relates to the Neuro-Rights Law, and the potential negative effects that the latter could have on research and medical practice, if it is not formulated correcting its errors.

Cross-subject electroencephalogram emotion recognition based on maximum classifier discrepancy / 生物医学工程学杂志

Affective brain-computer interfaces (aBCIs) has important application value in the field of human-computer interaction. Electroencephalogram (EEG) has been widely concerned in the field of emotion recognition due to its advantages in time resolution, reliability and accuracy. However, the non-stationary characteristics and individual differences of EEG limit the generalization of emotion recognition model in different time and different subjects. In this paper, in order to realize the recognition of emotional states across different subjects and sessions, we proposed a new domain adaptation method, the maximum classifier difference for domain adversarial neural networks (MCD_DA). By establishing a neural network emotion recognition model, the shallow feature extractor was used to resist the domain classifier and the emotion classifier, respectively, so that the feature extractor could produce domain invariant expression, and train the decision boundary of classifier learning task specificity while realizing approximate joint distribution adaptation. The experimental results showed that the average classification accuracy of this method was 88.33% compared with 58.23% of the traditional general classifier. It improves the generalization ability of emotion brain-computer interface in practical application, and provides a new method for aBCIs to be used in practice.

Towards designing Brain-Computer Interfaces in terms of User-Profiles, Neurophysiological Factors and User Experience / Hacia el diseño de Interfaces Cerebro-Computadora en términos del Perfil del Usuario, Factores Neurofisiológico y Experiencia del Usuario

Abstract Brain-computer interfaces (BCIs) are technology in development that attempt to establish interaction between individuals and their surroundings by modulating their neural activity. One of the most common strategies to modulate neural activity is motor imagery (MI). However, research on MI-based BCIs has been mostly carried out on the system-related part, whereas the user-related part has been relatively ignored. Thus far, up to 30% of users cannot gain control of BCI, while the remaining ones reach modest performance. The exclusion of users in the system design has possibly led to this outcome. Therefore, the aim of this paper is to establish a mixed method based on interactive design principles and in line with (1) user-profile, (2) psychological and (3) neurophysiological factors, (4) BCI technical issues and (5) user-experience. Although some of these elements have been previously discussed, their integration and application are seldom considered during investigation.

Resumen Las interfaces cerebro-computadora (ICC) son tecnología en desarrollo que intenta establecer interacción entre un individuo y su entorno a través de la modulación de su actividad neuronal. Una de las estrategias más usadas para modular la actividad neuronal ha sido la imaginación motora. Sin embargo, la investigación en ICC controladas por imaginación motora ha sido desarrollada mayoritariamente en términos del sistema, donde el usuario es generalmente ignorado. A la fecha, hasta el 30% de los usuarios no pueden controlar un sistema ICC basado en imaginación motora, mientras que el resto de los usuarios alcanzan un desempeño moderado. La exclusión de los usuarios en el diseño del sistema, posiblemente ha llevado al bajo índice de adaptación entre el sistema y el usuario. En base a esta evidencia, el objetivo de este artículo es establecer un método mixto sustentado en principios de diseño interactivo y considerando cinco elementos: (1) perfil del usuario, (2) factores psicológicos y (3) neurofisiológicos, (4) factores técnicos y (5) experiencia del usuario. Aunque todos estos elementos han sido discutidos previamente, su integración y aplicación son muy poco frecuentes durante la investigación.

Decoding Saccadic Directions Using Epidural ECoG in Non-Human Primates

A brain-computer interface (BCI) can be used to restore some communication as an alternative interface for patients suffering from locked-in syndrome. However, most BCI systems are based on SSVEP, P300, or motor imagery, and a diversity of BCI protocols would be needed for various types of patients. In this paper, we trained the choice saccade (CS) task in 2 non-human primate monkeys and recorded the brain signal using an epidural electrocorticogram (eECoG) to predict eye movement direction. We successfully predicted the direction of the upcoming eye movement using a support vector machine (SVM) with the brain signals after the directional cue onset and before the saccade execution. The mean accuracies were 80% for 2 directions and 43% for 4 directions. We also quantified the spatial-spectro-temporal contribution ratio using SVM recursive feature elimination (RFE). The channels over the frontal eye field (FEF), supplementary eye field (SEF), and superior parietal lobule (SPL) area were dominantly used for classification. The α-band in the spectral domain and the time bins just after the directional cue onset and just before the saccadic execution were mainly useful for prediction. A saccade based BCI paradigm can be projected in the 2D space, and will hopefully provide an intuitive and convenient communication platform for users.

Experimental Set Up of P300 Based Brain Computer Interface Using a Bioamplifier and BCI2000 System for Patients with Spinal Cord Injury

OBJECTIVE: Brain computer interface (BCI) is one of the most promising technologies for helping people with neurological disorders. Most current BCI systems are relatively expensive and difficult to set up. Therefore, we developed a P300-based BCI system with a cheap bioamplifier and open source software. The purpose of this study was to describe the setup process of the system and preliminary experimental results. METHODS: Ten spinal cord-injured patients were recruited. We used a sixteen-channel EEG(KT88-1016, Contec, China) and BCI2000 software (Wadsworth center, NY, USA). Subjects were asked to spell a 5-character word using the P300-based BCI system with 10 minutes of training. EEG data were acquired during the experiment. After subjects spelled the word for ten trials, the spelling accuracy and information transfer rate (ITR) were obtained in each patients. RESULTS: All subjects performed the experiment without difficulty. The mean accuracy was 59.4+/-22.8%. The spelling accuracy reversely correlated with the age. Younger subjects spelled with higher accuracy than older subjects (p=0.018). However, sex, injury level, time since injury and ASIA scale were not correlated with the accuracy. The mean of ITR was 2.26+/-1.22 bit/min. CONCLUSION: This study showed that a BCI system can be set up inexpensively with a low-price bioamplifier and open-source software. The spelling accuracy was moderately achieved with our system. P300-based BCI is useful in young patients, but modification is necessary in old patients who have low ability of recognition and concentration.