Non-Invasive Brain-to-Brain Interface (BBI): Establishing Functional Links between Two Brains

Transcranial focused ultrasound (FUS) is capable of modulating the neural activity of specific brain regions, with a potential role as a non-invasive computer-to-brain interface (CBI). In conjunction with the use of brain-to-computer interface (BCI) techniques that translate brain function to generate computer commands, we investigated the feasibility of using the FUS-based CBI to non-invasively establish a functional link between the brains of different species (i.e. human and Sprague-Dawley rat), thus creating a brain-to-brain interface (BBI). The implementation was aimed to non-invasively translate the human volunteer’s intention to stimulate a rat’s brain motor area that is responsible for the tail movement. The volunteer initiated the intention by looking at a strobe light flicker on a computer display, and the degree of synchronization in the electroencephalographic steady-state-visual-evoked-potentials (SSVEP) with respect to the strobe frequency was analyzed using a computer. Increased signal amplitude in the SSVEP, indicating the volunteer’s intention, triggered the delivery of a burst-mode FUS (350 kHz ultrasound frequency, tone burst duration of 0.5 ms, pulse repetition frequency of 1 kHz, given for 300 msec duration) to excite the motor area of an anesthetized rat transcranially. The successful excitation subsequently elicited the tail movement, which was detected by a motion sensor. The interface was achieved at 94.0±3.0% accuracy, with a time delay of 1.59±1.07 sec from the thought-initiation to the creation of the tail movement. Our results demonstrate the feasibility of a computer-mediated BBI that links central neural functions between two biological entities, which may confer unexplored opportunities in the study of neuroscience with potential implications for therapeutic applications.     

A Direct Brain-to-Brain Interface in Humans

We describe the first direct brain-to-brain interface in humans and present results from experiments involving six different subjects. Our non-invasive interface, demonstrated originally in August 2013, combines electroencephalography (EEG) for recording brain signals with transcranial magnetic stimulation (TMS) for delivering information to the brain. We illustrate our method using a visuomotor task in which two humans must cooperate through direct brain-to-brain communication to achieve a desired goal in a computer game. The brain-to-brain interface detects motor imagery in EEG signals recorded from one subject (the “sender”) and transmits this information over the internet to the motor cortex region of a second subject (the “receiver”). This allows the sender to cause a desired motor response in the receiver (a press on a touchpad) via TMS. We quantify the performance of the brain-to-brain interface in terms of the amount of information transmitted as well as the accuracies attained in (1) decoding the sender’s signals, (2) generating a motor response from the receiver upon stimulation, and (3) achieving the overall goal in the cooperative visuomotor task. Our results provide evidence for a rudimentary form of direct information transmission from one human brain to another using non-invasive means.     

Brain-Computer Interface Controlled Cyborg: Establishing a Functional Information Transfer Pathway from Human Brain to Cockroach Brain

An all-chain-wireless brain-to-brain system (BTBS), which enabled motion control of a cyborg cockroach via human brain, was developed in this work. Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) was used in this system for recognizing human motion intention and an optimization algorithm was proposed in SSVEP to improve online performance of the BCI. The cyborg cockroach was developed by surgically integrating a portable microstimulator that could generate invasive electrical nerve stimulation. Through Bluetooth communication, specific electrical pulse trains could be triggered from the microstimulator by BCI commands and were sent through the antenna nerve to stimulate the brain of cockroach. Serial experiments were designed and conducted to test overall performance of the BTBS with six human subjects and three cockroaches. The experimental results showed that the online classification accuracy of three-mode BCI increased from 72.86% to 78.56% by 5.70% using the optimization algorithm and the mean response accuracy of the cyborgs using this system reached 89.5%. Moreover, the results also showed that the cyborg could be navigated by the human brain to complete walking along an S-shape track with the success rate of about 20%, suggesting the proposed BTBS established a feasible functional information transfer pathway from the human brain to the cockroach brain.     

Artificial feedback for invasive brain–computer interfaces

During the last two decades, considerable progress has been made in the studies of brain–computer interfaces (BCIs)—devices in which motor signals from the brain are registered by multi-electrode arrays and transformed into commands for artificial actuators such as cursors and robotic devices. This review is focused on one problem. Voluntary motor control is based on neurophysiological processes, which strongly depend on the afferent innervation of skin, muscles, and joints. Thus, invasive BCI has to be based on a bidirectional system in which motor control signals are registered by multichannel microelectrodes implanted in motor areas, whereas tactile, proprioceptive, and other useful signals are transported back to the brain through spatiotemporal patterns of intracortical microstimulation (ICMS) delivered to sensory areas. In general, the studies of invasive BCIs have advanced in several directions. The progress of BCIs with artificial sensory feedback will not only help patients, but will also expand base knowledge in the field of human cortical functions.     

A collaborative brain-computer interface for improving human performance

Electroencephalogram (EEG) based brain-computer interfaces (BCI) have been studied since the 1970s. Currently, the main focus of BCI research lies on the clinical use, which aims to provide a new communication channel to patients with motor disabilities to improve their quality of life. However, the BCI technology can also be used to improve human performance for normal healthy users. Although this application has been proposed for a long time, little progress has been made in real-world practices due to technical limits of EEG. To overcome the bottleneck of low single-user BCI performance, this study proposes a collaborative paradigm to improve overall BCI performance by integrating information from multiple users. To test the feasibility of a collaborative BCI, this study quantitatively compares the classification accuracies of collaborative and single-user BCI applied to the EEG data collected from 20 subjects in a movement-planning experiment. This study also explores three different methods for fusing and analyzing EEG data from multiple subjects: (1) Event-related potentials (ERP) averaging, (2) Feature concatenating, and (3) Voting. In a demonstration system using the Voting method, the classification accuracy of predicting movement directions (reaching left vs. reaching right) was enhanced substantially from 66% to 80%, 88%, 93%, and 95% as the numbers of subjects increased from 1 to 5, 10, 15, and 20, respectively. Furthermore, the decision of reaching direction could be made around 100-250 ms earlier than the subject's actual motor response by decoding the ERP activities arising mainly from the posterior parietal cortex (PPC), which are related to the processing of visuomotor transmission. Taken together, these results suggest that a collaborative BCI can effectively fuse brain activities of a group of people to improve the overall performance of natural human behavior.     

Selective Sensation Based Brain-Computer Interface via Mechanical Vibrotactile Stimulation

In this work, mechanical vibrotactile stimulation was applied to subjects’ left and right wrist skins with equal intensity, and a selective sensation perception task was performed to achieve two types of selections similar to motor imagery Brain-Computer Interface. The proposed system was based on event-related desynchronization/synchronization (ERD/ERS), which had a correlation with processing of afferent inflow in human somatosensory system, and attentional effect which modulated the ERD/ERS. The experiments were carried out on nine subjects (without experience in selective sensation), and six of them showed a discrimination accuracy above 80%, three of them above 95%. Comparative experiments with motor imagery (with and without presence of stimulation) were also carried out, which further showed the feasibility of selective sensation as an alternative BCI task complementary to motor imagery. Specifically there was significant improvement () from near 65% in motor imagery (with and without presence of stimulation) to above 80% in selective sensation on some subjects. The proposed BCI modality might well cooperate with existing BCI modalities in the literature in enlarging the widespread usage of BCI system.     

An optical brain-to-brain interface supports rapid information transmission for precise locomotion control

Brain-to-brain interfaces(BtBIs) hold exciting potentials for direct communication between individual brains. However,technical challenges often limit their performance in rapid information transfer. Here, we demonstrate an optical brain-to-brain interface that transmits information regarding locomotor speed from one mouse to another and allows precise, real-time control of locomotion across animals with high information transfer rate. We found that the activity of the genetically identified neuromedin B(NMB) neurons within the nucleus incertus(NI) precisely predicts and critically controls locomotor speed. By optically recording Ca~(2+) signals from the NI of a "Master" mouse and converting them to patterned optogenetic stimulations of the NI of an "Avatar" mouse, the Bt BI directed the Avatar mice to closely mimic the locomotion of their Masters with information transfer rate about two orders of magnitude higher than previous Bt BIs. These results thus provide proof-of-concept that optical Bt BIs can rapidly transmit neural information and control dynamic behaviors across individuals.     

Principles of neurorehabilitation based on the brain-computer interface and biologically adequate control of the exoskeleton

The neurophysiological prerequisites for the development and operation of the brain-computer interfaces (BCI) that allow cerebral electrical signals alone to control external technical devices are considered. A BCI based on the discrimination of the EEG patterns related to imagery of extremity movements is described. The possibility of the rehabilitation of patients with motor disorders by means of the BCI based on motor imagery and the exoskeleton controlled by it is discussed.     

A real-time fMRI-based spelling device immediately enabling robust motor-independent communication

Human communication entirely depends on the functional integrity of the neuromuscular system. This is devastatingly illustrated in clinical conditions such as the so-called locked-in syndrome (LIS), in which severely motor-disabled patients become incapable to communicate naturally--while being fully conscious and awake. For the last 20 years, research on motor-independent communication has focused on developing brain-computer interfaces (BCIs) implementing neuroelectric signals for communication (e.g., [2-7]), and BCIs based on electroencephalography (EEG) have already been applied successfully to concerned patients. However, not all patients achieve proficiency in EEG-based BCI control. Thus, more recently, hemodynamic brain signals have also been explored for BCI purposes. Here, we introduce the first spelling device based on fMRI. By exploiting spatiotemporal characteristics of hemodynamic responses, evoked by performing differently timed mental imagery tasks, our novel letter encoding technique allows translating any freely chosen answer (letter-by-letter) into reliable and differentiable single-trial fMRI signals. Most importantly, automated letter decoding in real time enables back-and-forth communication within a single scanning session. Because the suggested spelling device requires only little effort and pretraining, it is immediately operational and possesses high potential for clinical applications, both in terms of diagnostics and establishing short-term communication with nonresponsive and severely motor-impaired patients.     

Encoding-based brain-computer interface controlled by non-motor area of rat brain

As the needs of disabled patients are increasingly recognized in society, researchers have begun to use single neuron activity to construct brain-computer interfaces (BCI), designed to facilitate the daily lives of individuals with physical disabilities. BCI systems typically allow users to control computer programs or external devices via signals produced in the motor or pre-motor areas of the brain, rather than producing actual motor movements. However, impairments in these brain areas can hinder the application of BCI. The current paper demonstrates the feasibility of a one-dimensional (1D) machine controlled by rat prefrontal cortex (PFC) neurons using an encoding method. In this novel system, rats are able to quench thirst by varying neuronal firing rate in the PFC to manipulate a water dish that can rotate in 1D. The results revealed that control commands generated by an appropriate firing frequency in rat PFC exhibited performance improvements with practice, indicated by increasing water-drinking duration and frequency. These results demonstrated that it is possible for rats to understand an encoding-based BCI system and control a 1D machine using PFC activity to obtain reward.     

Prediction of P300 BCI Aptitude in Severe Motor Impairment

Brain-computer interfaces (BCIs) provide a non-muscular communication channel for persons with severe motor impairments. Previous studies have shown that the aptitude with which a BCI can be controlled varies from person to person. A reliable predictor of performance could facilitate selection of a suitable BCI paradigm. Eleven severely motor impaired participants performed three sessions of a P300 BCI web browsing task. Before each session auditory oddball data were collected to predict the BCI aptitude of the participants exhibited in the current session. We found a strong relationship of early positive and negative potentials around 200 ms (elicited with the auditory oddball task) with performance. The amplitude of the P2 (r  =  −0.77) and of the N2 (r  =  −0.86) had the strongest correlations. Aptitude prediction using an auditory oddball was successful. The finding that the N2 amplitude is a stronger predictor of performance than P3 amplitude was reproduced after initially showing this effect with a healthy sample of BCI users. This will reduce strain on the end-users by minimizing the time needed to find suitable paradigms and inspire new approaches to improve performance.     

Information und Energie in der Physiologie

Research in sensory physiology proves the usefulness of describing distinct functions of sensory systems from the point of view of information processing, neglecting energetic (metabolic) processes, which may occur simultaneously. On the other hand, complex metabolic processes play an important role in sensory reception and sensory communication. Adaptation — in quite a few situations resulting in an actual gain of sensory information — is based upon interacting metabolic processes. It is conceivable that various enzyme systems, such as coenzyme B, involved in the building and destruction of a particular exitatory substance, e. g., acetylcholine and cholinesterase, influence the speed of these different metabolic processes within the sensory cells. It is even possible to separate the damage done to these processes by using an electrophysiological recording of combined action potentials on the auditory nerve and accounting for the time course of adaptation according toRanke's adaptation theory. The human central nervous system selects the 100 bit/sec processed for conscious perception from the 10⁹ bit/sec offered from all sensory receptors in two principal ways: (1) “Specific auditory information” is modulated by “unspecific” information processed through the reticular formation of the brain stem; (2) descending fiber systems alter selectively the information flow on every level of the auditory pathway. The filtered information perceived, in turn triggers a set of inborn and learned behavioral responses, such as speech and motor reaction, altogether representing appromaximately 10⁷ bit/sec. Metabolic processes possibly involved in this optimizing system are largely unknown.     

Prediction of Auditory and Visual P300 Brain-Computer Interface Aptitude

Objective

Brain-computer interfaces (BCIs) provide a non-muscular communication channel for patients with late-stage motoneuron disease (e.g., amyotrophic lateral sclerosis (ALS)) or otherwise motor impaired people and are also used for motor rehabilitation in chronic stroke. Differences in the ability to use a BCI vary from person to person and from session to session. A reliable predictor of aptitude would allow for the selection of suitable BCI paradigms. For this reason, we investigated whether P300 BCI aptitude could be predicted from a short experiment with a standard auditory oddball.

Methods

Forty healthy participants performed an electroencephalography (EEG) based visual and auditory P300-BCI spelling task in a single session. In addition, prior to each session an auditory oddball was presented. Features extracted from the auditory oddball were analyzed with respect to predictive power for BCI aptitude.

Results

Correlation between auditory oddball response and P300 BCI accuracy revealed a strong relationship between accuracy and N2 amplitude and the amplitude of a late ERP component between 400 and 600 ms. Interestingly, the P3 amplitude of the auditory oddball response was not correlated with accuracy.

Conclusions

Event-related potentials recorded during a standard auditory oddball session moderately predict aptitude in an audiory and highly in a visual P300 BCI. The predictor will allow for faster paradigm selection.

Significance

Our method will reduce strain on patients because unsuccessful training may be avoided, provided the results can be generalized to the patient population.     

On the Quantification of SSVEP Frequency Responses in Human EEG in Realistic BCI Conditions

This article concerns one of the most important problems of brain-computer interfaces (BCI) based on Steady State Visual Evoked Potentials (SSVEP), that is the selection of the a-priori most suitable frequencies for stimulation. Previous works related to this problem were done either with measuring systems that have little in common with actual BCI systems (e.g., single flashing LED) or were presented on a small number of subjects, or the tested frequency range did not cover a broad spectrum. Their results indicate a strong SSVEP response around 10 Hz, in the range 13–25 Hz, and at high frequencies in the band of 40–60 Hz. In the case of BCI interfaces, stimulation with frequencies from various ranges are used. The frequencies are often adapted for each user separately. The selection of these frequencies, however, was not yet justified in quantitative group-level study with proper statistical account for inter-subject variability. The aim of this study is to determine the SSVEP response curve, that is, the magnitude of the evoked signal as a function of frequency. The SSVEP response was induced in conditions as close as possible to the actual BCI system, using a wide range of frequencies (5–30 Hz, in step of 1 Hz). The data were obtained for 10 subjects. SSVEP curves for individual subjects and the population curve was determined. Statistical analysis were conducted both on the level of individual subjects and for the group. The main result of the study is the identification of the optimal range of frequencies, which is 12–18 Hz, for the registration of SSVEP phenomena. The applied criterion of optimality was: to find the largest contiguous range of frequencies yielding the strong and constant-level SSVEP response.     

Extracting optimal tempo-spatial features using local discriminant bases and common spatial patterns for brain computer interfacing

Brain computer interfaces (BCI) provide a new approach to human computer communication, where the control is realised via performing mental tasks such as motor imagery (MI). In this study, we investigate a novel method to automatically segment electroencephalographic (EEG) data within a trial and extract features accordingly in order to improve the performance of MI data classification techniques. A new local discriminant bases (LDB) algorithm using common spatial patterns (CSP) projection as transform function is proposed for automatic trial segmentation. CSP is also used for feature extraction following trial segmentation. This new technique also allows to obtain a more accurate picture of the most relevant temporal–spatial points in the EEG during the MI. The results are compared with other standard temporal segmentation techniques such as sliding window and LDB based on the local cosine transform (LCT).     

神经工程与脑-机接口

神经工程是近年来在生物医学工程领域备受关注的学科发展新方向。它运用神经科学和工程学的方法来分析神经功能并为神经功能缺失与紊乱的修复提供新的解决问题的方案;而脑-机接口则是当前神经工程领域中最活跃的研究方向之一。脑-机接口是在脑与计算机或其他外部设备之间建立的直接的通信和交流通道。在脑-机接口系统中,具有特定模式的脑信号携带着受试者希望表达的意愿,计算机将接收到的脑信号转换成相应的控制命令,于是那些有运动障碍的残疾人就可以利用脑-机接口系统来实现与外界的交流与对外部设备的控制。在基于脑电信号的脑-机接口系统中,受试者产生的脑信号大致可以分为内源性（endogenous）和外源性（exogenous）两类。其中外源性的成分主要取决于外部物理刺激（视觉、听觉或触觉）的参数而与认知行为无关;而内源性成分则主要由认知行为产生而与外部的物理刺激无关。在许多情况下,脑－机接口中的瞬态诱发电位通常都同时包含着内源性和外源性两种成分。寻找新的脑－机接口模式使之能显著提升记录脑电信号中的内源性与外源性成分在脑－机接口研究中具有重要意义。本文中将介绍一种基于运动起始时刻（motion—onset）的新的脑-机接口实验范式。本文的最后还探讨了脑－机接口未来发展的趋势与展望。     

Shaping the dynamics of a bidirectional neural interface

Progress in decoding neural signals has enabled the development of interfaces that translate cortical brain activities into commands for operating robotic arms and other devices. The electrical stimulation of sensory areas provides a means to create artificial sensory information about the state of a device. Taken together, neural activity recording and microstimulation techniques allow us to embed a portion of the central nervous system within a closed-loop system, whose behavior emerges from the combined dynamical properties of its neural and artificial components. In this study we asked if it is possible to concurrently regulate this bidirectional brain-machine interaction so as to shape a desired dynamical behavior of the combined system. To this end, we followed a well-known biological pathway. In vertebrates, the communications between brain and limb mechanics are mediated by the spinal cord, which combines brain instructions with sensory information and organizes coordinated patterns of muscle forces driving the limbs along dynamically stable trajectories. We report the creation and testing of the first neural interface that emulates this sensory-motor interaction. The interface organizes a bidirectional communication between sensory and motor areas of the brain of anaesthetized rats and an external dynamical object with programmable properties. The system includes (a) a motor interface decoding signals from a motor cortical area, and (b) a sensory interface encoding the state of the external object into electrical stimuli to a somatosensory area. The interactions between brain activities and the state of the external object generate a family of trajectories converging upon a selected equilibrium point from arbitrary starting locations. Thus, the bidirectional interface establishes the possibility to specify not only a particular movement trajectory but an entire family of motions, which includes the prescribed reactions to unexpected perturbations.     

Design of an EEG-based brain-computer interface (BCI) from standard components running in real-time under Windows.

An EEG-based brain-computer interface (BCI) is a direct connection between the human brain and the computer. Such a communication system is needed by patients with severe motor impairments (e.g. late stage of Amyotrophic Lateral Sclerosis) and has to operate in real-time. This paper describes the selection of the appropriate components to construct such a BCI and focuses also on the selection of a suitable programming language and operating system. The multichannel system runs under Windows 95, equipped with a real-time Kernel expansion to obtain reasonable real-time operations on a standard PC. Matlab controls the data acquisition and the presentation of the experimental paradigm, while Simulink is used to calculate the recursive least square (RLS) algorithm that describes the current state of the EEG in real-time. First results of the new low-cost BCI show that the accuracy of differentiating imagination of left and right hand movement is around 95%.     

基于节律性脑电信号的脑-机接口

脑-机接口系统是一个不依靠外周神经和肌肉组织等而实现大脑和外界装置之间直接的交流和控制的通道。它为那些运动障碍的残疾人表达自己的意愿和实现对外部设备的控制提供了一种新的强大的技术支持。基于脑电的脑-机接口作为一种非侵入型的技术引起了该领域很多人的关注。基于脑电的脑-机接口采用了很多种类型的脑电信号。其中,振荡性的脑电图由于有较高的幅值和对噪声不敏感等特性而体现出极大的优势。也是由于这些原因,振荡性的脑电图变成了脑-机接口的应用中非常成功的设计之一。本文要介绍主要的基于脑电的脑-机接口中的两种,分别是稳态视觉诱发电位和基于运动本体感觉节律的脑-机接口。作者将详细的叙述该研究的生理背景、脑-机接口的参数,以及该系统的构造及信号处理的方法,并且会演示一些具有潜在应用价值的科研成果。     

The User-Centered Design as Novel Perspective for Evaluating the Usability of BCI-Controlled Applications

Albeit research on brain-computer interfaces (BCI) for controlling applications has expanded tremendously, we still face a translational gap when bringing BCI to end-users. To bridge this gap, we adapted the user-centered design (UCD) to BCI research and development which implies a shift from focusing on single aspects, such as accuracy and information transfer rate (ITR), to a more holistic user experience. The UCD implements an iterative process between end-users and developers based on a valid evaluation procedure. Within the UCD framework usability of a device can be defined with regard to its effectiveness, efficiency, and satisfaction. We operationalized these aspects to evaluate BCI-controlled applications. Effectiveness was regarded equivalent to accuracy of selections and efficiency to the amount of information transferred per time unit and the effort invested (workload). Satisfaction was assessed with questionnaires and visual-analogue scales. These metrics have been successfully applied to several BCI-controlled applications for communication and entertainment, which were evaluated by end-users with severe motor impairment. Results of four studies, involving a total of N = 19 end-users revealed: effectiveness was moderate to high; efficiency in terms of ITR was low to high and workload low to medium; depending on the match between user and technology, and type of application satisfaction was moderate to high. The here suggested evaluation metrics within the framework of the UCD proved to be an applicable and informative approach to evaluate BCI controlled applications, and end-users with severe impairment and in the locked-in state were able to participate in this process.