Error-Related EEG Potentials Generated During Simulated Brain–Computer Interaction

Brain-computer interfaces (BCIs) are prone to errors in the recognition of subject's intent. An elegant approach to improve the accuracy of BCIs consists in a verification procedure directly based on the presence of error-related potentials (ErrP) in the electroencephalogram (EEG) recorded right after the occurrence of an error. Several studies show the presence of ErrP in typical choice reaction tasks. However, in the context of a BCI, the central question is: ldquoAre ErrP also elicited when the error is made by the interface during the recognition of the subject's intent?rdquo We have thus explored whether ErrP also follow a feedback indicating incorrect responses of the simulated BCI interface. Five healthy volunteer subjects participated in a new human-robot interaction experiment, which seem to confirm the previously reported presence of a new kind of ErrP. However, in order to exploit these ErrP, we need to detect them in each single trial using a short window following the feedback associated to the response of the BCI. We have achieved an average recognition rate of correct and erroneous single trials of 83.5% and 79.2%, respectively, using a classifier built with data recorded up to three months earlier.     

xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface

A brain-computer interface (BCI) is a communication system that allows to control a computer or any other device thanks to the brain activity. The BCI described in this paper is based on the P300 speller BCI paradigm introduced by Farwell and Donchin. An unsupervised algorithm is proposed to enhance P300 evoked potentials by estimating spatial filters; the raw EEG signals are then projected into the estimated signal subspace. Data recorded on three subjects were used to evaluate the proposed method. The results, which are presented using a Bayesian linear discriminant analysis classifier, show that the proposed method is efficient and accurate.     

Steady-State Movement Related Potentials for Brain–Computer Interfacing

An approach for brain-computer interfacing (BCI) by analysis of steady-state movement related potentials (ssMRPs) produced during rhythmic finger movements is proposed in this paper. The neurological background of ssMRPs is briefly reviewed. Averaged ssMRPs represent the development of a lateralized rhythmic potential, and the energy of the EEG signals at the finger tapping frequency can be used for single-trial ssMRP classification. The proposed ssMRP-based BCI approach is tested using the classic Fisher's linear discriminant classifier. Moreover, the influence of the current source density transform on the performance of BCI system is investigated. The averaged correct classification rates (CCRs) as well as averaged information transfer rates (ITRs) for different sliding time windows are reported. Reliable single-trial classification rates of 88%-100% accuracy are achievable at relatively high ITRs. Furthermore, we have been able to achieve CCRs of up to 93% in classification of the ssMRPs recorded during imagined rhythmic finger movements. The merit of this approach is in the application of rhythmic cues for BCI, the relatively simple recording setup, and straightforward computations that make the real-time implementations plausible.     

Coadaptive Brain–Machine Interface via Reinforcement Learning

This paper introduces and demonstrates a novel brain–machine interface (BMI) architecture based on the concepts of reinforcement learning (RL), coadaptation, and shaping. RL allows the BMI control algorithm to learn to complete tasks from interactions with the environment, rather than an explicit training signal. Coadaption enables continuous, synergistic adaptation between the BMI control algorithm and BMI user working in changing environments. Shaping is designed to reduce the learning curve for BMI users attempting to control a prosthetic. Here, we present the theory and in vivo experimental paradigm to illustrate how this BMI learns to complete a reaching task using a prosthetic arm in a 3-D workspace based on the user's neuronal activity. This semisupervised learning framework does not require user movements. We quantify BMI performance in closed-loop brain control over six to ten days for three rats as a function of increasing task difficulty. All three subjects coadapted with their BMI control algorithms to control the prosthetic significantly above chance at each level of difficulty.      

Optimizing Spatial filters for Robust EEG Single-Trial Analysis

Due to the volume conduction multichannel electroencephalogram (EEG) recordings give a rather blurred image of brain activity. Therefore spatial filters are extremely useful in single-trial analysis in order to improve the signal-to-noise ratio. There are powerful methods from machine learning and signal processing that permit the optimization of spatio-temporal filters for each subject in a data dependent fashion beyond the fixed filters based on the sensor geometry, e.g., Laplacians. Here we elucidate the theoretical background of the common spatial pattern (CSP) algorithm, a popular method in brain-computer interface (BCD research. Apart from reviewing several variants of the basic algorithm, we reveal tricks of the trade for achieving a powerful CSP performance, briefly elaborate on theoretical aspects of CSP, and demonstrate the application of CSP-type preprocessing in our studies of the Berlin BCI (BBCI) project.     

Recent Developments in Spatio-Temporal EEG Source Reconstruction Techniques

EEG is gaining recognition in the field of real-time applications. However, the EEG inverse problem leads to poor spatial resolution in brain source localization. This paper presents an overview of the existing EEG inverse solution methods. Further, a comparative analysis of recent techniques has been presented. This work discusses the challenges associated with the existing source reconstruction algorithms. The main focus is on the recent reports in this field that have combined the EEG denoising in the pre-processing phase along with the inverse localization approaches. Out of various existing techniques, SLORETA offers better localization results but its noise sensitivity is very high. It has been validated in a comparative analysis for simulated dipole sources with no noise. To illustrate the advantage of using pre-processed data with inverse localization, the classification accuracy of conventional methods has been compared. The accuracy has been analyzed for depression signals using the Naïve Bayes, RF, and SVM classifiers. The VMD- SLORETA method shows better accuracy as compared to EMD-SLORETA and SLORETA only. The existing EEG localization methods are efficient but the spatial resolution is still to be improved in the presence of various noise sources in raw EEG. More accurate localization is achieved by implementing denoising in combination with the source localization framework. There is a need to investigate further stages of EEG signal processing along with optimal feature selection. Further, additional studies should be conducted to improve the noise sensitivity of other existing localization systems using pre-processing approaches.

Graphic Abstract

     

Decision-Level Fusion of EEG and Pupil Features for Single-Trial Visual Detection Analysis

Several recent studies have reported success in applying EEG-based signal analysis to achieve accurate single-trial classification of responses to visual target detection. Pupil responses are proposed as a complementary modality that can support improved accuracy of single-trial signal analysis. We develop a pupillary response feature-extraction and -selection procedure that helps to improve the classification performance of a system based only on EEG signal analysis. We apply a two-level linear classifier to obtain cognitive-task-related analysis of EEG and pupil responses. The classification results based on the two modalities are then fused at the decision level. Here, the goal is to support increased classification confidence through the inherent modality complementarities. The fusion results show significant improvement over classification performance based on a single modality.     

Design of an EEG Preamplifier for Brain-Computer Interface

As a non-invasive neurophysiological index for brain-computer interface (BCI), electroencephalogram (EEG) attracts much attention at present. In order to have a portable BCI, a simple and efficient pre-amplifier is crucial in practice. In this work, a preamplifier based on the characteristics of EEG signals is designed, which consists of a highly symmetrical input stage, low-pass filter, 50 Hz notch filter and a post amplifier. A prototype of this EEG module is fabricated and EEG data are obtained through an actual experiment. The results demonstrate that the EEG preamplifier will be a promising unit for BCI in the future.     

Design of an EEG Preamplifier for Brain-Computer Interface

As a non-invasive neurophysiologieal index for brain-computer interface （BCI）, electroencephalogram （EEG） attracts much attention at present. In order to have a portable BCI, a simple and efficient pre-amplifier is crucial in practice. In this work, a preamplifier based on the characteristics of EEG signals is designed, which consists of a highly symmetrical input stage, low-pass filter, 50 Hz notch filter and a post amplifier. A prototype of this EEG module is fabricated and EEG data are obtained through an actual experiment. The results demonstrate that the EEG preamplifier will be a promising unit for BCI in the future.     

Human–Machine Interface for Robotic Surgery and Stereotaxy

While considerable technology has been integrated into the operating room, until recently, the actual performance of surgery has seen relatively few changes, relying mainly on hand-eye coordination. This paper outlines the development and composition as well as the requirements and reasoning that lead to the human-machine interface on neuroArm, a telerobotic surgical system. A critical component of the system was the workstation, where information was provided to and received from the operator. The surgeon controls the robotic system using two force-feedback hand controllers based on visual information from a stereoscopic viewing device and two liquid crystal displays. Two touch screens allow the user to monitor and control the settings of the robot and to view and manipulate 3-D MR images. Audio feedback from the surgical site and the operating room staff is also provided by a wireless communication system. The workstation components were chosen not only to recreate the sight, sound, and touch of surgery but also to facilitate the integration of surgeons with advanced imaging and robotic technologies.     

Insect–Machine Interface Based Neurocybernetics

We present details of a novel bioelectric interface formed by placing microfabricated probes into insect during metamorphic growth cycles. The inserted microprobes emerge with the insect where the development of tissue around the electronics during the pupal development allows mechanically stable and electrically reliable structures coupled to the insect. Remarkably, the insects do not react adversely or otherwise to the inserted electronics in the pupae stage, as is true when the electrodes are inserted in adult stages. We report on the electrical and mechanical characteristics of this novel bioelectronic interface, which we believe would be adopted by many investigators trying to investigate biological behavior in insects with negligible or minimal traumatic effect encountered when probes are inserted in adult stages. This novel insect–machine interface also allows for hybrid insect–machine platforms for further studies. As an application, we demonstrate our first results toward navigation of flight in moths. When instrumented with equipment to gather information for environmental sensing, such insects potentially can assist man to monitor the ecosystems that we share with them for sustainability. The simplicity of the optimized surgical procedure we invented allows for batch insertions to the insect for automatic and mass production of such hybrid insect–machine platforms. Therefore, our bioelectronic interface and hybrid insect–machine platform enables multidisciplinary scientific and engineering studies not only to investigate the details of insect behavioral physiology but also to control it.      

Simultaneous EEG Source and Forward Model Reconstruction (SOFOMORE) Using a Hierarchical Bayesian Approach

We present an approach to handle forward model uncertainty for EEG source reconstruction. A stochastic forward model representation is motivated by the many random contributions to the path from sources to measurements including the tissue conductivity distribution, the geometry of the cortical surface, and electrode positions. We first present a hierarchical Bayesian framework for EEG source localization that jointly performs source and forward model reconstruction (SOFOMORE). Secondly, we evaluate the SOFOMORE approach by comparison with source reconstruction methods that use fixed forward models. Analysis of simulated and real EEG data provide evidence that reconstruction of the forward model leads to improved source estimates.     

一种抗干扰能力极强的计算机通信接口电路

设计了一种结构简单，抗干扰能力极强的计算机通信接口电路。该电路利用模拟开关实现了全数字二态电平（1，0）与三态电平(1，0，-1)之间的相互转换；利用模拟开关导通电阻的动态效应以及MOS晶体管开启电压Vτ的衬偏效应，提高了接收电路的抗干扰能力，提出了一种与共模干扰无关的模拟数字混合接收电路。     

Core–Shell Nanoparticle Interface and Wetting Properties

Latex colloids are among the most promising materials for broad thin film applications due to their facile surface functionalization. Yet, the effect of these colloids on chemical film and wetting properties cannot be easily evaluated. At the nanoscale, core–shell particles can deform and coalesce during thermal annealing, yielding fine‐tuned physical properties. Two different core–shell systems (soft and rigid) with identical shells but with chemically different core polymers and core sizes are investigated. The core–shell nanoparticles (NPs) are probed during thermal annealing in order to investigate their behavior as a function of nanostructure size and rigidity. X‐ray scattering allows to follow the re‐arrangement of the NPs and the structural evolution in situ during annealing. Evaluation by real‐space imaging techniques reveals a disappearance of the structural integrity and a loss of NP boundaries. The possibility to fine‐tune the wettability by tuning the core–shell NPs morphology in thin films provides a facile template methodology for repellent surfaces.     

计算机彩色显示器信号源

在维修CRT式节能型计算机彩色显示器时,若采用专用的计算机显示器信号源能避免因显示器的频繁开关而损坏主机显卡,操作需要时可直接开关显示器,无需考虑信号源的安全,大大方便维修操作。由7555时基IC构成的振荡器、触发器与用CD4060二进制串行计数器、74LS161二进制同步计数器等构成的分频电路能够组成满足上述要求的计算机彩色显示器信号源。     

机载计算机电源的小型化设计

介绍了机载计算机电源的现状,同时对进一步实现电源小型化的途径作了分析和探讨,最后给出了电源小型化的实例。     

Potential‐Specific Structure at the Hematite–Electrolyte Interface

The atomic‐scale structure of the interface between a transition metal oxide and aqueous electrolyte regulates the interfacial chemical reactions fundamental to (photo)electrochemical energy conversion and electrode degradation. Measurements that probe oxide–electrolyte interfaces in situ provide important details of ion and solvent arrangements, but atomically precise structural models do not exist for common oxide–electrolyte interfaces far from equilibrium. Using a novel cell, the structure of the hematite (α‐Fe₂O₃) ()–electrolyte interface is measured under controlled electrochemical bias using synchrotron crystal truncation rod X‐ray scattering. At increasingly cathodic potentials, charge‐compensating protonation of surface oxygen groups increases the coverage of specifically bound water while adjacent water layers displace outwardly and became disordered. Returning to open circuit potential leaves the surface in a persistent metastable state. Therefore, the flux of current and ions across the interface is regulated by multiple electrolyte layers whose specific structure and polarization change in response to the applied potential. The study reveals the complex environment underlying the simplified electrical double layer models used to interpret electrochemical measurements and emphasizes the importance of condition‐specific structural characterization for properly understanding catalytic processes at functional transition metal oxide–electrolyte interfaces.     

Surface Current Source Reconstruction for Given Radiated Electromagnetic Fields

A general analytic approach is presented for reconstructing: 1) the minimum energy source enclosed by a sphere, and 2) the surface current distribution on a sphere from the knowledge of the radiated fields. The surface current source is derived by adding proper non-radiating sources to the minimum energy source. In contrast to the minimum energy volumetric distribution, the surface current derived in this paper is practically realizable. Finally, we present a closed form formula for the reconstructed spherical surface current source. We will show that this spherical surface current is indeed the unique solution of the inverse source problem for square-integrable surface electric current on a sphere in a homogenous medium.      

Wireless Brain Computer Interface for Smart Home and Medical System

Wireless Personal Communications - The number of aged and disabled people has been increasing worldwide. To look after these people is a big challenge in this era. However, scientists overcome the...     

Constitutional Isomerization Enables Bright NIR‐II AIEgen for Brain‐Inflammation Imaging

The shortage of high quantum yield (QY) organic fluorophores in the second near‐infrared window (NIR‐II) has become a bottleneck in bioimaging field. Now, a simple strategy is proposed to address this: constitutional isomerization on the basis of the molecular design philosophy of aggregation‐induced emission. With the combination of backbone distortion and rotor twisting, the resultant NIR‐II fluorophore 2TT‐oC6B displays an emission peak at 1030 nm and a QY of 11% in nanoparticles, one of the highest reported so far. Control molecules confirm that the distorted backbone and twisted rotors play equally important roles in determining the fluorescence properties of the NIR‐II fluorophores. To allow for the targeting ability to reach deeply located diseases, neutrophils (NEs) are used to penetrate the brain tissues and accumulate in the inflammation site. Herein, it is shown that NEs carrying 2TT‐oC6B nanoparticles can penetrate the blood‐brain‐barrier and visualize the deeply located inflammation through an intact scalp and skull. Notably, the bright 2TT‐oC6B contributes to a significantly enhanced signal‐to‐background ratio of 30.6 in the brain inflammation site.