Double Deep Q-Network Decoder Based on EEG Brain-Computer Interface

Brain-computer interfaces(BCI)use neural activity as a control signal to enable direct communication between the human brain and external devices.The electrical signals generated by the brain are captured through electroencephalogram(EEG)and translated into neural intentions reflecting the user’s behavior.Correct decoding of the neural intentions then facilitates the control of external devices.Reinforcement learning-based BCIs enhance decoders to complete tasks based only on feedback signals(rewards)from the environment,building a general framework for dynamic mapping from neural intentions to actions that adapt to changing environments.However,using traditional reinforcement learning methods can have challenges such as the curse of dimensionality and poor generalization.Therefore,in this paper,we use deep reinforcement learning to construct decoders for the correct decoding of EEG signals,demonstrate its feasibility through experiments,and demonstrate its stronger generalization on motion imaging(MI)EEG data signals with high dynamic characteristics.

EEG classification based on probabilistic neural network with supervised learning in brain computer interface

Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented in this paper. It applies the recognition rate of training samples to the learning progress of network parameters. The learning vector quantization is employed to group training samples and the Genetic algorithm （GA） is used for training the network＇ s smoothing parameters and hidden central vector for detemlining hidden neurons. Utilizing the standard dataset I （a） of BCI Competition 2003 and comparing with other classification methods, the experiment results show that the best performance of pattern recognition Js got in this way, and the classification accuracy can reach to 93.8%, which improves over 5% compared with the best result （88.7 % ） of the competition. This technology provides an effective way to EEG classification in practical system of BCI.

AI+BCI硅基碳基融合新智能的开始

我们正迎来人类发展的第四次浪潮,正处于从信息社会向人类社会-物理世界-信息空间融合的智能社会的关键转型期。近年来,计算和信息技术飞速发展,深度学习的空前普及和成功将人工智能(AI)确立为人类探索机器智能的前沿领域。与此同时,得益于器件的革命性进展和人工智能(AI)的发展,脑机接口(BCI)植入技术同样快速落地,这意味着BCI+AI碳基硅基融合的开始,然而,硅基和碳基运算的底层逻辑存在根本差异,脑的智能机制仍有待进一步探索。本研究提出的视觉认知引导的孪生AI深度网络,是由个人意识驱动的深度网络技术,通过捕捉并解析个体的思维模式和创意灵感,为每个用户量身打造独特的视觉世界。在这样的环境中,每个人都成为自己创造世界的视觉主导者,打破物质和意识的壁垒,得以展现丰富的个性和创造力。

A Hybrid Brain-Computer Interface for Closed-Loop Position Control of a Robot Arm

Brain-Computer interfacing(BCI)has currently added a new dimension in assistive robotics.Existing braincomputer interfaces designed for position control applications suffer from two fundamental limitations.First,most of the existing schemes employ open-loop control,and thus are unable to track positional errors,resulting in failures in taking necessary online corrective actions.There are examples of a few works dealing with closed-loop electroencephalography(EEG)-based position control.These existing closed-loop brain-induced position control schemes employ a fixed order link selection rule,which often creates a bottleneck preventing time-efficient control.Second,the existing brain-induced position controllers are designed to generate a position response like a traditional firstorder system,resulting in a large steady-state error.This paper overcomes the above two limitations by keeping provisions for steady-state visual evoked potential(SSVEP)induced linkselection in an arbitrary order as required for efficient control and generating a second-order response of the position-control system with gradually diminishing overshoots/undershoots to reduce steady-state errors.Other than the above,the third innovation is to utilize motor imagery and P300 signals to design the hybrid brain-computer interfacing system for the said application with gradually diminishing error-margin using speed reversal at the zero-crossings of positional errors.Experiments undertaken reveal that the steady-state error is reduced to 0.2%.The paper also provides a thorough analysis of the stability of the closed-loop system performance using the Root Locus technique.

Electric Wheelchair Control System Using Brain-Computer Interface Based on Alpha-Wave Blocking

A brain-computer interface(BCI)-based electric wheelchair control system was developed, which enables the users to move the wheelchair forward or backward, and turn left or right without any pre-learning. This control system makes use of the amplitude enhancement of alpha-wave blocking in electroencephalogram(EEG) when eyes close for more than 1 s to constitute a BCI for the switch control of wheelchair movements. The system was formed by BCI control panel, data acquisition, signal processing unit and interface control circuit. Eight volunteers participated in the wheelchair control experiments according to the preset routes. The experimental results show that the mean success control rate of all the subjects was 81.3%, with the highest reaching 93.7%. When one subject's triggering time was 2.8 s, i.e., the flashing time of each cycle light was 2.8 s, the average information transfer rate was 8.10 bit/min, with the highest reaching 12.54 bit/min.

Non-invasive EEG-based brain-computer interfaces in patients with disorders of consciousness

Disorders of consciousness(DoCs) are chronic conditions resulting usually from severe neurological deficits. The limitations of the existing diagnosis systems and methodologies cause a need for additional tools for relevant patients with DoCs assessment, including brain-computer interfaces(BCIs). Recent progress in BCIs' clinical applications may offer important breakthroughs in the diagnosis and therapy of patients with DoCs. Thus the clinical significance of BCI applications in the diagnosis of patients with DoCs is hard to overestimate. One of them may be brain-computer interfaces. The aim of this study is to evaluate possibility of non-invasive EEG-based brain-computer interfaces in diagnosis of patients with DOCs in post-acute and long-term care institutions.

Individualization of Data-Segment-Related Parameters for Improvement of EEG Signal Classification in Brain-Computer Interface

In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four datasegment-related parameters in each trial of 12 subjects’ EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI.

Performance and Implementations of Vibrotactile Brain-Computer Interface with Ipsilateral and Bilateral Stimuli

The tactile P300 brain-computer interface( BCI) is related to the somatosensory perception and response of the human brain,and is different from visual or audio BCIs. Recently,several studies focused on the tactile stimuli delivered to different parts of the human body. Most of these stimuli were symmetrically bilateral.Only a fewstudies explored the influence of tactile stimuli laterality.In the current study,we extensively tested the performance of a vibrotactile BCI system using ipsilateral stimuli and bilateral stimuli.Two vibrotactile P300-based paradigms were tested. The target stimuli were located on the left and right forearms for the left forearm and right forearm( LFRF) paradigm,and on the left forearm and calf for the left forearm and left calf( LFLC)paradigm. Ten healthy subjects participated in this study. Our experiments and analysis showed that the bilateral paradigm( LFRF) elicited larger P300 amplitude and achieved significantly higher classification accuracy than the ipsilateral paradigm( LFLC). However, both paradigms achieved classification accuracies higher than 70% after the completion of several trials on average,which was usually regarded as the minimum accuracy level required for BCI system to be deemed useful.

33% Classification Accuracy Improvement in a Motor Imagery Brain Computer Interface

A right-hand motor imagery based brain-computer interface is proposed in this work. Such a system requires the identification of different brain states and their classification. Brain signals recorded by electroencephalography are naturally contaminated by various noises and interferences. Ocular artifact removal is performed by implementing an auto-matic method “Kmeans-ICA” which does not require a reference channel. This method starts by decomposing EEG signals into Independent Components;artefactual ones are then identified using Kmeans clustering, a non-supervised machine learning technique. After signal preprocessing, a Brain computer interface system is implemented;physiologically interpretable features extracting the wavelet-coherence, the wavelet-phase locking value and band power are computed and introduced into a statistical test to check for a significant difference between relaxed and motor imagery states. Features which pass the test are conserved and used for classification. Leave One Out Cross Validation is performed to evaluate the performance of the classifier. Two types of classifiers are compared: a Linear Discriminant Analysis and a Support Vector Machine. Using a Linear Discriminant Analysis, classification accuracy improved from 66% to 88.10% after ocular artifacts removal using Kmeans-ICA. The proposed methodology outperformed state of art feature extraction methods, namely, the mu rhythm band power.

An efficient approach of EEG feature extraction and classification for brain computer interface

In the study of brain-computer interfaces,a method of feature extraction and classification used fortwo kinds of imaginations is proposed.It considers Euclidean distance between mean traces recorded fromthe channels with two kinds of imaginations as a feature,and determines imagination classes using thresh-old value.It analyzed the background of experiment and theoretical foundation referring to the data sets ofBCI 2003,and compared the classification precision with the best result of the competition.The resultshows that the method has a high precision and is advantageous for being applied to practical systems.

Common Spatial Pattern Ensemble Classifier and Its Application in Brain-Computer Interface

Abstract-Common spatial pattern （CSP） algorithm is a successful tool in feature estimate of brain-computer interface （BCI）. However, CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the covariance matrices of trials. In this paper, we propose a simple yet effective approach, named common spatial pattern ensemble （CSPE） classifier, to improve CSP performance. Through division of recording channels, multiple CSP filters are constructed. By projection, log-operation, and subtraction on the original signal, an ensemble classifier, majority voting, is achieved and outlier contaminations are alleviated. Experiment results demonstrate that the proposed CSPE classifier is robust to various artifacts and can achieve an average accuracy of 83.02%.

Real-Time Detection of Human Drowsiness via a Portable Brain-Computer Interface

In this paper, we proposed a new concept: depth of drowsiness, which can more precisely describe the drowsiness than existing binary description. A set of effective markers for drowsiness: normalized band norm was successfully developed. These markers are invariant from voltage amplitude of brain waves, eliminating the need for calibrating the voltage output of the brain-computer interface devices. A new polling algorithm was designed and implemented for computing the depth of drowsiness. The time cost of data acquisition and processing for each estimate is about one second, which is well suited for real-time applications. Test results with a portable brain-computer interface device show that the depth of drowsiness computed by the method in this paper is generally invariant from ages of test subjects and sensor channels (P3 and C4). The comparison between experiment and computing results indicate that the new method is noticeably better than one of the recent methods in terms of accuracy for predicting the drowsiness.

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.

Real-Time Brain-Computer Interface System Based on Motor Imagery

Abstract-A brain-computer interface （BCI） real- time system based on motor imagery translates the user＇s motor intention into a real-time control signal for peripheral equipments. A key problem to be solved for practical applications is real-time data collection and processing. In this paper, a real-time BCI system is implemented on computer with electroencephalogram amplifier. In our implementation, the on-line voting method is adopted for feedback control strategy, and the voting results are used to control the cursor horizontal movement. Three subjects take part in the experiment. The results indicate that the best accuracy is 90%.

Neurological rehabilitation of stroke patients via motor imaginary-based brain-computer interface technology

The present study utilized motor imaginary-based brain-computer interface technology combined with rehabilitation training in 20 stroke patients. Results from the Berg Balance Scale and the Holden Walking Classification were significantly greater at 4 weeks after treatment （P 〈 0.01）, which suggested that motor imaginary-based brain-computer interface technology improved balance and walking in stroke patients.

Probabilistic Methods in Multi-Class Brain-Computer Interface

Abstract-Two probabilistic methods are extended to research multi-class motor imagery of brain-computer interface （BCI）： support vector machine （SVM） with posteriori probability （PSVM） and Bayesian linear discriminant analysis with probabilistic output （PBLDA）. A comparative evaluation of these two methods is conducted. The results shows that： 1） probabilistie information can improve the performance of BCI for subjects with high kappa coefficient, and 2） PSVM usually results in a stable kappa coefficient whereas PBLDA is more efficient in estimating the model parameters.

脑机接口(BCI)在国际中文汉字教学中的应用路径

人工智能的快速发展正在深刻改变着我们生活的方方面面,教育领域也不例外。在这个信息时代,如何运用人工智能技术提升教学质量成为一个重要课题。国际中文教育的数字化转型也必须紧跟科技发展步伐。随着人机协同视域下的教学改革的兴起,如何将传统教育与智能技术相融合,不断拓展创新是当今教育发展的一个重要方向。目前国际中文教育中的汉字教学依然是个重难点,文章旨在探讨国际中文汉字教学中遇到的问题,并将借助人工智能及虚拟现实(VR)和增强现实(AR)设备或应用程序,解决学生在学习汉字的音、形、义以及词汇理解的问题。

Possibility to Realize the Brain-Computer Interface from the Quantum Brain Model Based on Superluminal Particles

R. Penrose and S. Hameroff have proposed an idea that the brain can attain high efficient quantum computation by functioning of microtubular structure of neurons in the cytoskelton of biological cells, including neurons of the brain. But Tegmark estimated the duration of coherence of a quantum state in a warm wet brain to be on the order of 10>–13

Transfer Learning in Motor Imagery Brain Computer Interface: A Review

Transfer learning,as a new machine learning methodology,may solve problems in related but different domains by using existing knowledge,and it is often applied to transfer training data from another domain for model training in the case of insuficient training data.In recent years,an increasing number of researchers who engage in brain-computer interface(BCI),have focused on using transfer learning to make most of the available electroencephalogram data from different subjects,effectively reducing the cost of expensive data acquisition and labeling as well as greatly improving the learning performance of the model.This paper surveys the development of transfer learning and reviews the transfer learning approaches in BCI.In addition,according to the"what to transfer"question in transfer learning,this review is organized into three contexts:instance-based transfer learning,parameter-based transfer learning,and feature-based transfer learning.Furthermore,the current transfer learning applications in BCI research are summarized in terms of the transfer learning methods,datasets,evaluation performance,etc.At the end of the paper,the questions to be solved in future research are put forward,laying the foundation for the popularization and in-depth research of transfer learning in BCI.