Listen to the Brain in Real Time-The Chengdu Brainwave Music

Electroencephalogram (EEG) provides a window for the activity of the human brain. As a novel form of the brain-computer interface (BCI), the online/offline EEG data may be interpreted through its auditory representation which can be considered as a specific tool in EEG monitoring and analysis. In this work, after a comprehensive comparison of the various designs of brainwave music generations, a waveform event mapping system for music display in real time-the Chengdu Brainwave Music (CBM) is proposed, which is a special on-line BCI system. In CBM, the user datagram protocol (UDP) is adopted to transport EEG data from the recorder to a music generator. The CBM could possibly be used as an audio feedback tool in BCI, or a monitoring tool in clinic EEG, and a subject specified music therapy method.     

Development of wireless brain computer interface with embedded multitask scheduling and its application on real-time driver's drowsiness detection and warning

Biomedical signal monitoring systems have been rapidly advanced with electronic and information technologies in recent years. However, most of the existing physiological signal monitoring systems can only record the signals without the capability of automatic analysis. In this paper, we proposed a novel brain-computer interface (BCI) system that can acquire and analyze electroencephalogram (EEG) signals in real-time to monitor human physiological as well as cognitive states, and, in turn, provide warning signals to the users when needed. The BCI system consists of a four-channel biosignal acquisition/amplification module, a wireless transmission module, a dual-core signal processing unit, and a host system for display and storage. The embedded dual-core processing system with multitask scheduling capability was proposed to acquire and process the input EEG signals in real time. In addition, the wireless transmission module, which eliminates the inconvenience of wiring, can be switched between radio frequency (RF) and Bluetooth according to the transmission distance. Finally, the real-time EEG-based drowsiness monitoring and warning algorithms were implemented and integrated into the system to close the loop of the BCI system. The practical online testing demonstrates the feasibility of using the proposed system with the ability of real-time processing, automatic analysis, and online warning feedback in real-world operation and living environments.     

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.     

Increased Arousal Levels and Decreased Sleep by Brain Music in Rats

More and more studies have been reported on whether music and other types of auditory stimulation would improve the quality of sleep. Many of these studies have found significant results, but others argue that music is not significantly better than the tones or control conditions in improving sleep. For further understanding the relationship between music and sleep or music and arousal, the present study therefore examines the effects of brain music on sleep and arousal by means of biofeedback. The music is from the transformation of rapid eye movement （REM） sleep electroencephalogram （EEG） of rats using an algorithm in the Chengdu Brain Music （CBM） system. When the brain music was played back to rats, EEG data were recorded to assess the efficacy of music to induce or improve sleep, or increase arousal levels by sleep staging, etc. Our results demonstrate that exposure to the brain music increases arousal levels and decreases sleep in rats, and the underlying mechanism of decreased non-rapid eye movement （NREM） and REM sleep may be different.     

Increased Arousal Levels and Decreased Sleep by Brain Music in Rats

More and more studies have been reported on whether music and other types of auditory stimulation would improve the quality of sleep. Many of these studies have found significant results, but others argue that music is not significantly better than the tones or control conditions in improving sleep. For further understanding the relationship between music and sleep or music and arousal, the present study therefore examines the effects of brain music on sleep and arousal by means of biofeedback. The music is from the transformation of rapid eye movement (REM) sleep electroencephalogram (EEG) of rats using an algorithm in the Chengdu Brain Music (CBM) system. When the brain music was played back to rats, EEG data were recorded to assess the efficacy of music to induce or improve sleep, or increase arousal levels by sleep staging, etc. Our results demonstrate that exposure to the brain music increases arousal levels and decreases sleep in rats, and the underlying mechanism of decreased non-rapid eye movement (NREM) and REM sleep may be different.     

Transfer Learning of BCI Using CUR Algorithm

The brain computer interface (BCI) are used in many applications including medical, environment, education, economy, and social fields. In order to have a high performing BCI classification, the training set must contain variations of high quality subjects which are discriminative. Variations will also drive transferability of training data for generalization purposes. However, if the test subject is unique from the training set variations, BCI performance may suffer. Previously, this problem was solved by introducing transfer learning in the context of spatial filtering on small training set by creating high quality variations within training subjects. In this study however, it was discovered that transfer learning can also be used to compress the training data into an optimal compact size while improving training data performance. The transfer learning framework proposed was on motor imagery BCI-EEG using CUR matrix decomposition algorithm which decomposes data into two components; C and UR which is each subject’s EEG signal and common matrix derived from historical EEG data, respectively. The method is considered transfer learning process because it utilizes historical data as common matrix for the classification purposes. This framework is implemented in the BCI system along with Common Spatial Pattern (CSP) as features extractor and Extreme Learning Machine (ELM) as classifier and this combination exhibits an increase of accuracy to up to 26% with 83% training database compression.

     

Single-Trial EEG Source Reconstruction for Brain–Computer Interface

A new way to improve the classification rate of an EEG-based brain-computer interface (BCI) could be to reconstruct the brain sources of EEG and to apply BCI methods to these derived sources instead of raw measured electrode potentials. EEG source reconstruction methods are based on electrophysiological information that could improve the discrimination between BCI tasks. In this paper, we present an EEG source reconstruction method for BCI. The results are compared with results from raw electrode potentials to enable direct evaluation of the method. Features are based on frequency power change and Bereitschaft potential. The features are ranked with mutual information before being fed to a proximal support vector machine. The dataset IV of the BCI competition II and data from four subjects serve as test data. Results show that the EEG inverse solution improves the classification rate and can lead to results comparable to the best currently known methods.     

Single-trial EEG source reconstruction for brain-computer interface.

A new way to improve the classification rate of an EEG-based brain-computer interface (BCI) could be to reconstruct the brain sources of EEG and to apply BCI methods to these derived sources instead of raw measured electrode potentials. EEG source reconstruction methods are based on electrophysiological information that could improve the discrimination between BCI tasks. In this paper, we present an EEG source reconstruction method for BCI. The results are compared with results from raw electrode potentials to enable direct evaluation of the method. Features are based on frequency power change and Bereitschaft potential. The features are ranked with mutual information before being fed to a proximal support vector machine. The dataset IV of the BCI competition II and data from four subjects serve as test data. Results show that the EEG inverse solution improves the classification rate and can lead to results comparable to the best currently known methods.     

Combining spatial filters for the classification of single-trial EEG in a finger movement task

Brain-computer interface (BCI) is to provide a communication channel that translates human intention reflected by a brain signal such as electroencephalogram (EEG) into a control signal for an output device. In recent years, the event-related desynchronization (ERD) and movement-related potentials (MRPs) are utilized as important features in motor related BCI system, and the common spatial patterns (CSP) algorithm has shown to be very useful for ERD-based classification. However, as MRPs are slow nonoscillatory EEG potential shifts, CSP is not an appropriate approach for MRPs-based classification. Here, another spatial filtering algorithm, discriminative spatial patterns (DSP), is newly introduced for better extraction of the difference in the amplitudes of MRPs, and it is integrated with CSP to extract the features from the EEG signals recorded during voluntary left versus right finger movement tasks. A support vector machines (SVM) based framework is designed as the classifier for the features. The results show that, for MRPs and ERD features, the combined spatial filters can realize the single-trial EEG classification better than anyone of DSP and CSP alone does. Thus, we propose an EEG-based BCI system with the two feature sets, one based on CSP (ERD) and the other based on DSP (MRPs), classified by SVM.     

Chernof f加权分类器框架在运动想象脑-机接口中的应用

针对现有脑机接口(BCI)分类器与大脑认知过程结合不够紧密的问题,该文提出一种基于Chernoff加权的分类器集成框架方法,并用于同步运动想象脑机接口中。通过对训练数据进行统计分析,获得各时刻脑电信号(EEG)的统计特性,并建立基于大脑认知过程的高斯概率模型。然后利用Chernoff边界特性得到该概率模型的最小误差,并以此确定该时刻分类器的权重,通过对各时刻分类器的加权,实现同步脑机接口的信号分类。以脑机接口竞赛数据作为测试,并与线性判决分析、支持向量机和极限学习方法分别结合构成新的集成方法。由实验结果可知,加权集成框架方法的分类性能比原独立分类方法有显著提高。     

Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms

Noninvasive electroencephalogram (EEG) recordings provide for easy and safe access to human neocortical processes which can be exploited for a brain-computer interface (BCI). At present, however, the use of BCIs is severely limited by low bit-transfer rates. We systematically analyze and develop two recent concepts, both capable of enhancing the information gain from multichannel scalp EEG recordings: 1) the combination of classifiers, each specifically tailored for different physiological phenomena, e.g., slow cortical potential shifts, such as the pre-movement Bereitschaftspotential or differences in spatio-spectral distributions of brain activity (i.e., focal event-related desynchronizations) and 2) behavioral paradigms inducing the subjects to generate one out of several brain states (multiclass approach) which all bare a distinctive spatio-temporal signature well discriminable in the standard scalp EEG. We derive information-theoretic predictions and demonstrate their relevance in experimental data. We will show that a suitably arranged interaction between these concepts can significantly boost BCI performances.     

Design of electrode layout for motor imagery based brain?computer interface

A simple electroencephalogram (EEG) electrode layout is proposed to implement a motor imagery based brain-computer interface (BCI). The design was derived from investigation of EEG synchronisation in the motor cortex. A significant improvement in BCI performance was obtained in the new system     

BCI Competition 2003--Data set IIa: spatial patterns of self-controlled brain rhythm modulations

A brain-computer interface (BCI) is a system that should in its ultimate form translate a subject's intent into a technical control signal without resorting to the classical neuromuscular communication channels. By using that signal to, e.g., control a wheelchair or a neuroprosthesis, a BCI could become a valuable tool for paralyzed patients. One approach to implement a BCI is to let users learn to self-control the amplitude of some of their brain rhythms as extracted from multichannel electroencephalogram. We present a method that estimates subject-specific spatial filters which allow for a robust extraction of the rhythm modulations. The effectiveness of the method was proved by achieving the minimum prediction error on data set IIa in the BCI Competition 2003, which consisted of data from three subjects recorded in ten sessions.     

基于相关性和稀疏表示的运动想象脑电通道选择方法

在基于运动想象(MI)的脑机接口(BCI)中,通常采用较多通道的脑电信号(EEG)来提高分类精度,但其中会有包含与MI任务无关或冗余信息的通道,从而影响BCI的性能提升。该文针对运动想象脑电分类中的通道选择问题,提出一种采用相关性和稀疏表示对通道进行选择的方法(CSR-CS)。首先计算训练样本每个通道的皮尔逊相关系数来选择显著通道,然后提取显著通道所在区域的滤波器组共空间模式特征拼接成字典,利用由字典所得到的非零稀疏系数的个数表征每个区域的分类能力,选出显著区域所包含的显著通道作为最优通道,最后采用共空间模式和支持向量机分别进行特征提取与分类。在对BCI第3次竞赛数据集IVa和BCI第4次竞赛数据集I两个二分类MI任务的分类实验中,平均分类精度达到了88.61%和83.9%,表明所提通道选择方法的有效性和鲁棒性。     

基于深度时空特征融合的多通道运动想象EEG解码方法

脑电(EEG)是一种在临床上广泛应用的脑信息记录形式,其反映了脑活动中神经细胞放电产生的电场变化情况。脑电广泛应用于脑-机接口(BCI)系统。然而,研究表明脑电信息空间分辨率较低,这种缺陷可以综合分析多通道电极的脑电数据来弥补。为了从多通道数据中高效地获取到与运动想象任务相关的辨识特征,该文提出一种针对多通道脑电信息的卷积神经网络(MC-CNN)解码方法,先对预先选取好的多通道数据预处理后送入2维卷积神经网络(CNN)进行时间-空间特征提取,然后利用自动编码(AE)器把这些特征映射为具有辨识度的特征子空间,最后指导识别网络进行分类识别。实验结果表明,该文所提多通道空间特征提取和构建方法在运动想象脑电任务识别性能和效率上都具有较大优势。     

Semi-Supervised Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) is a popular method for low-rank approximation of nonnegative matrix, providing a useful tool for representation learning that is valuable for clustering and classification. When a portion of data are labeled, the performance of clustering or classification is improved if the information on class labels is incorporated into NMF. To this end, we present semi-supervised NMF (SSNMF), where we jointly incorporate the data matrix and the (partial) class label matrix into NMF. We develop multiplicative updates for SSNMF to minimize a sum of weighted residuals, each of which involves the nonnegative 2-factor decomposition of the data matrix or the label matrix, sharing a common factor matrix. Experiments on document datasets and EEG datasets in BCI competition confirm that our method improves clustering as well as classification performance, compared to the standard NMF, stressing that semi-supervised NMF yields semi-supervised feature extraction.     

A comparison of common spatial patterns with complex band power features in a four-class BCI experiment

We report on the offline analysis of four-class brain-computer interface (BCI) data recordings. Although the analysis is done within defined time windows (cue-based BCI), our goal is to work toward an approach which classifies on-going electroencephalogram (EEG) signals without the use of such windows (un-cued BCI). To that end, we provide some elements of that analysis related to timing issues that will become important as we pursue this goal in the future. A new set of features called complex band power (CBP) features which make explicit use of phase are introduced and are shown to produce good results. As reference methods we used traditional band power features and the method of common spatial patterns. We consider also for the first time in the context of a four-class problem the issue of variability of the features over time and how much data is required to give good classification results. We do this in a practical way where training data precedes testing data in time.     

Automatic ocular artifact removal from EEG data using a hybrid CAE-RLS approach

Traditional methods for removing ocular artifacts (OAs) from electroencephalography (EEG) signals often involve a large number of EEG electrodes or require electrooculogram (EOG) as the reference, these constraints make subjects uncomfortable during the acquisition process and increase the complexity of brain-computer interfaces (BCI). To address these limitations, a method combining a convolutional autoencoder (CAE) and a recursive least squares (RLS) adaptive filter is proposed. The proposed method consists of offline and online stages. In the offline stage, the peak and local mean of the four-channel EOG signals are automatically extracted to obtain the CAE model. Once the model is trained, the EOG channels are no longer needed. In the online stage, by using the CAE model to identify the OAs from a single-channel raw EEG signal, the identified OAs and the given raw EEG signal are used as the reference and input for an RLS adaptive filter. Experiments show that the root mean square error (RMSE) of the CAE-RLS algorithm and independent component analysis (ICA) are 1.253 3 and 1.254 6 respectively, and the power spectral density (PSD) curve for the CAE-RLS is similar to the original EEG signal. These experimental results indicate that by using only a couple of EEG channels, the proposed method can effectively remove OAs without parallel EOG records and accurately reconstruct the EEG signal. In addition, the processing time of the CAE-RLS is shorter than that of ICA, so the CAE-RLS algorithm is very suitable for BCI system.     

BCI Competition 2003--Data set Ia: combining gamma-band power with slow cortical potentials to improve single-trial classification of electroencephalographic signals

In one type of brain-computer interface (BCI), users self-modulate brain activity as detected by electroencephalography (EEG). To infer user intent, EEG signals are classified by algorithms which typically use only one of the several types of information available in these signals. One such BCI uses slow cortical potential (SCP) measures to classify single trials. We complemented these measures with estimates of high-frequency (gamma-band) activity, which has been associated with attentional and intentional states. Using a simple linear classifier, we obtained significantly greater classification accuracy using both types of information from the same recording epochs compared to using SCPs alone.