Comparison of linear, nonlinear, and feature selection methods for EEG signal classification

The reliable operation of brain-computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires accurate classification of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. The high-dimensional and noisy nature of EEG may limit the advantage of nonlinear classification methods over linear ones. This paper reports the results of a linear (linear discriminant analysis) and two nonlinear classifiers (neural networks and support vector machines) applied to the classification of spontaneous EEG during five mental tasks, showing that nonlinear classifiers produce only slightly better classification results. An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement.     

Unsupervised adaptation of electroencephalogram signal processing based on fuzzy C‐means algorithm

This paper studies an unsupervised approach for online adaptation of electroencephalogram (EEG) based brain–computer interface (BCI). The approach is based on the fuzzy C‐means (FCM) algorithm. It can be used to improve the adaptability of BCIs to the change in brain states by online updating the linear discriminant analysis classifier. In order to evaluate the performance of the proposed approach, we applied it to a set of simulation data and compared with other unsupervised adaptation algorithms. The results show that the FCM‐based algorithm can achieve a desirable capability in adapting to changes and discovering class information from unlabeled data. The algorithm has also been tested by the real EEG data recorded in experiments in our laboratory and the data from other sources (set IIb of the BCI Competition IV). The results of real data are consistent with that of simulation data. Copyright © 2011 John Wiley & Sons, Ltd.     

Brain-Computer Interfaces Based on Visual Evoked Potentials

Recently, electroencephalogram (EEG)-based brain? computer interfaces (BCIs) have become a hot spot in the study of neural engineering, rehabilitation, and brain science. In this article, we review BCI systems based on visual evoked potentials (VEPs). Although the performance of this type of BCI has already been evaluated by many research groups through a variety of laboratory demonstrations, researchers are still facing many difficulties in changing the demonstrations to practically applicable systems. On the basis of the literature, we describe the challenges in developing practical BCI systems. Also, our recent work in the designs and implementations of the BCI systems based on steady-state VEPs (SSVEPs) is described in detail. The results show that by adequately considering the problems encountered in system design, signal processing, and parameter optimization, SSVEPs can provide the most useful information about brain activities using the least number of electrodes. At the same time, system cost could be greatly decreased and usability could be readily improved, thus benefiting the implementation of a practical BCI.     

A parametric feature extraction and classification strategy for brain-computer interfacing.

Parametric modeling strategies are explored in conjunction with linear discriminant analysis for use in an electroencephalogram (EEG)-based brain-computer interface (BCI). A left/right self-paced typing exercise is analyzed by extending the usual autoregressive (AR) model for EEG feature extraction with an AR with exogenous input (ARX) model for combined filtering and feature extraction. The ensemble averaged Bereitschafts potential (an event related potential preceding the onset of movement) forms the exogenous signal input to the ARX model. Based on trials with six subjects, the ARX case of modeling both the signal and noise was found to be considerably more effective than modeling the noise alone (common in BCI systems) with the AR method yielding a classification accuracy of 52.8+/-4.8% and the ARX method an accuracy of 79.1+/-3.9 % across subjects. The results suggest a role for ARX-based feature extraction in BCIs based on evoked and event-related potentials.     

An improved P300-based brain-computer interface.

A brain-computer interface (BCI) is a system for direct communication between brain and computer. The BCI developed in this work is based on a BCI described by Farwell and Donchin in 1988, which allows a subject to communicate one of 36 symbols presented on a 6 x 6 matrix. The system exploits the P300 component of event-related brain potentials (ERP) as a medium for communication. The processing methods distinguish this work from Donchin's work. In this work, independent component analysis (ICA) was used to separate the P300 source from the background noise. A matched filter was used together with averaging and threshold techniques for detecting the existence of P300s. The processing method was evaluated offline on data recorded from six healthy subjects. The method achieved a communication rate of 5.45 symbols/min with an accuracy of 92.1% compared to 4.8 symbols/min with an accuracy of 90% in Donchin's work. The online interface was tested with the same six subjects. The average communication rate achieved was 4.5 symbols/min with an accuracy of 79.5 % as apposed to the 4.8 symbols/min with an accuracy of 56 % in Donchin's work. The presented BCI achieves excellent performance compared to other existing BCIs, and allows a reasonable communication rate, while maintaining a low error rate.     

The use of EEG modifications due to motor imagery for brain-computer interfaces

The opening of a communication channel between brain and computer [brain-computer interface (BCI)] is possible by using changes in electroencephalogram (EEG) power spectra related to the imagination of movements. In this paper, we present results obtained by recording EEG during an upper limb motor imagery task in a total of 18 subjects by using low-resolution surface Laplacian, different linear and quadratic classifiers, as well as a variable number of scalp electrodes, from 2 to 26. The results (variable correct classification rate of mental imagery between 75% and 95%) suggest that it is possible to recognize quite reliably ongoing mental movement imagery for BCI applications.     

Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis

Brain-computer interfaces (BCIs) involve two coupled adapting systems-the human subject and the computer. In developing our BCI, our goal was to minimize the need for subject training and to impose the major learning load on the computer. To this end, we use behavioral paradigms that exploit single-trial EEG potentials preceding voluntary finger movements. Here, we report recent results on the basic physiology of such premovement event-related potentials (ERP). 1) We predict the laterality of imminent left- versus right-hand finger movements in a natural keyboard typing condition and demonstrate that a single-trial classification based on the lateralized Bereitschaftspotential (BP) achieves good accuracies even at a pace as fast as 2 taps/s. Results for four out of eight subjects reached a peak information transfer rate of more than 15 b/min; the four other subjects reached 6-10 b/min. 2) We detect cerebral error potentials from single false-response trials in a forced-choice task, reflecting the subject's recognition of an erroneous response. Based on a specifically tailored classification procedure that limits the rate of false positives at, e.g., 2%, the algorithm manages to detect 85% of error trials in seven out of eight subjects. Thus, concatenating a primary single-trial BP-paradigm involving finger classification feedback with such secondary error detection could serve as an efficient online confirmation/correction tool for improvement of bit rates in a future BCI setting. As the present variant of the Berlin BCI is designed to achieve fast classifications in normally behaving subjects, it opens a new perspective for assistance of action control in time-critical behavioral contexts; the potential transfer to paralyzed patients will require further study.     

Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain-computer interface

Although brain-computer interface (BCI) techniques have been developing quickly in recent decades, there still exist a number of unsolved problems, such as improvement of motor imagery (MI) signal classification. In this paper, we propose a hybrid algorithm to improve the classification success rate of MI-based electroencephalogram (EEG) signals in BCIs. The proposed scheme develops a novel cross-correlation based feature extractor, which is aided with a least square support vector machine (LS-SVM) for two-class MI signals recognition. To verify the effectiveness of the proposed classifier, we replace the LS-SVM classifier by a logistic regression classifier and a kernel logistic regression classifier, separately, with the same features extracted from the cross-correlation technique for the classification. The proposed approach is tested on datasets, IVa and IVb of BCI Competition III. The performances of those methods are evaluated with classification accuracy through a 10-fold cross-validation procedure. We also assess the performance of the proposed method by comparing it with eight recently reported algorithms. Experimental results on the two datasets show that the proposed LS-SVM classifier provides an improvement compared to the logistic regression and kernel logistic regression classifiers. The results also indicate that the proposed approach outperforms the most recently reported eight methods and achieves a 7.40% improvement over the best results of the other eight studies.     

Cortical Imaging of Event-Related (de)Synchronization During Online Control of Brain-Computer Interface Using Minimum-Norm Estimates in Frequency Domain

It is of wide interest to study the brain activity that correlates to the control of brain–computer interface (BCI). In the present study, we have developed an approach to image the cortical rhythmic modulation associated with motor imagery using minimum-norm estimates in the frequency domain (MNEFD). The distribution of cortical sources of mu activity during online control of BCI was obtained with the MNEFD. Contralateral decrease (event-related desynchronization) and ipsilateral increase (event-related synchronization) are localized in the sensorimotor cortex during online control of BCI in a group of human subjects. Statistical source analysis revealed that maximum correlation with movement imagination is localized in sensorimotor cortex.      

BCI Meeting 2005--workshop on clinical issues and applications.

This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant to brain-computer interface (BCI) clinical applications. These include potential BCI users, applications, validation, getting BCIs to users, role of government and industry, plasticity, and ethics.     

基于TRCSP和L2范数的脑电通道选择方法

脑-机接口（BCI）系统常用高密度电极通道来获取较高空间分辨率的脑电（EEG）信号，但同时也会引入过多的噪声通道，影响脑电的解码性能。为了消除无关的噪声通道，提出了一种基于Tikhonov正则化共空间模式（TRCSP）和L2范数的运动想象脑电通道选择方法。首先基于TRCSP和分类器得到最优的空间滤波器，接着基于L2范数对空间滤波器得到的各通道的权重值进行排序。选择前K个通道的数据进行CSP特征提取，根据分类器的分类准确率确定最优K值，进而得到最优的通道数和通道组合。在实验中，使用6种分类器分别在BCI竞赛III（2005）数据集IVa和实验室自采集数据上验证所提出的通道选择方法的有效性。所提出的方法在两个数据集上的平均分类准确率分别达到了87.57%和74.32%，优于其它现有的方法。     

Enhancement of steady‐state visual evoked potential‐based brain–computer interface systems via a steady‐state motion visual stimulus modality

Steady‐state visual evoked potential (SSVEP)‐based brain–computer interface (BCI) systems are among the most accurate assistive devices for patients with severe disabilities. However, existing visual stimulation patterns lead to eye fatigue, which affects the system performance. Therefore, in this study, we propose two improvements to SSVEP‐based BCI systems. First, we propose a novel visual stimulator that incorporates a visual motion stimulus for the steady‐state visual stimulus to reduce eye fatigue while maintaining the advantages associated with SSVEPs. We also propose two corresponding feature extraction algorithms, i.e. SSVEP detection and visual attention detection, to capture the phenomena of steady‐state motion visual stimulus responses. The accuracy of the test was ∼83.6%. Second, we propose a novel hybrid BCI‐SSVEP system and a motion visual stimulus hybrid BCI system to enhance the SSVEP‐based BCI system during a state of eye fatigue. Participants used the SSVEP system until reaching a fatigued state and then began using a hybrid motion visual stimulus. The accuracy of the proposed system was ∼85.6%. The proposed improvements can be incorporated into practical BCI systems for wheelchair control. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Utilizing gamma band to improve mental task based brain-computer interface design.

A common method for designing brain-computer Interface (BCI) is to use electroencephalogram (EEG) signals extracted during mental tasks. In these BCI designs, features from EEG such as power and asymmetry ratios from delta, theta, alpha, and beta bands have been used in classifying different mental tasks. In this paper, the performance of the mental task based BCI design is improved by using spectral power and asymmetry ratios from gamma (24-37 Hz) band in addition to the lower frequency bands. In the experimental study, EEG signals extracted during five mental tasks from four subjects were used. Elman neural network (ENN) trained by the resilient backpropagation algorithm was used to classify the power and asymmetry ratios from EEG into different combinations of two mental tasks. The results indicated that ((1) the classification performance and training time of the BCI design were improved through the use of additional gamma band features; (2) classification performances were nearly invariant to the number of ENN hidden units or feature extraction method.     

An enhanced time-frequency-spatial approach for motor imagery classification.

Human motor imagery (MI) tasks evoke electroencephalogram (EEG) signal changes. The features of these changes appear as subject-specific temporal traces of EEG rhythmic components at specific channels located over the scalp. Accurate classification of MI tasks based upon EEG may lead to a noninvasive brain-computer interface (BCI) to decode and convey intention of human subjects. We have previously proposed two novel methods on time-frequency feature extraction, expression and classification for high-density EEG recordings (Wang and He 2004; Wang, Deng, and He, 2004). In the present study, we refined the above time-frequency-spatial approach and applied it to a one-dimensional "cursor control" BCI experiment with online feedback. Through offline analysis of the collected data, we evaluated the capability of the present refined method in comparison with the original time-frequency-spatial methods. The enhanced performance in terms of classification accuracy was found for the proposed approach, with a mean accuracy rate of 91.1% for two subjects studied.     

基于视听交互刺激的认知机理与脑机接口范式研究进展

与人类视听觉感知密切相关的图像、语音和文本(语言)信息在社会经济发展与国家安全保障等领域中扮演着重要角色,脑机接口(BCI)是无需外围神经肌肉便可操控外部设备、使"思想"直接变成"行动"的创新技术,基于视、听觉刺激的BCI系统具有极其广阔应用前景。目前认知心理学研究普遍认为视听交互刺激模式可产生比视或听单模态刺激更强的事件相关电位信号,因而可使识别大脑思维模式的正确率更高和响应速度更快,然而BCI研究至今尚难有充分验证其优势的成果。分别从认知心理机制和BCI实验范式两方面综述了视听交互刺激的信息整合机制、视听刺激之间的协同与竞争关系、匹配度对刺激效果的影响及相关BCI实验范式的研究现状;深入分析了视听交互刺激范式未在现有BCI系统中体现其优越性的原因;最后从BCI实验范式选择、系统硬件性能改进、脑电信号处理等方面提出了改进思路并展望了基于视听交互刺激BCI范式研究未来动向。     

Current trends in Graz Brain-Computer Interface (BCI) research.

This paper describes a research approach to develop a brain-computer interface (BCI) based on recognition of subject-specific EEG patterns. EEG signals recorded from sensorimotor areas during mental imagination of specific movements are classified on-line and used e.g. for cursor control. In a number of on-line experiments, various methods for EEG feature extraction and classification have been evaluated.     

基于皮层慢电位的自发脑电实验设计与研究

针对基于自发脑电信号的脑机接口研究,设计了一种科学的且易实现的运动想象实验范例,利用运动想象脑电作为BCI的控制信号.该实验方案能有效地获得可识别的、具有特征性的自发脑电,满足脑机接口实验要求,为BCI的研究提供了一种更加自然、更加实用的控制方式.     

Brain-computer interaction research at the Computer Vision and Multimedia Laboratory, University of Geneva.

This paper describes the work being conducted in the domain of brain-computer interaction (BCI) at the Multimodal Interaction Group, Computer Vision and Multimedia Laboratory, University of Geneva, Geneva, Switzerland. The application focus of this work is on multimodal interaction rather than on rehabilitation, that is how to augment classical interaction by means of physiological measurements. Three main research topics are addressed. The first one concerns the more general problem of brain source activity recognition from EEGs. In contrast with classical deterministic approaches, we studied iterative robust stochastic based reconstruction procedures modeling source and noise statistics, to overcome known limitations of current techniques. We also developed procedures for optimal electroencephalogram (EEG) sensor system design in terms of placement and number of electrodes. The second topic is the study of BCI protocols and performance from an information-theoretic point of view. Various information rate measurements have been compared for assessing BCI abilities. The third research topic concerns the use of EEG and other physiological signals for assessing a user's emotional status.     

Multi‐command SSAEP‐based BCI system with training sessions for SSVEP during an eye fatigue state

This paper proposes a steady‐state auditory stimulus modality and a detection algorithm to replace steady‐state visual evoked potential (SSVEP )‐based brain–computer interface (BCI ) systems during visual fatigue periods. The optimal speaker position for the steady‐state auditory evoked potential (SSAEP )‐based BCI system and possible electrode positions are investigated. Using the proposed system, an accuracy of 85% for two commands was achieved based on the T3–T5 and T4–T6 electrode positions using only one speaker. SSAEP is a promising BCI modality for mitigating the problem of eye fatigue that often occurs during the use of SSVEP ‐based BCI systems. However, SSAEP ‐based BCI systems suffer from low accuracy. To increase accuracy, we propose a new enhanced SSAEP training method. The training process was enhanced by instructing users to control their attention levels while simultaneously detecting an auditory stimulus frequency. Furthermore, we propose a corresponding single‐frequency, multi‐command BCI paradigm for the proposed training method. With the proposed paradigm, four commands can be detected using only one auditory stimulus frequency. The proposed training system yielded an accuracy of ∼81% compared to 66% for sessions performed without the proposed training method. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Electroencephalography (EEG)-based brain-computer interface (BCI): a 2-D virtual wheelchair control based on event-related desynchronization/synchronization and state control

This study aims to propose an effective and practical paradigm for a brain-computer interface (BCI)-based 2-D virtual wheelchair control. The paradigm was based on the multi-class discrimination of spatiotemporally distinguishable phenomenon of event-related desynchronization/synchronization (ERD/ERS) in electroencephalogram signals associated with motor execution/imagery of right/left hand movement. Comparing with traditional method using ERD only, where bilateral ERDs appear during left/right hand mental tasks, the 2-D control exhibited high accuracy within a short time, as incorporating ERS into the paradigm hypothetically enhanced the spatiotemoral feature contrast of ERS versus ERD. We also expected users to experience ease of control by including a noncontrol state. In this study, the control command was sent discretely whereas the virtual wheelchair was moving continuously. We tested five healthy subjects in a single visit with two sessions, i.e., motor execution and motor imagery. Each session included a 20 min calibration and two sets of games that were less than 30 min. Average target hit rate was as high as 98.4% with motor imagery. Every subject achieved 100% hit rate in the second set of wheelchair control games. The average time to hit a target 10 m away was about 59 s, with 39 s for the best set. The superior control performance in subjects without intensive BCI training suggested a practical wheelchair control paradigm for BCI users.