An SSVEP-based BCI using high duty-cycle visual flicker

Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have generated significant interest due to their high information transfer rate (ITR). Due to the amplitude-frequency characteristic of the SSVEP, the flickering frequency of an SSVEP-based BCI is typically lower than 20 Hz to achieve a high SNR. However, a visual flicker with a flashing frequency below the critical flicker-fusion frequency often makes subjects feel flicker jerky and causes visual discomfort. This study presents a novel technique using high duty-cycle visual flicker to decrease user's visual discomfort. The proposed design uses LEDs flashing at 13.16 Hz, driven by flickering sequences consisting of repetitive stimulus cycles with a duration T (T = 76 ms). Each stimulus cycle included an ON state with a duration T(ON) and an OFF state with a duration T(OFF) (T = T(ON) + T(OFF)), and the duty cycle, defined as T(ON)/T, varied from 10.5% to 89.5%. This study also includes a questionnaire survey and analyzes the SSVEPs induced by different duty-cycle flickers. An 89.5% duty-cycle flicker, reported as a comfortable flicker, was adopted in a phase-tagged SSVEP system. Six subjects were asked to sequentially input a sequence of cursor commands with the 25.08-bits/min ITR.     

An asynchronous P300 BCI with SSVEP-based control state detection

In this paper, an asynchronous brain-computer interface (BCI) system combining the P300 and steady-state visually evoked potentials (SSVEPs) paradigms is proposed. The information transfer is accomplished using P300 event-related potential paradigm and the control state (CS) detection is achieved using SSVEP, overlaid on the P300 base system. Offline and online experiments have been performed with ten subjects to validate the proposed system. It is shown to achieve fast and accurate CS detection without significantly compromising the performance. In online experiments, the system is found to be capable of achieving an average data transfer rate of 19.05 bits/min, with CS detection accuracy of about 88%.     

面向中高频SSVEP脑机接口的编解码算法研究

基于稳态视觉诱发电位（steady-state visual evoked potentials,SSVEPs）的脑-机接口系统（brain-computer interface,BCI）通常使用低频强闪烁刺激诱发强特征脑电信号。尽管相关数据处理技术日臻成熟,但是系统使用舒适度差,训练时间较长。提高刺激频率能够有效缓解受试者的视觉疲劳,提高系统友好度,然而现有中高频SSVEP系统又存在指令集数量少、信息传输率（information transfer rate,ITR）低等缺陷。针对以上问题,本文基于中高频SSVEP脑电特征,提出并使用了包含空码的Code Words编码范式与集成任务相关成分分析（ensemble task-related component analysis, eTRCA）解码算法,并研究了该套编解码方法的适用性与可扩展性。本研究选择中高频段的4个频率（20、24、30、40 Hz）分别构建脑控字符拼写系统,单个频率的闪烁刺激可独立构建多达6个控制指令,联合多个频率理论上可实现指令集数量的成倍扩增。共有10位健康受试者参与了离线脑电实验,利用18~60 Hz带通滤波对脑电数据进行预处理,使用eTRCA算法进行特征识别。18指令集系统的理论平均分类准确率为96.71±1.69 %,理论平均ITR达86.94±6.07 bits/min。以上结果表明,本研究提出的编解码算法能够有效诱发并准确识别中高频SSVEP的时-频-相多维特征,在此基础上通过增加编码单元频率种类、提高有效编码率、改进解码算法等方式有希望进一步提升系统性能。      

Accounting for phase drifts in SSVEP-based BCIs by means of biphasic stimulation

This study proposes a novel biphasic stimulation technique to solve the issue of phase drifts in steady-state visual evoked potential (SSVEPs) in phase-tagged systems. Phase calibration was embedded in stimulus sequences using a biphasic flicker, which is driven by a sequence with alternating reference and phase-shift states. Nine subjects were recruited to participate in off-line and online tests. Signals were bandpass filtered and segmented by trigger signals into reference and phase-shift epochs. Frequency components of SSVEP in the reference and phase-shift epochs were extracted using the Fourier method with a 50% overlapped sliding window. The real and imaginary parts of the SSVEP frequency components were organized into complex vectors in each epoch. Hotelling's t-square test was used to determine the significances of nonzero mean vectors. The rejection of noisy data segments and the validation of gaze detections were made based on p values. The phase difference between the valid mean vectors of reference and phase-shift epochs was used to identify user's gazed targets in this system. Data showed an average information transfer rate of 44.55 and 38.21 bits/min in off-line and online tests, respectively.     

MSI和CCA算法对稳态视觉诱发电位信号分类的比较研究

脑机接口（Brain Computer Interface, BCI）系统能让那些有运动障碍的病人用脑信号与外界设备交互。稳态视觉诱发电位（Steady State Visual Evoked Potential,SSVEP）具有分析正确率高,不用训练等优点而倍受重视。如何高效地对SSVEP信号频率识别是SSVEP-BCI的关键问题,并关系到BCI的系统优劣。本文采用多变量同步指数与典型相关分析方法对SSVEP信号分类进行比较研究,探讨了两种方法在数据长度、导联数量、导联位置以及参考信号的谐波数量对SSVEP信号分类效果的影响。六位被试者参与实验采集数据,实验结果证实,在时间窗较小,数据长度较少的条件下,多变量同步指数方法较典型相关分析方法性能更优。而对于SSVEP信号分析来说,导联位置的准确性是影响频率分析算法的最根本因素。      

不同刺激光源面积对SSVEP共振频率的影响研究

稳态视觉诱发电位（steady-state visual evoked potentials，SSVEP）的共振频率为诱发最大SSVEP响应对应的刺激频率，对其研究在临床神经科学和脑机接口技术领域均具有很好的应用前景。刺激光源面积是影响SSVEP共振频率的一个要素，但目前对共振频率随光源面积变化规律知之甚少。本文首先进行的理论研究结果表明周期性视觉刺激光源面积变化和SSVEP性能变化密切相关；然后侧重实验研究不同LED光源刺激面积变化对SSVEP共振频率的影响规律:首先采集不同光源面积刺激下的SSVEP信号，对其依次进行50 Hz陷波、带通滤波（带宽为3～35 Hz）去噪、去趋势与眼电等预处理；然后基于快速傅立叶变换进行频谱分析，计算不同刺激频率下的SSVEP平均归一化基波功率，以确定SSVEP的共振频率。结果表明:当光源半径和刺激频率分别在5～9 mm和6～20 Hz取值时，SSVEP共振频率随光源面积变化的规律是:当光源面积小于某阈值时，共振频率与光源面积正相关；而超出这个阈值时，共振频率与光源面积负相关。此外本文用闪光LED作为刺激源，可有效解决以屏幕闪光为刺激源时存在的频率选择受限于屏幕刷新率问题。本文研究结果可为神经系统疾病的预测或诊断和SSVEP在脑机接口领域的有效应用提供有意义的理论和实验依据。      

Low Mutual Coupling of Microstrip Antenna Array Integrated with Dollar Shaped Resonator

This paper describes the effectiveness of feature obtained by power spectrum analysis (PSA) as well as the combined method of empirical mode decomposition (EMD) and PSA for the development of brain–computer interface (BCI) system using steady-state visual evoked potential (SSVEP). Accurate detection of SSVEP response from the recorded EEG signal is a difficult task for a new development of the BCI inference system. The EMD technique is a non-linear method of signal decomposition, which generates several intrinsic mode functions (IMFs) of different flickering frequencies. Prominent IMF signal of SSVEP plays a vital role in the accurate detection of frequency. The proposed method achieves the average detection accuracy of 81.45% over four subjects; in contrast, the conventional method of PSA achieves average detection accuracy of 80.43%. The achieved result indicates that the proposed method out performs state of the art by more than 1.02% over four subjects.

     

The chording glove: a glove-based text input device

This paper introduces a new text input device called the chording glove. The keys of a chord keyboard are mounted on the fingers of a glove. A chord can be made by pressing the fingers against any surface. Shift buttons placed on the index finger enable the glove to enter the full ASCII character set. The chording glove is designed as a text input device for wearable computers and virtual environments. An experiment was conducted to assess the performance of the glove. After an average of 80 min of a tutorial, ten subjects reached a continuous text input speed of 8.9±1.4 words/min, and after 10 1-hr sessions, they achieved 16.8±2.5 words/min     

A Study on SSVEP-Based BCI

Abstract-Brain-computer interface （BCI） can help the deformity person finish some basic activities. In this paper, we concern some critical aspects of SSVEP based BCI, including stimulator selection, method of SSVEP extracting in a short time, stimulating frequency selection, and signal electrode selection. The conclusion is that the stimulator type should be based on the complexity of the BCI system, the method based on wavelet analysis is more valid than the power spectrum method in extracting the SSVEP in a short period, and the selections of stimulating frequency and electrode are important in designing a BCI system. These contents are meaningful for implementing a real SSVEP-based BCI.     

Complex-Valued Spatial Filters for SSVEP-Based BCIs With Phase Coding

Brain-computer interface (BCI) systems based on steady-state visual evoked potentials (SSVEPs) have gained considerable popularity because of the robustness and high information transfer rate these can provide. Typical SSVEP setups make use of visual targets flashing at different frequencies, where a user's choice is determined from the SSVEPs elicited by the user gazing at a specific target. The range of stimulus frequencies available for such setups is limited by a variety of factors, including the strength of the evoked potentials as well as user comfort and safety with light stimuli flashing at those frequencies. One way to tackle this limitation is by introducing targets flickering at the same frequency but with different phases. In this paper, we propose the use of the analytic common spatial patterns (ACSPs) method to discriminate between phase coded SSVEP targets, and we demonstrate that the complex-valued spatial filters used for discrimination can exceed the performance of existing techniques. Furthermore, the ACSP method also yields a set of spatial patterns, separable into amplitude and phase components, that provide insight into the underlying brain activity.