CN103054574B - Frequency identification method on basis of multivariate synchronous indexes - Google Patents

Frequency identification method on basis of multivariate synchronous indexes Download PDF

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CN103054574B
CN103054574B CN 201310003618 CN201310003618A CN103054574B CN 103054574 B CN103054574 B CN 103054574B CN 201310003618 CN201310003618 CN 201310003618 CN 201310003618 A CN201310003618 A CN 201310003618A CN 103054574 B CN103054574 B CN 103054574B
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frequency
reference signal
index
synchronization
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张杨松
徐鹏
尧德中
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电子科技大学
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Abstract

本发明公开了一种基于多变量同步指数的频率识别方法,具体为:根据SSVEP-BCI系统所使用的频率构造出每个频率对应的参考信号;分别计算多导脑电信号与各个参考信号之间的同步指数;找出与脑电信号同步指数最大的参考信号,将其对应的频率作为识别的频率输出。 The present invention discloses a method for identifying a frequency based on multivariate synchronization index, specifically: a configuration corresponding to the reference signal for each frequency according to the frequency SSVEP-BCI system used; calculates multichannel EEG signals with respective reference synchronization between the index; find the maximum of the reference signal and EEG synchronization index, which was identified as corresponding to the frequency of the output frequency. 本发明的方法通过计算脑电信号和基于系统使用的刺激构造的不同参考信号之间的同步指数,根据同步指数的大小,找出与脑电信号同步指数最大的参考信号,将该参考信号的频率作为识别的结果输出。 The method of the present invention by calculating EEG-based synchronization and reference signal indices between different stimuli used by the system configuration, according to the size of the sync index, and find the maximum index EEG synchronization reference signal, the reference signal as a result of the recognition frequency output. 与现有的所使用的主要多导频率检测方法相比,具有更高的准确率;并且在电极导数少,数据长度较短的条件下,具有最优的性能。 Compared with the conventional multi-channel primary frequency detection method used, with a higher accuracy; and in a small number of conductive electrodes, a short data length conditions, with optimal performance.

Description

基于多变量同步指数的频率识别方法 Based on multivariate synchronization frequency identification index method

技术领域 FIELD

[0001]本发明属于生物医学信息技术领域,具体涉及脑-机接口(Brain ComputerInterface, BCI)系统中的频率识别方法。 [0001] The present invention is in the biomedical field of information technology, particularly to the brain - machine interfaces Frequency Identification (Brain ComputerInterface, BCI) system.

背景技术 Background technique

[0002] 脑-机接口能为人或动物与外界环境提供直接的在线通讯通道,由于不依赖于传统的外围神经和肌肉输出通道,因而在神经工程和神经科学中具有重要应用价值。 [0002] brain - computer interface can be a human or animal with the external environment to provide direct online communication channels, because they do not rely on traditional peripheral nerves and muscles output channel, which has important applications in neural engineering and neuroscience.

[0003] 当受到大于4Hz频率恒定的外界视觉刺激时,大脑将会产生和外界刺激频率或其谐波频率相同的响应,即(Regan D (1989) Human brain electrophysiology: evokedpotentials and evoked magnetic fields in science and medicine:Elsevier.X 由于SSVEP是大脑的内源反应,这类信号具有很高的信噪比、很强的稳健性以及较少的训练,使得基于SSVEP的脑-机接口(SSVEP-BCI)有较高的信息传输率,一直是BCI在线系统研究的一个重要方向。 [0003] When subjected to a frequency greater than 4Hz constant external visual stimuli, and the brain to external stimuli will produce the same frequency or a harmonic of the frequency response, i.e. (Regan D (1989) Human brain electrophysiology: evokedpotentials and evoked magnetic fields in science and medicine: Elsevier.X SSVEP Since the reaction is an endogenous brain, such signals having a high signal to noise ratio, is robust and less training, such SSVEP based brain - machine Interface (SSVEP-BCI) have a higher rate of information transfer, has been an important research direction BCI online system.

[0004] SSVEP-BCI系统包括信号采集,信号处理,应用接口等几个主要模块。 [0004] SSVEP-BCI system comprises several major modules signal acquisition, signal processing, application interface. 系统的性能主要取决于信号处理模块的效率。 Performance of the system depends on the efficiency of the signal processing module. 因此,快速准确的信号处理方法是至关重要的。 Therefore, fast and accurate signal processing method is essential. 不同被试的SSVEP的幅度、分布以及可用的刺激频率存在很大的差异。 Different subjects SSVEP amplitude, distribution and very different stimulation frequencies available. 在使用目前的SSVEP-BCI系统时,为了获得较好的性能,必须进行电极选择、数据段长度等参数优化,尤其是在系统使用传统的信号处理方法时,这些优化过程是必须的。 When using the current SSVEP-BCI systems, in order to obtain better performance, the electrode must be selected, the data segment length parameter optimization, especially when the system uses conventional signal processing method, which are necessary optimization process.

[0005] 近些年,提出了一些基于多导信号检测的频率识别方法,这些方法通过从多导脑电信号中组合优化提取出更多有用信息,提高了识别精度,同时减少电极选择等优化过程。 [0005] In recent years, a number of proposed frequency identification signal detection method based on multi-channel, by a combination of these methods to extract from the multichannel EEG optimization more useful information to improve the recognition accuracy, while reducing the electrode selection optimization process. 基于最小能量法的检测方法(Minimum Energy Combination, MEC)和基于典型相关分析的方法(Canonical CorrelationAnalysis,CCA)是现在SSVEP-BCI系统中所使用的两种多导信号检测的频率识别方法。 Based detection methods (Minimum Energy Combination, MEC) Minimum energy method and a method (Canonical CorrelationAnalysis, CCA) is now two methods of multi-channel frequency identification signal detection SSVEP-BCI systems used in the canonical correlation analysis.

[0006] 基于最小能量法的检测方法通过寻找空间滤波器,将原始多导信号进行投影,得到低维组合信号,从而削弱噪声信号以及其他伪迹信号。 [0006] Looking through the spatial filter, the original multi-channel detection methods based on minimum energy projection process a signal, to obtain a combination of low dimensional signals, thus weakening the noise signal and other signal artifact. 该方法能得到高的准确率,不需要预实验数据进行参数优化,已经被成功应用于实际的SSVEP-BCI系统。 This method can obtain a high accuracy, no pre-parameter optimization experimental data, has been successfully applied to the actual system SSVEP-BCI.

[0007] 典型相关分析是一种多变量的统计方法,该方法通过找到一对线性投影向量,使得多导脑电信号和参考信号之间的相关最大。 [0007] The canonical correlation analysis is a multivariable statistical method, the method by finding a pair of linear projection vectors, such that the maximum correlation between the multi-channel EEG signals and reference signals. 该方法比基于最小能量法的检测方法具有更高的准确率和健壮性。 The method has higher accuracy and robustness than the minimum energy detection method.

[0008] 快速和准确性高的算法对实际的SSVEP-BCI系统是非常重要的,是实现高性能系统的核心组成部分。 [0008] fast and highly accurate algorithm to actual SSVEP-BCI system is very important, it is the core component of the high-performance systems. 多导检测算法通过优化组合多导信号,对噪声表现出更大的稳健性,从而改进算法性能;同时这类算法几乎不需要进行参数优化,从而在实际实施过程中带来了更多的方便。 Multi-channel detection algorithm by optimizing the combination of multi-channel signals, the noise exhibits a greater robustness, to improve the performance of the algorithm; while almost no such algorithm parameter optimization, which brings more convenience in the practical embodiment of the process . 但是高信息传输率的SSVEP-BCI系统对识别算法的要求高,从实验结果来看,上述两种方法(MEC和CCA)所得到的准确率和性能都有待进一步的提高,以提升SSVEP-BCI系统的性能。 However, for high information transmission rate SSVEP-BCI high demands on the system identification algorithm, from the experimental results, the accuracy and performance of the two methods (MEC and CCA) are obtained to be further improved to enhance the SSVEP-BCI performance of the system. 发明内容 SUMMARY

[0009] 本发明的目的是为了解决现有的多导频率检测方法存在的上述问题,提出了一种基于多变量同步指数的频率识别方法。 [0009] The object of the present invention is to solve the above problems of the conventional multi-channel frequency detection method, proposed frequency identification method based on multivariate synchronization index.

[0010] 本发明的技术方案为:一种基于多变量同步指数的频率识别方法,具体包括如下步骤: [0010] aspect of the present invention is: A frequency synchronization index multivariable identification based, includes the following steps:

[0011] 步骤1:根据SSVEP-BCI系统所使用的刺激频率f1; f2,-,fK,构造出每个频率对应的参考信号Rfl, Rf2,…,Rfk ; [0011] Step 1: The frequency of stimulation SSVEP-BCI systems used by f1; f2, -, fK, to construct a reference signal corresponding to each frequency Rfl, Rf2, ..., Rfk;

[0012] 步骤2:分别计算多导脑电信号与各个参考信号之间的同步指数S1, S2,…,Sk ; [0012] Step 2: calculate the synchronization Multichannel EEG index S1 between the respective reference signal, S2, ..., Sk;

[0013] 步骤3:找出与脑电信号同步指数最大的参考信号,将其对应的频率作为识别的频率输出。 [0013] Step 3: Find the maximum index and EEG synchronization reference signal, which is identified as corresponding to the frequency of the output frequency.

[0014] 进一步的,步骤I中构造对应于频率的参考信号可按如下公式计算: [0014] Further, in step I corresponds to the configuration of the reference signal frequency can be calculated as:

Figure CN103054574BD00051

[0016] Fs为采样率,M为样本数。 [0016] Fs is the sampling rate, M being the number of samples.

[0017] 进一步的,步骤2中计算同步指数的具体过程如下: [0017] Further, the specific process steps 2 synchronization index calculated as follows:

[0018] 设多导脑电信号矩阵为X,参考信号为Y,计算X和Y的联合相关矩阵: [0018] provided for the multichannel EEG signal matrix X, the reference signal for the Y, X and Y combined calculated correlation matrix:

Figure CN103054574BD00052

[0024] 进行如下的线性变换: [0024] The following linear transformation:

Figure CN103054574BD00053

[0028] 其中,Inxn为N维单位方阵,4\,2&为2Nh维单位方阵,N是电极数目,Nh是参考信号谐波数量。 [0028] wherein, Inxn an N-dimensional unit matrix, 4 \, & 2 is 2Nh dimensional unit matrix, N is the number of electrodes, Nh is the number of harmonics of the reference signal.

[0029] 将矩阵R进行特征值分解,得到其特征值λρλ)…,λ ρ,并进行标准化: [0029] The eigenvalue decomposition for matrix R, to obtain eigenvalues ​​λρλ) ..., λ ρ, and normalized:

Figure CN103054574BD00054

[0031]其中,p = N+2Nh; [0031] wherein, p = N + 2Nh;

[0032] 最后多导脑电信号和该参考信号之间的同步指数可计算为 [0032] Finally, multi-channel EEG synchronization between the index and the reference signal can be calculated as

Figure CN103054574BD00061

[0033] 本发明的有益效果:本发明提出了一种基于多变量同步指数(MultivariteSynchronization Index, MSI)的频率识别方法,在本方法中,采用两个多维信号的同步指数作为分类特征,对SSVEP-BCI系统中的脑电信号进行频率识别,其核心是计算脑电信号和基于系统使用的刺激频率构造出的不同参考信号之间的同步指数,根据同步指数的大小,找出与脑电信号同步指数最大的参考信号,将该参考信号的频率作为识别的结果输出。 [0033] Advantageous Effects of Invention: The present invention provides a method for identifying a frequency synchronization Multivariate Index (MultivariteSynchronization Index, MSI) based, in this method, the multidimensional signal synchronized using two index as the classification characteristic of SSVEP -BCI EEG frequency identification system, the core index is synchronized between the computing EEG and different reference signals constructed based on the frequency of stimulation system, according to the size of the sync index, and to identify EEG the maximum index sync reference signal, the reference frequency signal as an output result of the recognition. 与现有的所使用的主要多导频率检测方法相比,具有更高的准确率;并且在电极导数少,数据长度较短的条件下,具有最优的性能。 Compared with the conventional multi-channel primary frequency detection method used, with a higher accuracy; and in a small number of conductive electrodes, a short data length conditions, with optimal performance. 本发明的方法能有效地加快SSVEP-BCI系统的响应速度,提高系统的性能。 The method of the present invention is effective to increase the response speed of SSVEP-BCI systems, to improve system performance.

附图说明 BRIEF DESCRIPTION

[0034] 图1基于多变量同步指数(MSI)的频率识别方法的流程示意图。 [0034] Figure 1 is based multivariable synchronizing frequency identification index (MSI) is a schematic flow chart.

[0035] 图2本发明的方法与现有的两种方法的仿真实验对比结果示意图。 [0035] The method of the present invention FIG. 2 is a schematic view of the comparative results of simulation of the two existing methods.

[0036] 图3本发明的方法与现有的两种方法的真实脑电实验对比结果示意图。 [0036] The method of FIG. 3 according to the invention compared with conventional real EEG experiment showing the results of the two methods.

具体实施方式 Detailed ways

[0037] 下面结合附图和具体实施例对本发明做进一步的说明。 Drawings and specific embodiments of the present invention will be further described [0037] below in conjunction.

[0038] 计算信号同步性可以有很多的方法,而SSVEP-BCI系统对识别算法的运行效率要求很高(算法必须在小于I秒时间内给出当前脑电信号的识别结果)。 [0038] The synchronization signal may be calculated there are many ways, but SSVEP-BCI system operating efficiency demanding recognition algorithm (algorithm must be given recognition result of the current EEG time in less than I second). 因此,在基于多变量同步指数(MSI)的频率识别框架中,给出如下的一种高效的频率识别方法。 Thus, the multivariable identification framework frequency synchronization index (MSI), the following is given a high frequency of recognition.

[0039] 假设脑电信号为X (NXM维矩阵),参考信号为Y (2NhXM维矩阵)。 [0039] EEG is assumed that X (NXM dimensional matrix), the reference signal Y (2NhXM dimensional matrix). 这里,N是电极数目,M为样本数,Nh是参考信号谐波数量。 Here, N is the number of electrodes, M being the number of samples, Nh is the number of harmonics of the reference signal. 不是一般性,X和Y已标准化处理,具有零均值单位方差。 Not a general, X and Y have been normalized, with zero mean and unit variance. 下面详细论述基于多变量同步指数的频率检测方法的实施过程: Process embodiment discussed in detail below based on the frequency detection method of multivariate synchronization index:

[0040] 首先,计算X和Y的联合相关矩阵 [0040] First, calculate the X and Y co correlation matrix

Figure CN103054574BD00062

[0046] C包含X、Y自相关以及X和Y互相关,为了减弱自相关对同步指数的影响,进行如下的线性变换: [0046] C comprising X, Y and X and Y from the associated cross-correlation, auto-correlation in order to weaken the influence of the synchronization index, the following linear transformation:

Figure CN103054574BD00063

[0048] 则得到: [0048] is obtained:

Figure CN103054574BD00071

[0050] Inxn为N维单位方阵,'为2Nh维单位方阵。 [0050] Inxn an N-dimensional unit matrix, "as 2Nh dimensional unit matrix.

[0051] 将矩阵R进行特征值分解,得到其特征值λ ρ λ2,…,λ ρ,并进行标准化 [0051] The eigenvalue decomposition for matrix R, to obtain eigenvalues ​​λ ρ λ2, ..., λ ρ, and normalized

Figure CN103054574BD00072

[0053]这里 P=N+2Nh。 [0053] where P = N + 2Nh.

[0054] 最后脑电信号和参考信号之间的同步指数可计算为: [0054] Finally, the synchronization between the index and the reference EEG signals can be calculated as:

[0055] [0055]

Figure CN103054574BD00073

[0056] 假设SSVEP-BCI系统有K个刺激频率f1; f2,…,fK,则对应于频率A的参考信号 [0056] Suppose there are K SSVEP-BCI systems stimulus frequencies f1; f2, ..., fK, corresponds to a frequency of the reference signal A is

可按如下公式计算: It can be calculated as:

Figure CN103054574BD00074

[0058] Fs为采样率。 [0058] Fs is the sampling rate.

[0059] 根据(I) - (9),可以计算所有参考信号与脑电信号的同步指数,进而得到K个同步指数S1, S2,…,Sk,通过如下公式进行最后的频率识别: [0059] The (I) - (9), the index may be calculated for all the reference sync signal and the EEG, and thus obtain a K index sync S1, S2, ..., Sk, final frequency identified by the following formula:

[0060] T=maxSi,i=1,2,...,K[0061] 即当前脑电信号所对应的频率为与脑电信号之间具有最大同步指数的参考信号的频率。 [0060] T = maxSi, i = 1,2, ..., K frequency [0061] i.e., the current EEG frequency corresponding to the maximum between the EEG signal having a reference signal synchronized index.

[0062] 为了更具体说明发明所提及的SSVEP-BCI系统频率识别方法,结合图1进行进一步说明。 [0062] In order to illustrate the invention more specifically referred SSVEP-BCI frequency identification method and system, and further described in conjunction with FIG.

[0063] 如图1所不,多导脑电信号X分别与K个参考信号Rfl, Rf2,…,Rfk作为本发明方法的输入,得到K个同步指数S1, S2,…,Sk,然后求出K个同步指数中最大值。 [0063] FIG. 1 is not, multichannel EEG X and K are reference signals Rfl, Rf2, ..., Rfk as an input method of the present invention, the K synchronization indices obtained S1, S2, ..., Sk, and then seek the maximum index K sync. 根据这个最大值,找到对应的参考信号,该参考信号所使用的频率作为本发明方法的输出结果。 According to this maximum value, find the corresponding reference signal, the reference frequency as a method of the present invention, the output signal is used.

[0064] 为验证本发明的方法的可行性和效果,采用3组频率进行仿真验证,同时与现有的基于最小能量法的检测方法(MEC)和基于典型相关分析的方法(CCA)进行比较。 [0064] In order to verify the feasibility and effect of the method according to the present invention, a simulation frequency group 3, and at the same time compared with the conventional method based on the detection (MEC) Minimum energy method method (CCA) based on canonical correlation analysis . 采用的频率如下: Frequency used is as follows:

[0065] A) 27Hz, 29Hz, 31Hz, 33Hz, 35Hz, 37Hz, 39Hz, 41Hz and43Hz ; [0065] A) 27Hz, 29Hz, 31Hz, 33Hz, 35Hz, 37Hz, 39Hz, 41Hz and43Hz;

[0066] B) 8Hz, 9Hz, 10Hz, 11Hz, 12Hz, 13Hz, 14Hz, 15Hz ; [0066] B) 8Hz, 9Hz, 10Hz, 11Hz, 12Hz, 13Hz, 14Hz, 15Hz;

[0067] C) 6.7Hz, 7.5Hz, 8.6Hz, 10Hz, 12Hz, 15Hz ; [0067] C) 6.7Hz, 7.5Hz, 8.6Hz, 10Hz, 12Hz, 15Hz;

[0068] 用每一个频率产生4个该频率下的正弦信号来模拟4导脑电信号,信号长10s,采样率为250Hz。 [0068] produces four sinusoidal signals at the frequency with a frequency of each guide 4 analog EEG signal length 10s, a sampling rate of 250Hz. 对每导信号按一定信噪比添加高斯白噪声来模拟受噪声污染的真实脑电信号;然后对每组频率下的信号进行频率识别,得到识别准确率,用于频率识别的信号长度为Is ;每组频率重复50次这样的操作,将50次结果的平均识别准确率作为该信噪比下的算法性能评价指标,彳目噪比范围从_7db to_20db。 For each pilot signal according to a certain signal to noise ratio added to simulate the real white Gaussian noise EEG contaminated by noise; then at each frequency the signal frequency identification, recognition accuracy obtained, a signal is frequency identification Is length ; each frequency of this operation was repeated 50 times and the average recognition accuracy algorithm 50 as a result of the performance evaluation in the signal to noise ratio, SNR ranges from left foot mesh _7db to_20db.

[0069] 信噪比的定义公式如下: [0069] The SNR is defined using the following formula:

Figure CN103054574BD00081

[0071] Psignal为信号的能量,Pmise为噪声能量,A正弦信号幅度,σ 2为噪声方差。 [0071] Psignal energy signal, Pmise to noise energy, A sinusoidal signal amplitude, σ 2 is the noise variance.

[0072] 具体仿真结果如图2所示,其中,Ca)所使用的频率为27Hz,29Hz,31Hz,33Hz,35Hz, 37Hz, 39Hz, 41Hz and43Hz ; (b)所使用的频率为8Hz, 9Hz, 10Hz, 11Hz, 12Hz, 13Hz, 14Hz, I5Hz ; (c)所使用的频率为6.7Hz, 7.5Hz, 8.6Hz, 10Hz, 12Hz, 15Hz。 [0072] DETAILED simulation results shown in Figure 2, wherein, Ca) used is frequency 27Hz, 29Hz, 31Hz, 33Hz, 35Hz, 37Hz, 39Hz, 41Hz and43Hz; frequency (b) used is 8Hz, 9Hz, 10Hz, 11Hz, 12Hz, 13Hz, 14Hz, I5Hz; frequency (c) used was 6.7Hz, 7.5Hz, 8.6Hz, 10Hz, 12Hz, 15Hz.

[0073] *表示该条件下MSI与CCA具有显著性差异,+表示该条件下MSI与MEC具有显著性差异,结果使用配对T检验,p〈0.05。 [0073] * represents the MSI CCA having significantly different under this condition, this condition indicates MSI + MEC and having significantly different, the results using a paired T-test, p <0.05.

[0074] 从仿真结果来看,本发明方法的结果是最好的。 [0074] From the simulation results, the results of the method of the present invention is best. 对于第一组高频频率集合和第二组低频频率集合,当信噪比大于_12db时,本发明的方法与现有的两种方法相比具有显著性差异,说明本发明的方法对噪声具有更强的稳健性。 For the first group and second group set high frequency low frequency set, when the SNR is greater than _12db, the method of the present invention and the conventional method has two significant differences compared to the method of the present invention described noise with more robust. 在第三组频率中,因为具有谐波关系的频率成分,所有算法都不能达到100%准确率,但是本发明的方法与现有的两种方法始终保持显著性差异。 In a third set of frequencies, because the frequency component having a harmonic relationship, all the algorithms can not achieve 100% accuracy, but the method of the present invention and the conventional two approaches remains a significant difference.

[0075]另外,采用真实脑电信号进一步验证算法的有效性。 [0075] In addition, the use of real EEG signals further verify the effectiveness of the algorithm. 在实验中,采用8导联脑电采集系统,4种频率7.5Hz,8.6Hz,10Hz, 12Hz,采集每种频率下被试30s脑电信号。 In the experiment, 8-lead EEG acquisition system, four kinds of frequency 7.5Hz, 8.6Hz, 10Hz, 12Hz, 30s EEG acquisition subjects at each frequency. 11个被试(21-28岁)参加了验证试验,实验结果如图3所示。 11 subjects (21-28 years) participated in the verification test, the experimental results shown in Fig. 图中,(a) 4导脑电,(b) 6导脑电,(c)8导脑电。 Figure, (a) 4 Electroencephalogram, (b) 6 EEG signals, (c) 8 EEG signals. *表示该条件下MSI与CCA具有显著性差异,+表示该条件下MSI与MEC具有显著性差异,结果使用配对T检验,p〈0.05。 And * denotes MSI CCA having significantly different under this condition, this condition indicates MSI + MEC and having significantly different, the results using a paired T-test, p <0.05.

[0076] 从结果来看,本发明的方法结果比现有的两种方法都好,尤其是在只用4导脑电信号,信号长度在Is情况下,本发明的方法与现有的两种方法结果存在显著性差异。 [0076] From the results, the method of the present invention are better than the results of the two existing methods, especially in only 4 pilot EEG signal Is in length, the method of the present invention and the conventional two ways significant difference was found. 电极数少,可以给SSVEP-BCI系统的应用带来更多的方便。 Small number of electrodes can be applied to SSVEP-BCI system more convenient. 更为重要的是,用于频率识别的信号长度越短,准确率越高的算法越能减少系统的响应时间,提高系统的响应速度,因此本发明的方法具有更大的潜能去提高SSVEP-BCI系统的性能。 More importantly, the shorter the length of the frequency identification signal, the higher the accuracy of the algorithm can reduce the system's response time, to improve the response speed of the system, thus the method of the present invention has a greater potential to improve SSVEP- BCI system performance.

[0077] 总体来说,仿真实验和真实实验结果验证了本发明方案的有效性和可行性。 [0077] In general, simulation and real experimental results show the effectiveness and feasibility of the present invention.

[0078] 本领域的普通技术人员将会意识到,这里所述的实施例是为了帮助读者理解本发明的原理,应被理解为本发明的保护范围并不局限于这样的特别陈述和实施例。 [0078] Those of ordinary skill in the art will appreciate that the embodiments described herein are to aid the reader in understanding the principles of the present invention, it should be understood that the scope of the present invention is not limited to such embodiments and specifically stated . 本领域的普通技术人员可以根据本发明公开的这些技术启示做出各种不脱离本发明实质的其它各种具体变形和组合,这些变形和组合仍然在本发明的保护范围内。 Those of ordinary skill in the art can make various modifications and other various concrete compositions of the present invention without departing from the spirit of techniques according to teachings of the present disclosure, it is still within the scope of the present invention such variations and combinations.

Claims (3)

1.一种基于多变量同步指数的频率识别方法,具体包括如下步骤: 步骤1:根据SSVEP-BCI系统所使用的刺激频率f\,f2,…,fK,构造出每个频率对应的参考信号Rfl, Rf2,…,Rfk; 步骤2:分别计算多导脑电信号与各个参考信号之间的同步指数S1, S2,…,Sk ; 步骤3:找出与脑电信号同步指数最大的参考信号,将其对应的频率作为识别的频率输出。 1. A frequency synchronization recognition based on multivariate index, includes the following steps: Step 1: The frequency of stimulation SSVEP-BCI system used f \, f2, ..., fK, to construct a reference signal corresponding to each frequency Rfl, Rf2, ..., Rfk; step 2: calculate the synchronization multichannel EEG index S1 between the respective reference signal, S2, ..., Sk; step 3: find the maximum index and EEG synchronization reference signal which was identified as corresponding to the frequency of the output frequency.
2.根据权利要求1所述的频率识别方法,其特征在于,步骤I中构造对应于频率仁的参考信号可按如下公式计算: The frequency of the identification method according to claim 1, characterized in that, in step I Ren configuration corresponding to a frequency of the reference signal can be calculated as:
Figure CN103054574BC00021
Fs为采样率,M为样本数,Nh是参考信号谐波数量。 Fs is the sampling rate, M being the number of samples, Nh is the number of harmonics of the reference signal.
3.根据权利要求2所述的频率识别方法,其特征在于,步骤2中计算同步指数的具体过程如下: 设多导脑电信号矩阵为X,参考信号矩阵为Y,计算X和Y的联合相关矩阵: 3. The frequency of the identification method according to claim 2, wherein during Step 2 specific synchronization index calculated as follows: Let multichannel EEG signal matrix X, the matrix of the reference signal Y, X and Y calculated joint correlation matrix:
Figure CN103054574BC00022
其中, among them,
Figure CN103054574BC00023
进行如下的线性变换: Linear transformation as follows:
Figure CN103054574BC00024
得到: get:
Figure CN103054574BC00025
Inxn为N维方阵,为2Nh维方阵,N是电极数目; 将矩阵R进行特征值分解,得到其特征值λ” λ2,…,λ ρ,并进行标准化: Inxn of N-dimensional square matrix, as 2Nh dimensional matrix, N is the number of electrodes; R matrix eigenvalue decomposition to obtain eigenvalues ​​λ "λ2, ..., λ ρ, and normalized:
Figure CN103054574BC00026
其中,P=N+2Nh;最后可以得到多导脑电信号和该参考信号之间的同步指数 Wherein, P = N + 2Nh; final index can be synchronized between multiple EEG signal and the reference pilot signal
Figure CN103054574BC00031
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