CN114371784A - Brain-computer interface decoding method for steady-state visual evoked potential - Google Patents

Brain-computer interface decoding method for steady-state visual evoked potential Download PDF

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CN114371784A
CN114371784A CN202210046576.6A CN202210046576A CN114371784A CN 114371784 A CN114371784 A CN 114371784A CN 202210046576 A CN202210046576 A CN 202210046576A CN 114371784 A CN114371784 A CN 114371784A
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肖晓琳
王丽婕
许敏鹏
明东
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Abstract

The invention discloses a brain-computer interface decoding method for steady-state visual evoked potential, which comprises the following steps: acquiring data of different stimulation durations through a sliding time window aiming at a steady-state visual evoked potential data set, and carrying out band-pass filtering on the data; according to the periodic oscillation characteristic of data, cyclic displacement is taken as a basic means, a novel data expansion technology is designed, and the data expansion technology is combined with other mode recognition methods to carry out electroencephalogram decoding. By expanding the sample data, the invention fully excavates the effective information of the sample, can improve the robustness of the algorithm, shorten the stimulation time and the calibration times, reduce the visual fatigue of the testee and improve the information transmission efficiency of the BCI system.

Description

Brain-computer interface decoding method for steady-state visual evoked potential
Technical Field
The invention relates to the field of brain-computer interfaces, in particular to a brain-computer interface decoding method for Steady-State Visual Evoked Potential (SSVEP).
Background
Brain-Computer Interface (BCI) refers to a system that directly converts central nervous system activities into artificial output, and can replace, repair, enhance, supplement, or improve the normal output of the central nervous system, thereby realizing direct interaction between the nervous system and external devices. BCI integrates multiple engineering technologies, can provide a closed-loop interaction approach for people with limited motor ability, and provides an alternative interaction method for healthy people. According to different signal acquisition modes, the BCI can be divided into invasive BCI, partially invasive BCI and non-invasive BCI. Based on the characteristics of device safety and low cost, the non-invasive BCI is widely applied. The BCI system generally comprises technical links such as signal acquisition, signal decoding and output control, and finally realizes direct communication between the brain and external equipment.
SSVEP is the electrical potential produced by the cerebral cortex to repetitive visual flicker stimulation above 6Hz, consisting of oscillatory activity at the fundamental and harmonic frequencies of the stimulation. The SSVEP based BCI associates blinking visual stimuli with different frequencies with specific commands, and the user can select an output command by focusing on the different stimuli. Moreover, the BCI based on the SSVEP has the characteristics of high signal-to-noise ratio and high information transmission rate, and thus has been widely noticed and researched. Currently, the current practice is. Decoding methods for SSVEP have been developed more and more. Algorithms based on typical Correlation Analysis (CCA) and task Correlation Analysis (TRCA) are commonly used in the decoding process of SSVEP-BCIs, and the information identification accuracy and the information transmission rate of the SSVEP-BCIs are greatly improved. The current BCI technology based on the steady-state visual evoked potential has reached the information transmission rate of 376.58bits/min, and lays a strong foundation for further converting the BCI technology into application results.
However, the decoding method also has the problems of too long single stimulation time and more training times, which easily causes visual fatigue of the user, is not favorable for the user to perform long-time attention and operation, and reduces the user friendliness of the system. In order to increase the availability of the SSVEP-BCIs system, researchers propose a Joint Frequency-Phase Modulation (JFPM) method, and the stimulation paradigm is improved. On the other hand, the trend of research on the SSVEP-BCIs decoding algorithm is to improve the robustness of the algorithm with reduced calibration data and stimulation time. The SSVEP has the characteristic of periodic oscillation, which increases the availability and operability of data.
Disclosure of Invention
The invention provides a brain-computer interface decoding method facing steady state visual evoked potential, the invention provides a cyclic displacement single trial sample (CST) algorithm by utilizing the periodic oscillation characteristic of SSVEP, the algorithm can improve the robustness of the algorithm, shorten the stimulation time and the calibration times, reduce the visual fatigue of a subject and simultaneously improve the information transmission efficiency of a BCI system by expanding sample data and fully excavating effective information of the sample, and the details are described as follows:
a brain-computer interface decoding method oriented to steady-state visual evoked potentials, the method comprising:
acquiring a public reference data set, acquiring data of different stimulation durations from the reference data set through a sliding time window, and performing band-pass filtering on the data;
according to the periodic oscillation characteristic of the reference data set, a data expansion technology suitable for the reference data set is designed by taking cyclic displacement as a basic means, and electroencephalogram decoding is performed by adopting the data expansion technology;
the data expansion technology applied to the reference data set specifically includes:
1) performing cyclic displacement on the training data according to the marking frequency to obtain expanded training data; respectively constructing an original training template and an expanded training template based on the original training data and the expanded training data;
2) constructing an extended training-based spatial filter from the extended training data; performing cyclic displacement on the sample to be tested according to the potential target frequency to obtain extended data of the test sample;
3) constructing an extended test template by using extended data of the test sample; constructing an extended test spatial filter from extended data of the test sample; and matching the correlation coefficients by combining the templates.
Wherein, the training data carries out cyclic shift according to the mark frequency, and the obtained expanded training data is as follows:
the method comprises the steps of periodically dividing training data according to a current assumed frequency, and dividing a first period into a part 1A and a part 1B according to a tail incomplete period; carrying out cyclic shift according to the periodic sequence in sequence;
for the u-th cyclic shiftMoving the original signal according to the direction from a low-order period to a high-order period, wherein the shifting starting point is the starting point of the (u + 1) th period, and the shifting length is the length of a sampling point of one period; the incomplete period of the last vacancy is complemented by the 1B part of the first period in the original signal, the signal with overflow high bit is complemented from the second period to the end, and finally the 1A part of one period is complemented to the end of the signal to generate a new signal with the length of N
Figure BDA0003471334040000021
Wherein, the extended training template is:
extended training template for kth frequency
Figure BDA0003471334040000022
Figure BDA0003471334040000023
Wherein q is the number of single-test-time samples, ckIs the length of a sampling point;
Figure BDA0003471334040000024
the extended data obtained for performing the u-th cyclic shift is named extended training data.
Further, the extended test template is:
extended test template corresponding to k-th frequency
Figure BDA0003471334040000031
Figure BDA0003471334040000032
Figure BDA0003471334040000033
For the k-th frequencyPerforming task related component analysis on the extended test data of the kth frequency for the u-th extended sample to obtain an extended test spatial filter w'k；
And performing combined template matching on the data, and measuring the correlation between the sample to be measured and the kth frequency to be measured in different modes:
Figure BDA0003471334040000034
Figure BDA0003471334040000035
Figure BDA0003471334040000036
Figure BDA0003471334040000037
final correlation coefficient rkFrom a weighted sum of four coefficients:
Figure BDA0003471334040000038
the target frequency is detected as follows:
Figure BDA0003471334040000039
where a is a weight vector.
The technical scheme provided by the invention has the beneficial effects that:
1. the invention utilizes the periodic oscillation characteristic of SSVEP, can improve the robustness of the decoding algorithm under the conditions of reduced calibration data and shortened stimulation time; the invention can reduce the visual fatigue of the user, increase the user friendliness of the system and promote the conversion of the technology to the application achievement;
2. the CST designed by the invention is a data expansion technology, can expand effective data through cyclic displacement, fully excavates effective information of the data, can improve the correlation between a sample to be tested and target frequency, and simultaneously reduces the correlation between the sample to be tested and non-target frequency;
3. the decoding algorithm designed by the invention proves that in the SSVEP, the sample to be detected contains effective information, and the information can be reasonably utilized to improve the performance of the SSVEP-BCIs system; the method provides an innovative and feasible research direction for improving the performance of the SSVEP-BCIs system; further research can obtain a perfect brain-computer interface system, and considerable social and economic benefits are expected to be obtained.
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FIG. 1 is a schematic diagram of a steady-state visual evoked potential oriented brain-computer interface decoding;
the design includes, among other things, acquisition of the published SSVEP reference data set, signal preprocessing, and target detection identification.
FIG. 2 is a schematic diagram of the u-th CST;
FIG. 3 is a schematic of SSVEP decoding based on cyclic shift single-run sample and task-related component analysis.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in further detail below.
Example 1
When the human eye is subjected to a visual stimulus of a fixed frequency, the visual cortex of the human brain produces a continuous response related to the stimulus frequency, called the steady-state visual evoked potential. According to the method, an algorithm of a Cyclic Shift single test sample (CST) is designed according to the periodic oscillation characteristic of the SSVEP, and a Task-Related Component Analysis (TRCA) algorithm is fused to perform SSVEP classification identification. The off-line test result shows that the method is beneficial to improving the information identification accuracy and the information transmission rate of the SSVEP-BCIs.
The technical process comprises the following steps: the method comprises the steps of obtaining a public SSVEP reference data set, carrying out certain preprocessing and feature extraction, carrying out classification and identification by using a CST-TRCA algorithm, and calculating the judgment accuracy and the information transmission rate.
The invention can effectively improve the performance of the SSVEP-BCIs system in small samples and short stimulation time, thereby having more market application scenes, being applied to the fields of rehabilitation, life, entertainment and the like of disabled people and being expected to obtain considerable social and economic benefits.
Example 2
The scheme in embodiment 1 is further described below with reference to specific calculation formulas, examples, and fig. 1 to 3, and is described in detail below:
first, obtaining reference data
The embodiment of the invention adopts an SSVEP reference data set proposed in 2016 of the King force et al, which comprises data of 35 healthy volunteers. In the experiment, the subject was seated in a chair 70cm from the display screen, on which 40 visual stimuli were displayed in a 5 × 8 matrix. And coding the stimulation matrix by adopting a joint frequency phase modulation method. The frequency range of all stimulation blocks is set to 8-15.8Hz and the phase is 0-1.5 pi.
Second, signal preprocessing
Data for different stimulation durations are first acquired by sliding the time window. The time window length is 0.3s-1.0s, and the sliding step length is 0.1 s. The purpose of the sliding time window was to explore the effect of CST on the information recognition rate and information transmission rate at different stimulation durations. The data is then subjected to appropriate bandpass filtering.
Third, decoding step
The target detection and identification of the embodiment of the invention is based on cyclic shift single test sample (CST) and Task Related Component Analysis (TRCA) development.
The embodiment of the invention designs a data expansion technology CST suitable for SSVEP by taking cyclic displacement as a basic means according to the periodic oscillation characteristic of the SSVEP, adopts a CST-TRCA algorithm to perform electroencephalogram decoding, and mainly comprises the following seven steps:
(1) performing cyclic displacement on the training data according to the marking frequency to obtain expanded training data;
(2) respectively constructing an original training template and an expanded training template based on the original training data and the expanded training data;
(3) constructing an extended training spatial filter based on TRCA (true training code) by using the extended training data;
(4) performing cyclic displacement on the sample to be tested according to the potential target frequency to obtain extended data of the test sample;
(5) constructing an extended test template by using extended data of the test sample;
(6) constructing a TRCA-based extended test spatial filter from extended data of the test sample;
(7) and matching the correlation coefficients by combining the templates.
1. Cyclic shift test single sample (CST)
Assume a sampling frequency of fsIdeally, f is for one frequencykThe SSVEP test of (2), the period length of the signal being fs/fk. For the experiment, perform an fs/fkA cyclic shift of the length will result in a new trial. The new data had the same SSVEP properties as the initial experiments. However, if the trial is circularly shifted by other lengths, the original SSVEP properties will be broken in the expanded data. On this basis, embodiments of the present invention suggest that CST generate a new series of data from a single sample and investigate which displacement length is more suitable for extracting stable SSVEP.
Referring to FIG. 2, for a sample point of N, the sampling frequency is fsAssuming a single trial signal of frequency fkThe signal comprising
Figure BDA0003471334040000061
An integer number of periods. If the signal contains incomplete periods, calculating the complete analog period signal corresponding to the current period number, the length of the sampling point is ck：
Figure BDA0003471334040000062
Wherein the content of the first and second substances,
Figure BDA0003471334040000063
indicating rounding the number towards infinity. Therefore, the sampling point length of the incomplete period of the current signal is lk：
lk＝ck-N (2)
And the original signal is periodically divided according to the current assumed frequency, and the first period is divided into two parts, namely 1A and 1B according to the last incomplete period. And sequentially performing cyclic shift according to the periodic sequence. For u (th)
Figure BDA0003471334040000064
And (2) secondary cyclic shift, moving the original signal in the direction from a low bit period to a high bit period (from right to left), wherein the shift starting point is the starting point of the (u + 1) th period, and the shift length is the length of a sampling point in one period. The incomplete period with empty tail is complemented by the 1B part of the first period in the original signal, the signal with overflow high bit is complemented from the second period to the tail, finally the 1A part of one period is complemented to the tail end of the signal to generate a new signal with length of N
Figure BDA0003471334040000065
2. Decoding process
In the feature extraction process, the training samples are first processed.
The original training sample set for the kth stimulus is denoted XkThere were q single-test samples. According to the mark frequency fkThe extended data obtained by performing the u-th cyclic shift is named extended training data
Figure BDA0003471334040000066
Thus, a set X comprising the original training samples can be obtainedkIn CkQ single test samples.
The corresponding trial times are superposed and averaged to obtain the original training template and the expansion of the k frequencyThe training templates are respectively recorded as
Figure BDA0003471334040000067
And
Figure BDA0003471334040000068
Figure BDA0003471334040000069
and (4) performing task related component analysis on all the stimulated extension training data to obtain the extended integrated spatial filter W.
3. Processing a sample to be tested
The sample X to be tested performs CST at all potential target frequencies. Wherein, the u-th spreading sample corresponding to the k-th frequency is recorded as
Figure BDA0003471334040000071
Thereby, an extended test template corresponding to the k-th frequency can be obtained
Figure BDA0003471334040000072
Figure BDA0003471334040000073
Task related component analysis is carried out on the extended test data of the kth frequency to obtain an extended test spatial filter w'k。
Referring to fig. 3, then, the data is subjected to joint template matching, and the correlation between the sample to be measured and the kth frequency to be measured can be measured in different ways:
Figure BDA0003471334040000074
Figure BDA0003471334040000075
Figure BDA0003471334040000076
Figure BDA0003471334040000077
final correlation coefficient rkFrom a weighted sum of four coefficients:
Figure BDA0003471334040000078
where a is a weight vector.
The target frequency is detected according to equation (10):
Figure BDA0003471334040000079
the decoding of the electroencephalogram characteristics induced by the target frequency can be completed according to the decoding algorithm.
In the embodiment of the present invention, except for the specific description of the model of each device, the model of other devices is not limited, as long as the device can perform the above functions.
Those skilled in the art will appreciate that the drawings are only schematic illustrations of preferred embodiments, and the above-described embodiments of the present invention are merely provided for description and do not represent the merits of the embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (3)

1. A brain-computer interface decoding method oriented to steady-state visual evoked potentials, the method comprising:
acquiring data of different stimulation durations through a sliding time window aiming at a steady-state visual evoked potential data set, and carrying out band-pass filtering on the data;
according to the periodic oscillation characteristic of data, cyclic displacement is taken as a basic means, a novel data expansion technology is designed, and the data expansion technology is combined with other mode identification methods for electroencephalogram decoding;
the novel data expansion technology specifically comprises the following steps:
1) performing cyclic displacement on the training data according to the marking frequency to obtain expanded training data; respectively constructing an original training template and an expanded training template based on the original training data and the expanded training data;
2) constructing an extended training-based spatial filter from the extended training data; performing cyclic displacement on the sample to be tested according to the potential target frequency to obtain extended data of the test sample;
3) constructing an extended test template by using extended data of the test sample; constructing an extended test spatial filter from extended data of the test sample; and matching the correlation coefficients by combining the templates.
2. The steady-state visual evoked potential oriented brain-computer interface decoding method as set forth in claim 1, wherein the training data is circularly shifted according to the labeling frequency, and the obtained extended training data is:
the method comprises the steps of periodically dividing training data according to a current assumed frequency, and dividing a first period into a part 1A and a part 1B according to a tail incomplete period; carrying out cyclic shift according to the periodic sequence in sequence;
for the u-th cyclic shift, moving the original signal according to the direction from a low bit period to a high bit period, wherein the shift starting point is the starting point of the u +1 th period, and the shift length is the length of a sampling point of one period; the incomplete period of the last vacancy is complemented by the 1B part of the first period in the original signal, the signal with overflow high bit is complemented from the second period to the end, and finally the 1A part of one period is complemented to the end of the signal to generate a new signal with the length of N
Figure FDA0003471334030000011
3. The method of claim 1, wherein the brain-computer interface decoding method facing the steady-state visual evoked potential,
the extended training template is as follows:
extended training template for kth frequency
Figure FDA0003471334030000012
Figure FDA0003471334030000013
Wherein q is the number of single-test-time samples, ckIs the length of a sampling point;
Figure FDA0003471334030000014
the extended data obtained for executing the u-th cyclic shift is named extended training data;
the extended test template is as follows:
extended test template corresponding to k-th frequency
Figure FDA0003471334030000021
Figure FDA0003471334030000022
Figure FDA0003471334030000023
Task related component analysis is carried out on the extended test data of the k-th frequency for the u-th extended sample corresponding to the k-th frequency, and an extended test spatial filter w 'can be obtained'k；
And performing combined template matching on the data, and measuring the correlation between the sample to be measured and the kth frequency to be measured in different modes:
Figure FDA0003471334030000024
Figure FDA0003471334030000025
Figure FDA0003471334030000026
Figure FDA0003471334030000027
final correlation coefficient rkFrom a weighted sum of four coefficients:
Figure FDA0003471334030000028
the target frequency is detected as follows:
Figure FDA0003471334030000029
where a is a weight vector.
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