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Novel MI-SSSEP mixed brain-computer interface method and system thereof

A machine interface, a new type of technology, applied in the field of brain-machine interface, can solve the problem of not effectively improving the overall performance of MI-BCI, and achieve the effect of improving overall performance and enhancing robustness

Inactive Publication Date: 2017-02-01
TIANJIN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, existing research on MI-SSSEPBCI has not effectively improved the overall performance of MI-BCI

Method used

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  • Novel MI-SSSEP mixed brain-computer interface method and system thereof
  • Novel MI-SSSEP mixed brain-computer interface method and system thereof
  • Novel MI-SSSEP mixed brain-computer interface method and system thereof

Examples

Experimental program
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Embodiment 1

[0039] A novel MI-SSSEP hybrid brain-computer interface method, see figure 1 , the interface method includes the following steps:

[0040] 101: Place two ECG electrodes on the left and right wrists respectively, and perform electrical stimulation on the left and right hands respectively according to the preset frequency to induce slight tremors in the thumb and induce obvious steady-state somatosensory evoked potentials;

[0041] 102: The subject performs motor imagery while receiving stimulation, collects EEG data, and performs preprocessing;

[0042] 103: Perform feature extraction and pattern recognition on the preprocessed EEG data through the common space pattern algorithm, and obtain a single task EEG feature vector;

[0043] 104: Input the single-task EEG feature vector into the support vector machine to train the classifier, and use the ten-fold cross-validation strategy to complete the classification recognition through the support vector machine;

[0044] That is, ...

Embodiment 2

[0053] Below in conjunction with specific drawings, the calculation formula introduces the scheme in embodiment 1 in detail, see the following description for details:

[0054] 201: Median nerve stimulation;

[0055] Among them, electrical stimulation was simultaneously applied to the bilateral median nerves through bidirectional pulses with a pulse width of 200 μs. Two ECG electrodes with a distance of 4 cm are placed on the left and right wrists respectively, such as figure 2 shown. The stimulation frequency was 26 Hz for the left hand and 31 Hz for the right hand. Adjust the position of the electrodes and the magnitude of the current on the left / right wrist respectively to induce a slight trembling of the thumb and elicit an obvious steady-state somatosensory evoked potential. Current intensities varied between 1.5-7 mA for all subjects.

[0056] Wherein, the embodiment of the present invention does not limit the distance between the two ECG electrodes, which can be se...

Embodiment 3

[0082] Below in conjunction with concrete test data, the scheme in embodiment 1 and 2 is done feasibility verification, see the following description for details:

[0083] Figure 5 Classification accuracy for 14 subjects using separate ERD features (ERD), separate SSSEP features (SSSEP) and fusion features (MI-SSSEP). It can be seen that the average correct rate exceeds 70%, and the correct rate obtained through the fusion of ERD and SSSEP features is the highest, with an average of 85%. Through one-way repeated measures analysis of variance, there is a significant difference in the correct rate obtained by using different feature extraction strategies (F(2,26)=7.182, p=0.010). Moreover, the correct rate under MI-SSSEP was significantly higher than that using ERD (p=0.0004) and SSSEP (p=0.042) features alone. The results show that the fusion of the two features of ERD and SSSEP contributes to the improvement of the classification accuracy rate in the mixed paradigm, which p...

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Abstract

The invention discloses a novel motor imagery (MI)-steady state somatosensory evoked potential (SSSEP) mixed brain-computer interface method. The method comprises: two electrocardioelectrodes are placed at a left wrist and a right wrist, electrical simulation is carried out a left hand and a right hand according to a preset frequency, thumbs are induced to tremble slightly, thereby inducing obvious steady-state somatosensory evoked potentials; a tester is simulated and is processed by motor imagery, electroencephalogram data are collected, and pretreatment is carried out; feature extraction and pattern recognition are carried out on the electroencephalogram data by using a common spatial pattern algorithm and a single-task electroencephalogram feature vector is obtained; the single-task electroencephalogram feature vector is inputted into a support vector machine to train a classifier and classification identification is completed by using the support vector machine based on a ten-fold cross validation strategy, so that six sub frequency bands are built by using 4 Hz as stepping at frequency bands of 8 to 32Hz so as to complete classification identification. With the method, the ERD feature and the SSSEP feature are integrated, thereby realizing performance improvement; and robustness of neural-feedback-based rehabilitation training is enhanced.

Description

technical field [0001] The invention relates to the field of brain-computer interface, in particular to a novel MI-SSSEP hybrid brain-computer interface method and system. Background technique [0002] Brain-computer interface (BCI) based on Motor imagery (MI) is the only active BCI system that does not require external stimuli and directly reflects the user's subjective motor awareness. [0003] Motor imagery, that is, only motor intention without actual action output, can lead to changes in the activity state of a large number of neurons in the sensorimotor area of ​​the cerebral cortex, and make certain frequency components in the EEG signal synchronously attenuated or enhanced. This phenomenon is called Event-related desynchronization or synchronization phenomenon (event-related desynchronization orsynchronization, ERD / ERS). [0004] Compared with the visual BCI paradigm, imagined action is the only active BCI paradigm that does not require external stimuli and directly...

Claims

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Application Information

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IPC IPC(8): A61N1/36A61B5/0484
CPCA61B5/7225A61B5/7235A61B5/7264A61B5/7271A61N1/36014A61B5/375A61B5/377
Inventor 明东奕伟波邱爽綦宏志赵欣
Owner TIANJIN UNIV
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