A research for single trial detection of error related negativity_Journal of Biomedical Engineering

Authors：

ZHANG Rui ,  LU Peng , NIU Xin , LIU Sujie , HU Yuxia

School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, P.R.China;

Corresponding author：

LU Peng, Email: lupeng@zzu.edu.cn

Keywords：

error related negativity; brain-computer interface; wavelet transform; time-frequency domain features; single trial recognition

DOI：

10.7507/1001-5515.201708043

Video：

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Abstract Full text Figures/Tables Video References Cited by

Error related negativity (ERN) is generated in frontal and central cortical regions when individuals perceive errors. Because ERN has low signal-to-noise ratio and large individual difference, it is difficult for single trial ERN recognition. In current study, the optimized electroencephalograph (EEG) channels were selected based on the brain topography of ERN activity and ERN offline recognition rate, and the optimized EEG time segments were selected based on the ERN offline recognition rate, then the low frequency time domain and high frequency time-frequency domain features were analyzed based on wavelet transform, after which the ERN single detection algorithm was proposed based on the above procedures. Finally, we achieved average recognition rate of 72.0% ± 9.6% in 10 subjects by using the sample points feature in 0~3.9 Hz and the power and variance features in 3.9~15.6 Hz from the EEG segments of 200~600 ms on the selected 6 channels. Our work has the potential to help the error command real-time correction technique in the application of online brain-computer interface system.

Citation： ZHANG Rui, LU Peng, NIU Xin, LIU Sujie, HU Yuxia. A research for single trial detection of error related negativity. Journal of Biomedical Engineering, 2018, 35(4): 606-612. doi: 10.7507/1001-5515.201708043 Copy

1.	Jafarifarmand A, Badamchizadeh M A, Khanmohammadi S, et al. A new self-regulated neuro-fuzzy framework for classification of EEG signals in motor imagery BCI. IEEE Transactions on Fuzzy Systems, 2018, 26(3): 1485-1497.
2.	Gomez-Pilar J, Corralejo R, Nicolas-Alonso L F, et al. Neurofeedback training with a motor imagery-based BCI: neurocognitive improvements and EEG changes in the elderly. Med Biol Eng Comput, 2016, 54(11): 1655-1666.
3.	Gehring WJ, Goss B, Coles MGH, et al. A neural system for error detection and compensation. Psychol Sci, 1993, 4: 385-90.
4.	Su K K, Kirchner E A. Classifier transferability in the detection of error related potentials from observation to interaction//IEEE International Conference on Systems Man and Cybernetics, 2013: 3360-3365.
5.	Holroyd C B, Coles M G. The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity. Psychological review, 2002, 109(4): 679-709.
6.	Margaux P, Emmanuel M, Sébastien D, et al. Objective and subjective evaluation of online error correction during P300-based spelling. Advances in Human-Computer Interaction, 2012, (6): 1-13.
7.	Omedes J, Iturrate I, Montesano L, et al. Using frequency-domain features for the generalization of EEG error-related potentials among different tasks//2013 35th Annual International Conference of the IEEE Engineering In Medicine and Biology Society (EMBC), 2013: 5263-5266.
8.	Zhang Huaijian, Chavarriaga R, Goel M K, et al. Improved recognition of error related potentials through the use of brain connectivity features. Conf Proc IEEE Eng Med Biol Soc, 2012, (4): 6740-6743.
9.	Tong Jijun, Lin Q, Xiao Ran, et al. Combining multiple features for error detection and its application in brain-computer interface. Biomedical Engineering Online, 2016, 15(1): 17.
10.	Chavarriaga R, Millan J D. Learning from EEG error-related potentials in noninvasive brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng, 2010, 18(4): 381-388.
11.	李松, 伏云发, 杨秋红, 等. 基于左右手运动想象单通道脑电信号的预处理研究. 生物医学工程学杂志, 2016, (5): 862-866.
12.	Hu Jing, Wang Chunsheng, Wu Min, et al. Removal of EOG and EMG artifacts from EEG using combination of functional link neural network and adaptive neural fuzzy inference system. Neurocomputing, 2015, 151(1): 278-287.
13.	Ge Sunan, Han Jie, Han Min. Nonnegative mixture for underdetermined blind source separation based on a tensor algorithm. Circuits Systems and Signal Processing, 2015, 34(9): 2935-2950.
14.	Navarro-Cebrian A, Knight R T, Kayser A S. Frontal monitoring and parietal evidence: mechanisms of error correction. J Cogn Neurosci, 2016, 28(8): 1166-1177.
15.	Munneke G J, Nap T S, Schippers E E, et al. A statistical comparison of EEG time- and time-frequency domain representations of error processing. Brain Research, 2015, 1618: 222-230.
16.	Bhattacharyya A, Pachori R B. A multivariate approach for patient-specific EEG seizure detection using empirical wavelet transform. IEEE Transactions on Biomedical Engineering, 2017, 64(9): 2003-2015.
17.	张毅, 罗明伟, 罗元. 脑电信号的小波变换和样本熵特征提取方法. 智能系统学报, 2012, 7(4): 339-344.
18.	崔刚强, 夏良斌, 梁建峰, 等. 基于小波多尺度分析和极限学习机的癫痫脑电分类算法. 生物医学工程学杂志, 2016, 33(6): 1025-1030.

1. Jafarifarmand A, Badamchizadeh M A, Khanmohammadi S, et al. A new self-regulated neuro-fuzzy framework for classification of EEG signals in motor imagery BCI. IEEE Transactions on Fuzzy Systems, 2018, 26(3): 1485-1497.
2. Gomez-Pilar J, Corralejo R, Nicolas-Alonso L F, et al. Neurofeedback training with a motor imagery-based BCI: neurocognitive improvements and EEG changes in the elderly. Med Biol Eng Comput, 2016, 54(11): 1655-1666.
3. Gehring WJ, Goss B, Coles MGH, et al. A neural system for error detection and compensation. Psychol Sci, 1993, 4: 385-90.
4. Su K K, Kirchner E A. Classifier transferability in the detection of error related potentials from observation to interaction//IEEE International Conference on Systems Man and Cybernetics, 2013: 3360-3365.
5. Holroyd C B, Coles M G. The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity. Psychological review, 2002, 109(4): 679-709.
6. Margaux P, Emmanuel M, Sébastien D, et al. Objective and subjective evaluation of online error correction during P300-based spelling. Advances in Human-Computer Interaction, 2012, (6): 1-13.
7. Omedes J, Iturrate I, Montesano L, et al. Using frequency-domain features for the generalization of EEG error-related potentials among different tasks//2013 35th Annual International Conference of the IEEE Engineering In Medicine and Biology Society (EMBC), 2013: 5263-5266.
8. Zhang Huaijian, Chavarriaga R, Goel M K, et al. Improved recognition of error related potentials through the use of brain connectivity features. Conf Proc IEEE Eng Med Biol Soc, 2012, (4): 6740-6743.
9. Tong Jijun, Lin Q, Xiao Ran, et al. Combining multiple features for error detection and its application in brain-computer interface. Biomedical Engineering Online, 2016, 15(1): 17.
10. Chavarriaga R, Millan J D. Learning from EEG error-related potentials in noninvasive brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng, 2010, 18(4): 381-388.
11. 李松, 伏云发, 杨秋红, 等. 基于左右手运动想象单通道脑电信号的预处理研究. 生物医学工程学杂志, 2016, (5): 862-866.
12. Hu Jing, Wang Chunsheng, Wu Min, et al. Removal of EOG and EMG artifacts from EEG using combination of functional link neural network and adaptive neural fuzzy inference system. Neurocomputing, 2015, 151(1): 278-287.
13. Ge Sunan, Han Jie, Han Min. Nonnegative mixture for underdetermined blind source separation based on a tensor algorithm. Circuits Systems and Signal Processing, 2015, 34(9): 2935-2950.
14. Navarro-Cebrian A, Knight R T, Kayser A S. Frontal monitoring and parietal evidence: mechanisms of error correction. J Cogn Neurosci, 2016, 28(8): 1166-1177.
15. Munneke G J, Nap T S, Schippers E E, et al. A statistical comparison of EEG time- and time-frequency domain representations of error processing. Brain Research, 2015, 1618: 222-230.
16. Bhattacharyya A, Pachori R B. A multivariate approach for patient-specific EEG seizure detection using empirical wavelet transform. IEEE Transactions on Biomedical Engineering, 2017, 64(9): 2003-2015.
17. 张毅, 罗明伟, 罗元. 脑电信号的小波变换和样本熵特征提取方法. 智能系统学报, 2012, 7(4): 339-344.
18. 崔刚强, 夏良斌, 梁建峰, 等. 基于小波多尺度分析和极限学习机的癫痫脑电分类算法. 生物医学工程学杂志, 2016, 33(6): 1025-1030.

Journal of Biomedical Engineering

A research for single trial detection of error related negativity

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