Comparison of decoding performance between spike and local field potential signals during goal-directed decision-making task of pigeons_Journal of Biomedical Engineering

Authors：

 LIU Xinyu ^1,2,3 , PING Yanna ¹ , WANG Dongyun ¹ , YAO Ruxian ¹ , WAN Hong ^2,3

1. School of Information Engineering, Huanghuai University, Zhumadian, Henan 463000, P.R.China;
2. School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, P.R.China;
3. Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou 450001, P.R.China;

Corresponding author：

LIU Xinyu, Email: liuxinyu@huanghuai.edu.cn

Keywords：

pigeon; decoding; spike; local field potential; k-nearest neighbor

DOI：

10.7507/1001-5515.201712038

Video：

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Abstract Full text Figures/Tables Video References Cited by

Both spike and local field potential (LFP) signals are two of the most important candidate signals for neural decoding. At present there are numerous studies on their decoding performance in mammals, but the decoding performance in birds is still not clear. We analyzed the decoding performance of both signals recorded from nidopallium caudolaterale area in six pigeons during the goal-directed decision-making task using the decoding algorithm combining leave-one-out and k-nearest neighbor (LOO-kNN). And the influence of the parameters, include the number of channels, the position and size of decoding window, and the nearest neighbor k value, on the decoding performance was also studied. The results in this study have shown that the two signals can effectively decode the movement intention of pigeons during the this task, but in contrast, the decoding performance of LFP signal is higher than that of spike signal and it is less affected by the number of channels. The best decoding window is in the second half of the goal-directed decision-making process, and the optimal decoding window size of LFP signal (0.3 s) is shorter than that of spike signal (1 s). For the LOO-kNN algorithm, the accuracy is inversely proportional to the k value. The smaller the k value is, the larger the accuracy of decoding is. The results in this study will help to parse the neural information processing mechanism of brain and also have reference value for brain-computer interface.

Citation： LIU Xinyu, PING Yanna, WANG Dongyun, YAO Ruxian, WAN Hong. Comparison of decoding performance between spike and local field potential signals during goal-directed decision-making task of pigeons. Journal of Biomedical Engineering, 2018, 35(5): 786-793. doi: 10.7507/1001-5515.201712038 Copy

1.	Horikawa T, Tamaki M, Miyawaki Y, et al. Neural decoding of visual imagery during sleep. Science, 2013, 340(6132): 639-642.
2.	董琪, 秦臻, 胡靓, 等. 基于脑-机接口和嗅觉解码的仿生气味识别系统. 电子科技大学学报, 2015, 44(5): 795-799.
3.	刘新玉, 海鑫, 尚志刚, 等. 利用粒子滤波重建位置细胞编码的运动轨迹. 生物化学与生物物理进展, 2016, 43(8): 817-826.
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6.	Chapin J K, Moxon K A, Markowitz R S, et al. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat Neurosci, 1999, 2(7): 664-670.
7.	Mehring C, Rickert J, Vaadia E, et al. Inference of hand movements from local field potentials in monkey motor cortex. Nat Neurosci, 2003, 6(12): 1253-1254.
8.	Zhuang Jun, Truccolo W, Vargas-Irwin C, et al. Decoding 3-D reach and grasp kinematics from high-frequency local field potentials in primate primary motor cortex. IEEE Trans Biomed Eng, 2010, 57(7): 1774-1784.
9.	Stavisky S D, Kao J C, Nuyujukian P, et al. A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes. J Neural Eng, 2015, 12(3): 036009.
10.	Hu Liyu, Huang Minwei, Ke Shihwen, et al. The distance function effect on k-nearest neighbor classification for medical datasets. SpringerPlus, 2016, 5(1): 1304.
11.	Cawley G C, Talbot N L. Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers. Pattern Recognit, 2003, 36(11): 2585-2592.
12.	Liu Xinyu, Wan Hong, Li Shan, et al. The role of nidopallium caudolaterale in the goal-directed behavior of pigeons. Behav Brain Res, 2017, 326: 112-120.
13.	Liu Xinyu, Zhao Kun, Wang Dongyun, et al. Goal-directed behavior elevates gamma oscillations in nidopallium caudolaterale of pigeon. Brain Res Bull, 2018, 137: 10-16.
14.	Karten H J, Hodos W. Stereotaxic atlas of the brain of the pigeon (Columba livia). Baltimore: Johns Hopkins Press, 1967: 3-7.
15.	刘新玉, 尚志刚, 万红. 神经元锋电位检测中大幅值干扰的去除. 中国科技论文, 2013, 8(1): 46-50.
16.	Liu Xinyu, Wan Hong, Li Shan, et al. Adaptive common average reference for in vivo multichannel local field potentials. Biomedical Engineering Letters, 2017, 7(1): 7-15.
17.	Wilcoxon F. Individual comparisons by ranking methods. Biometrics Bull, 1945, 6(1): 80-83.
18.	Warren D J, Kellis S, Nieveen J G, et al. Recording and decoding for neural prostheses. Proceedings of the IEEE, 2016, 104(2, SI): 374-391.
19.	徐佳敏, 王策群, 林龙年. 多通道在体记录技术——动作电位与场电位信号处理. 生理学报, 2014, 66(3): 349-357.

1. Horikawa T, Tamaki M, Miyawaki Y, et al. Neural decoding of visual imagery during sleep. Science, 2013, 340(6132): 639-642.
2. 董琪, 秦臻, 胡靓, 等. 基于脑-机接口和嗅觉解码的仿生气味识别系统. 电子科技大学学报, 2015, 44(5): 795-799.
3. 刘新玉, 海鑫, 尚志刚, 等. 利用粒子滤波重建位置细胞编码的运动轨迹. 生物化学与生物物理进展, 2016, 43(8): 817-826.
4. Lu Hu, Yang Shengtao, Lin Longnian, et al. Prediction of rat behavior outcomes in memory tasks using functional connections among neurons. PLoS One, 2013, 8(9): e74298.
5. Lebedev M A, Nicolelis M A. Brain-machine interfaces: past, present and future. Trends Neurosci, 2006, 29(9): 536-546.
6. Chapin J K, Moxon K A, Markowitz R S, et al. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat Neurosci, 1999, 2(7): 664-670.
7. Mehring C, Rickert J, Vaadia E, et al. Inference of hand movements from local field potentials in monkey motor cortex. Nat Neurosci, 2003, 6(12): 1253-1254.
8. Zhuang Jun, Truccolo W, Vargas-Irwin C, et al. Decoding 3-D reach and grasp kinematics from high-frequency local field potentials in primate primary motor cortex. IEEE Trans Biomed Eng, 2010, 57(7): 1774-1784.
9. Stavisky S D, Kao J C, Nuyujukian P, et al. A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes. J Neural Eng, 2015, 12(3): 036009.
10. Hu Liyu, Huang Minwei, Ke Shihwen, et al. The distance function effect on k-nearest neighbor classification for medical datasets. SpringerPlus, 2016, 5(1): 1304.
11. Cawley G C, Talbot N L. Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers. Pattern Recognit, 2003, 36(11): 2585-2592.
12. Liu Xinyu, Wan Hong, Li Shan, et al. The role of nidopallium caudolaterale in the goal-directed behavior of pigeons. Behav Brain Res, 2017, 326: 112-120.
13. Liu Xinyu, Zhao Kun, Wang Dongyun, et al. Goal-directed behavior elevates gamma oscillations in nidopallium caudolaterale of pigeon. Brain Res Bull, 2018, 137: 10-16.
14. Karten H J, Hodos W. Stereotaxic atlas of the brain of the pigeon (Columba livia). Baltimore: Johns Hopkins Press, 1967: 3-7.
15. 刘新玉, 尚志刚, 万红. 神经元锋电位检测中大幅值干扰的去除. 中国科技论文, 2013, 8(1): 46-50.
16. Liu Xinyu, Wan Hong, Li Shan, et al. Adaptive common average reference for in vivo multichannel local field potentials. Biomedical Engineering Letters, 2017, 7(1): 7-15.
17. Wilcoxon F. Individual comparisons by ranking methods. Biometrics Bull, 1945, 6(1): 80-83.
18. Warren D J, Kellis S, Nieveen J G, et al. Recording and decoding for neural prostheses. Proceedings of the IEEE, 2016, 104(2, SI): 374-391.
19. 徐佳敏, 王策群, 林龙年. 多通道在体记录技术——动作电位与场电位信号处理. 生理学报, 2014, 66(3): 349-357.

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