生物医学工程学杂志

生物医学工程学杂志

鸽子目标导向抉择任务中锋电位和局部场电位解码性能对比研究

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锋电位(spike)和局部场电位(LFP)是神经信息解码中最重要的两种候选信号。目前对于哺乳类动物的spike信号和LFP信号的解码性能已有诸多研究,但是鸟类大脑这两种信号的解码性能并不清楚。本文利用6只鸽子为模式动物,基于留一法和k近邻的神经解码算法(LOO-kNN)研究了目标导向抉择任务中弓状皮质尾外侧区spike信号和LFP信号的解码性能,探讨了通道个数、解码窗口的位置、长度以及最近邻k值等参数对解码性能的影响。本文研究结果表明,spike信号和LFP信号都能有效解码出目标导向抉择任务中鸽子的运动意图,但是相比之下,LFP信号解码性能更优异,而且受通道个数的影响较弱。对于解码窗口而言,最佳解码窗口位于目标导向抉择过程的后半段,而且LFP信号最佳解码窗口长度(0.3 s)明显短于spike信号最佳解码窗口长度(1 s)。对于LOO-kNN算法来说,解码正确率与k值的大小基本成反比,k值越小解码正确率越高。通过以上本文研究结果,期望本文方法有助于大脑神经信息处理机制的解析,对于脑—机接口等进一步深入研究提供一定的参考价值。

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.

关键词: 鸽子; 解码; 锋电位; 局部场电位; k近邻算法

Key words: pigeon; decoding; spike; local field potential; k-nearest neighbor

引用本文: 刘新玉, 平燕娜, 王东云, 姚汝贤, 万红. 鸽子目标导向抉择任务中锋电位和局部场电位解码性能对比研究. 生物医学工程学杂志, 2018, 35(5): 786-793. doi: 10.7507/1001-5515.201712038 复制

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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.