Liu Xinyu, Ping Yanna, Wang Dongyun, Yao Ruxian, Wan Hong
School of Information Engineering, Huanghuai University, Zhumadian, Henan 463000, P.R.China;School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, P.R.China;Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou 450001,
School of Information Engineering, Huanghuai University, Zhumadian, Henan 463000, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2018 Oct 25;35(5):786-793. doi: 10.7507/1001-5515.201712038.
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 -nearest neighbor (LOO- NN). And the influence of the parameters, include the number of channels, the position and size of decoding window, and the nearest neighbor 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- NN algorithm, the accuracy is inversely proportional to the value. The smaller the 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.
尖峰信号和局部场电位（LFP）信号都是神经解码最重要的候选信号。目前，关于它们在哺乳动物中的解码性能有大量研究，但在鸟类中的解码性能仍不清楚。我们使用留一法和最近邻（LOO-NN）相结合的解码算法，分析了六只鸽子在目标导向决策任务期间从尾外侧巢皮质区域记录的这两种信号的解码性能。并且还研究了通道数量、解码窗口的位置和大小以及最近邻值等参数对解码性能的影响。本研究结果表明，在该任务期间这两种信号都能有效解码鸽子的运动意图，但相比之下，LFP信号的解码性能高于尖峰信号，且受通道数量的影响较小。最佳解码窗口处于目标导向决策过程的后半段，LFP信号的最佳解码窗口大小（0.3秒）比尖峰信号的（1秒）短。对于LOO-NN算法，准确率与值成反比。值越小，解码准确率越高。本研究结果将有助于解析大脑的神经信息处理机制，也对脑机接口具有参考价值。
