Decoding Phase-based Information from SSVEP Recordings with Use of Complex-Valued Neural Network
Decoding Phase-based Information from SSVEP Recordings with Use of Complex-Valued Neural Network
复制标题
使用复值神经网络解码 SSVEP 记录中的基于相位的信息
DOI：
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发表时间：
2011
期刊：
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通讯作者：
Be
中科院分区：
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作者：
N. Manyakov;N. Chumerin;Adrien Combaz;Arne Robben;Marijn van Vliet;M. V. Van Hulle;N. Manyakov;Marijn Vanvliet;Be
本文报道了利用基于多值神经元的多层前馈神经网络对稳态视觉诱发电位(SSVEP)记录中的相位信息进行解码。这类网络的输入和输出非常适合所考虑的任务。译码精度对W.r.t.的依赖性。讨论了目标个数和译码窗口大小。将已有的基于相位的SSVEP译码方法与本文提出的译码方法进行了比较，结果表明，在目标类数目较多、译码窗口大小较大的情况下，基于相位的译码方法性能较好。讨论了为每个对象选择合适的频率的必要性。
In this paper, we report on the decoding of phase-based information from steady-state visual evoked potential (SSVEP) recordings by means of a multilayer feedforward neural network based on multivalued neurons. Networks of this kind have inputs and outputs which are well fitted for the considered task. The dependency of the decoding accuracy w.r.t. the number of targets and the decoding window size is discussed. Comparing existing phase-based SSVEP decoding methods with the proposed approach, we show that the latter performs better for the larger amount of target classes and the sufficient size of decoding window. The necessity of the proper frequency selection for each subject is discussed.