Donders Institute for Brain, Cognition and Behaviour: Centre for Cognition, Radboud University, Montessorilaan 3, 6525 HE, Nijmegen, The Netherlands.
J Neural Eng. 2011 Jun;8(3):036002. doi: 10.1088/1741-2560/8/3/036002. Epub 2011 Apr 4.
Subjective accenting is a cognitive process in which identical auditory pulses at an isochronous rate turn into the percept of an accenting pattern. This process can be voluntarily controlled, making it a candidate for communication from human user to machine in a brain-computer interface (BCI) system. In this study we investigated whether subjective accenting is a feasible paradigm for BCI and how its time-structured nature can be exploited for optimal decoding from non-invasive EEG data. Ten subjects perceived and imagined different metric patterns (two-, three- and four-beat) superimposed on a steady metronome. With an offline classification paradigm, we classified imagined accented from non-accented beats on a single trial (0.5 s) level with an average accuracy of 60.4% over all subjects. We show that decoding of imagined accents is also possible with a classifier trained on perception data. Cyclic patterns of accents and non-accents were successfully decoded with a sequence classification algorithm. Classification performances were compared by means of bit rate. Performance in the best scenario translates into an average bit rate of 4.4 bits min(-1) over subjects, which makes subjective accenting a promising paradigm for an online auditory BCI.
主观重音是一种认知过程，在这个过程中，等时率的相同听觉脉冲会变成重音模式的感知。这个过程可以被主动控制，因此它是脑机接口（BCI）系统中人类用户向机器进行通信的候选方案。在这项研究中，我们调查了主观重音是否是 BCI 的一种可行范例，以及它的时间结构性质如何可以从非侵入性 EEG 数据中进行最佳解码。十位被试感知并想象了不同的度量模式（两拍、三拍和四拍）叠加在稳定的节拍器上。通过离线分类范例，我们在单次试验（0.5 秒）水平上对想象中的重音和非重音进行分类，所有被试的平均准确率为 60.4%。我们表明，使用基于感知数据训练的分类器也可以对想象中的重音进行解码。使用序列分类算法成功解码了重音和非重音的循环模式。通过比特率进行分类性能比较。最佳情况下的性能转换为平均每位受试者 4.4 位/分钟，这使得主观重音成为在线听觉 BCI 的一种很有前途的范例。
