Herff Christian, Putze Felix, Heger Dominic, Guan Cuntai, Schultz Tanja
Cognitive Systems Lab, Karlsruhe Institute of Technology, Adenauerring 4, 76131 Karlsruhe, Germany.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1715-8. doi: 10.1109/EMBC.2012.6346279.
Speech is our most natural form of communication and even though functional Near Infrared Spectroscopy (fNIRS) is an increasingly popular modality for Brain Computer Interfaces (BCIs), there are, to the best of our knowledge, no previous studies on speech related tasks in fNIRS-based BCI. We conducted experiments on 5 subjects producing audible, silently uttered and imagined speech or do not produce any speech. For each of these speaking modes, we recorded fNIRS signals from the subjects performing these tasks and distinguish segments containing speech from those not containing speech, solely based on the fNIRS signals. Accuracies between 69% and 88% were achieved using support vector machines and a Mutual Information based Best Individual Feature approach. We are also able to discriminate the three speaking modes with 61% classification accuracy. We thereby demonstrate that speech is a very promising paradigm for fNIRS based BCI, as classification accuracies compare very favorably to those achieved in motor imagery BCIs with fNIRS.
言语是我们最自然的交流形式，尽管功能性近红外光谱技术（fNIRS）在脑机接口（BCI）中越来越受欢迎，但据我们所知，以前没有关于基于fNIRS的BCI中与言语相关任务的研究。我们对5名受试者进行了实验，他们分别发出可听见的语音、默读语音、想象语音或不发出任何语音。对于每种发声模式，我们记录了执行这些任务的受试者的fNIRS信号，并仅根据fNIRS信号区分包含语音的片段和不包含语音的片段。使用支持向量机和基于互信息的最佳个体特征方法，准确率达到了69%至88%。我们还能够以61%的分类准确率区分这三种发声模式。我们由此证明，言语对于基于fNIRS的BCI是一个非常有前景的范例，因为其分类准确率与基于fNIRS的运动想象BCI所取得的准确率相比非常有利。
