Brain Evoked Potential Latencies Optimization for Spatial Auditory Brain–Computer Interface

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• 作者：Zhenyu Cai ; Shoji Makino ; Tomasz M. Rutkowski
• 关键词：Auditory BCI ; Spatial auditory cognition ; EEG ; Event ; related potential (ERP) ; Feature extraction ; Statistical signal processing
• 刊名：Cognitive Computation
• 出版年：2015
• 出版时间：February 2015
• 年：2015
• 卷：7
• 期：1
• 页码：34-43
• 全文大小：694 KB
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  5. Cai Z, Terasawa H, Makino S, Yamada T, Rutkowski TM. Sound timbre and spatial location as informative cues in auditory BCI—brain evoked potential enhancement and involuntary eye movements artifacts suppression approach. In: Proceedings of the third APSIPA annual summit and conference (APSIPA ASC 2011). Xi’an, China: APSIPA; 2011. p. paper #241.
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  11. Cai Z, Makino S, Rutkowski TM. Multichannel analysis of ERP at early and P300 latencies in application for spatial auditory BCI improvement. In: Proceedings of the fourth APSIPA annual summit and conference (APSIPA ASC 2012). Hollywood, CA, USA: APSIPA; 2012. p. paper #355.
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• 刊物主题：Neurosciences; Computation by Abstract Devices; Artificial Intelligence (incl. Robotics); Computational Biology/Bioinformatics;
• 出版者：Springer US
• ISSN：1866-9964

文摘

We propose a novel method for the extraction of discriminative features in electroencephalography (EEG) evoked potential latency. Based on our offline results, we present evidence indicating that a full surround sound auditory brain–computer interface (BCI) paradigm has potential for an online application. The auditory spatial BCI concept is based on an eight-directional audio stimuli delivery technique, developed by our group, which employs a loudspeaker array in an octagonal horizontal plane. The stimuli presented to the subjects vary in frequency and timbre. To capture brain responses, we utilize an eight-channel EEG system. We propose a methodology for finding and optimizing evoked response latencies in the P300 range in order later to classify them correctly and to elucidate the subject’s chosen targets or ignored non-targets. To accomplish the above, we propose an approach based on an analysis of variance for feature selection. Finally, we identify the subjects-intended commands with a Naive Bayesian classifier for sorting the final responses. The results obtained with ten subjects in offline BCI experiments support our research hypothesis by providing higher classification results and an improved information transfer rate compared with state-of-the-art solutions.