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首页> 外文会议>Humaine Association Conference on Affective Computing and Intelligent Interaction >Towards an Affective Brain-Computer Interface Monitoring Musical Engagement
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Towards an Affective Brain-Computer Interface Monitoring Musical Engagement

机译：朝着一个情感脑电电脑界面监测音乐参与

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A non-invasive way to monitor a music listener's level of engagement could give us a valuable tool for music classification, technology, and therapy. To investigate whether musical engagement can be monitored, we developed an experimental protocol using the mobile brain/body imaging (MoBI) paradigm in which participants make expressive rhythmic arm gestures to encourage and/or index musical engagement. Participants communicate the feeling pulse of music they are hearing via simple rhythmic U-shaped back-and-forth hand/arm 'conducting' gesture cycles that animate, in real time, the mirroring movement of a spot of light on a video display in front of them. Participants are asked to imagine that this display is also being viewed remotely by a deaf friend to whom they are attempting to communicate the feeling of the music they are hearing. In an Engaged condition, listeners are encouraged to fully engage themselves in this musical/emotional communication task. In a Not Engaged condition, a concurrent internal arithmetic distractor task is introduced to induce less fully engaged listening. Here, we report results of training a classifier using a frequency-based common spatial patterns (FBCSP) approach to correctly distinguish Engaged and Not Engaged conditions from concurrently recorded EEG data. Here the approach gave 67% classification accuracy across subjects (versus 50% chance), and 85% accuracy within subjects, cross-validated using a block wise paradigm.
机译：一种非侵入性的方式来监控音乐侦听器的参与水平可能为我们提供一个有价值的音乐分类，技术和治疗工具。为了调查是否可以监控音乐参与，我们使用移动脑/体成像（MOBI）范式开发了一种实验协议，其中参与者使表现力的节奏臂手势鼓励和/或指标音乐参与。参与者通过简单的有节奏U形来回手/手臂'进行手势循环来传达音乐的感觉脉冲，即实时地动画，在前面的视频显示屏上的光点镜像运动他们。要求参与者想象，这个显示也被他们试图传达他们听到的音乐的感觉的聋人来远程观看。在订单中，鼓励听众在这种音乐/情感沟通任务中完全搞。在一个没有订婚的情况下，引入了一个并发内部算术干扰任务，以诱导更少的完全接合的聆听。在这里，我们使用基于频率的常见空间模式（FBCSP）方法来报告培训分类器的结果，以正确地区分从事录制的EEG数据的接合和不参与条件。这里，该方法在主题（与50％的几率）上进行了67％的分类准确性，对象内的85％的准确性，使用块明智的范式交叉验证。

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