Detection of Note Onsets From EEG While Listening to Music
Detection of Note Onsets From EEG While Listening to Music
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发表时间：
2021-12
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通讯作者：
Yuiko Kumagai;Toshihisa Tanaka
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作者：
Yuiko Kumagai;Toshihisa Tanaka
本文提出了一种根据脑电图（EEG）信号预测音乐中音符开始的方法。参与者聆听了 45 种由相同节奏的钢琴声音产生的单音旋律。使用旋律分数按 100 毫秒给出训练标签（开始或不开始）。听音乐时的脑电图被分成窗口宽度为 500 毫秒、重叠为 100 毫秒的片段。然后，我们使用逻辑回归（LR）或支持向量机（SVM）解决分类问题。我们报告称，14 名参与者中有 5 名的曲线下面积 (AUC) 超过 0.7。此外，当使用预测的开始序列来预测正在听的音乐刺激时，最大分类准确度为 91.7%。这些结果表明每个音符都可以从大脑反应中解码。所提出的方法可以测量大脑对每个音符的反应或适用于使用自然音乐的脑机接口（BCI）。
This paper proposes an approach to predicting the onsets of notes in music from electroencephalogram (EEG) signals. Participants listened to 45 kinds of single-tone melodies produced by piano sounds set on the same tempo. Training labels (onset or not-onset) were given by 100 ms using the scores of the melodies. An EEG while listening music was divided into segments with a window width of 500 ms and an overlap of 100 ms. Then, we solve the classification problems using logistic regression (LR) or support vector machine (SVM). We report that five out of fourteen participants' areas under the curve (AUC) indicated more than 0.7. Furthermore, when the predicted onset sequence was used to predict the musical stimulus being listened to, the maximum classification accuracy was 91.7%. These results suggest that each note can be decoded from brain response. The proposed approach can measure brain responses to each note or adapted for brain-computer interface (BCI) using natural music.