Nogueira Waldo, Dolhopiatenko Hanna, Schierholz Irina, Büchner Andreas, Mirkovic Bojana, Bleichner Martin G, Debener Stefan
Department of Otolaryngology, Hearing4all, Hannover Medical School, Hanover, Germany.
Neuropsychology Lab, Department of Psychology, Hearing4all, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany.
Front Neurosci. 2019 Jul 18;13:720. doi: 10.3389/fnins.2019.00720. eCollection 2019.
Electroencephalography (EEG) data can be used to decode an attended speech source in normal-hearing (NH) listeners using high-density EEG caps, as well as around-the-ear EEG devices. The technology may find application in identifying the target speaker in a cocktail party like scenario and steer speech enhancement algorithms in cochlear implants (CIs). However, the worse spectral resolution and the electrical artifacts introduced by a CI may limit the applicability of this approach to CI users. The goal of this study was to investigate whether selective attention can be decoded in CI users using an around-the-ear EEG system (cEEGrid). The performances of high-density cap EEG recordings and cEEGrid EEG recordings were compared in a selective attention paradigm using an envelope tracking algorithm. Speech from two audio books was presented through insert earphones to NH listeners and via direct audio cable to the CI users. 10 NH listeners and 10 bilateral CI users participated in the study. Participants were instructed to attend to one out of the two concurrent speech streams while data were recorded by a 96-channel scalp EEG and an 18-channel cEEGrid setup simultaneously. Reconstruction performance was evaluated by means of parametric correlations between the reconstructed speech and both, the envelope of the attended and the unattended speech stream. Results confirm the feasibility to decode selective attention by means of single-trial EEG data in NH and CI users using a high-density EEG. All NH listeners and 9 out of 10 CI achieved high decoding accuracies. The cEEGrid was successful in decoding selective attention in 5 out of 10 NH listeners. The same result was obtained for CI users.
脑电图(EEG)数据可用于在正常听力(NH)的听众中使用高密度EEG帽以及耳周EEG设备来解码被关注的语音源。该技术可能会应用于在类似鸡尾酒会的场景中识别目标说话者,并指导人工耳蜗(CI)中的语音增强算法。然而,CI引入的较差频谱分辨率和电伪迹可能会限制这种方法在CI用户中的适用性。本研究的目的是调查是否可以使用耳周EEG系统(cEEGrid)在CI用户中解码选择性注意力。在使用包络跟踪算法的选择性注意力范式中,比较了高密度帽EEG记录和cEEGrid EEG记录的性能。来自两本有声读物的语音通过插入式耳机呈现给NH听众,并通过直接音频电缆呈现给CI用户。10名NH听众和10名双侧CI用户参与了该研究。参与者被指示在由96通道头皮EEG和18通道cEEGrid装置同时记录数据时,关注两个并发语音流中的一个。通过重建语音与被关注和未被关注语音流的包络之间的参数相关性来评估重建性能。结果证实了在NH和CI用户中使用高密度EEG通过单次试验EEG数据解码选择性注意力的可行性。所有NH听众和10名CI用户中的9名实现了高解码准确率。cEEGrid在10名NH听众中的5名中成功解码了选择性注意力。CI用户也得到了相同的结果。