Department of Neurosciences, KU Leuven, Research Group Experimental Oto-Rhino-Laryngology, Leuven, Belgium.
Waste Recycling Technologies, Sustainable Materials Management, VITO N.V., Mol, Belgium.
PLoS One. 2021 Feb 11;16(2):e0246769. doi: 10.1371/journal.pone.0246769. eCollection 2021.
Measurement of neural tracking of natural running speech from the electroencephalogram (EEG) is an increasingly popular method in auditory neuroscience and has applications in audiology. The method involves decoding the envelope of the speech signal from the EEG signal, and calculating the correlation with the envelope of the audio stream that was presented to the subject. Typically EEG systems with 64 or more electrodes are used. However, in practical applications, set-ups with fewer electrodes are required. Here, we determine the optimal number of electrodes, and the best position to place a limited number of electrodes on the scalp. We propose a channel selection strategy based on an utility metric, which allows a quick quantitative assessment of the influence of a channel (or a group of channels) on the reconstruction error. We consider two use cases: a subject-specific case, where the optimal number and position of the electrodes is determined for each subject individually, and a subject-independent case, where the electrodes are placed at the same positions (in the 10-20 system) for all the subjects. We evaluated our approach using 64-channel EEG data from 90 subjects. In the subject-specific case we found that the correlation between actual and reconstructed envelope first increased with decreasing number of electrodes, with an optimum at around 20 electrodes, yielding 29% higher correlations using the optimal number of electrodes compared to all electrodes. This means that our strategy of removing electrodes can be used to improve the correlation metric in high-density EEG recordings. In the subject-independent case, we obtained a stable decoding performance when decreasing from 64 to 22 channels. When the number of channels was further decreased, the correlation decreased. For a maximal decrease in correlation of 10%, 32 well-placed electrodes were sufficient in 91% of the subjects.
从脑电图(EEG)中测量自然跑步语音的神经跟踪是听觉神经科学中越来越流行的方法,并且在听力学中有应用。该方法涉及从 EEG 信号中解码语音信号的包络,并计算与呈现给受试者的音频流的包络的相关性。通常使用具有 64 个或更多电极的 EEG 系统。然而,在实际应用中,需要更少电极的设置。在这里,我们确定了最佳的电极数量,以及在头皮上放置有限数量电极的最佳位置。我们提出了一种基于效用度量的通道选择策略,该策略允许对通道(或一组通道)对重建误差的影响进行快速定量评估。我们考虑了两种用例:一种是针对每个受试者单独确定电极的最佳数量和位置的特定于受试者的情况,另一种是所有受试者都将电极放置在相同位置(在 10-20 系统中)的独立于受试者的情况。我们使用 90 名受试者的 64 通道 EEG 数据评估了我们的方法。在特定于受试者的情况下,我们发现实际和重建的包络之间的相关性首先随着电极数量的减少而增加,在大约 20 个电极时达到最佳,与使用所有电极相比,使用最佳数量的电极可获得 29%更高的相关性。这意味着我们的去除电极策略可用于改善高密度 EEG 记录中的相关性度量。在独立于受试者的情况下,当从 64 个通道减少到 22 个通道时,我们获得了稳定的解码性能。当通道数量进一步减少时,相关性降低。为了使相关性最大降低 10%,在 91%的受试者中,仅需 32 个位置良好的电极即可。