2023 年国际听觉、视觉和听觉科学大会听觉脑电图解码挑战赛

IF 2.9 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Mohammad Jalilpour Monesi;Lies Bollens;Bernd Accou;Jonas Vanthornhout;Hugo Van Hamme;Tom Francart
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引用次数: 0

摘要

本文介绍了听觉脑电图挑战赛,该挑战赛是 2023 年 ICASSP 上的信号处理大挑战赛之一。挑战赛提供了 85 名受试者的脑电图记录,这些受试者在听有声读物或播客等连续语音的同时,大脑活动也被记录下来。其中 71 名受试者的脑电图记录作为训练集提供,这样挑战赛参赛者就可以在一个相对较大的数据集上训练他们的模型。剩下的 14 个受试者则作为评估挑战赛的保留受试者。挑战赛由两项任务组成,将脑电图(EEG)信号与呈现的语音刺激相关联。第一项任务是 "匹配-错配",目的是确定两个语音片段中哪个诱发了给定的脑电图片段。在第二项回归任务中,目标是根据脑电图重建语音包络。在 "匹配-错配 "任务中,不同团队的表现都接近基线模型,而且这些模型都能很好地泛化到未见过的受试者身上。与此相反,在回归任务中,顶尖团队在保留故事测试集上的表现明显优于基线模型,但却不能泛化到未见过的受试者身上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Auditory EEG Decoding Challenge for ICASSP 2023
This paper describes the auditory EEG challenge, organized as one of the Signal Processing Grand Challenges at ICASSP 2023. The challenge provides EEG recordings of 85 subjects who listened to continuous speech, as audiobooks or podcasts, while their brain activity was recorded. EEG recordings of 71 subjects were provided as a training set such that challenge participants could train their models on a relatively large dataset. The remaining 14 subjects were used as held-out subjects in evaluating the challenge. The challenge consists of two tasks that relate electroencephalogram (EEG) signals to the presented speech stimulus. The first task, match-mismatch, aims to determine which of two speech segments induced a given EEG segment. In the second regression task, the goal is to reconstruct the speech envelope from the EEG. For the match-mismatch task, the performance of different teams was close to the baseline model, and the models did generalize well to unseen subjects. In contrast, For the regression task, the top teams significantly improved over the baseline models in the held-out stories test set while failing to generalize to unseen subjects.
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来源期刊
CiteScore
5.30
自引率
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审稿时长
22 weeks
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