EEG-based Auditory Attention Decoding: Towards Neuro-Steered Hearing Devices
Simon Geirnaert, Servaas Vandecappelle, Emina Alickovic, Alain de Cheveigné, Edmund Lalor, Bernd T. Meyer, Sina Miran, Tom Francart, Alexander Bertrand
People suffering from hearing impairment often have difficulties participating in conversations in so-called `cocktail party' scenarios with multiple people talking simultaneously. Although advanced algorithms exist to suppress background noise in these situations, a hearing device also needs information on which of these speakers the user actually aims to attend to. The correct (attended) speaker can then be enhanced using this information, and all other speakers can be treated as background noise. Recent neuroscientific advances have shown that it is possible to determine the focus of auditory attention from non-invasive neurorecording techniques, such as electroencephalography (EEG). Based on these new insights, a multitude of auditory attention decoding (AAD) algorithms have been proposed, which could, combined with the appropriate speaker separation algorithms and miniaturized EEG sensor devices, lead to so-called neuro-steered hearing devices. In this paper, we provide a broad review and a statistically grounded comparative study of EEG-based AAD algorithms and address the main signal processing challenges in this field.
University of Texas at Dallas（德克萨斯大学达拉斯分校）；Tongmyong University（东明大学）
In multi speakers environments, cochlear implant (CI) users may attend to a target sound source in a different manner from the normal hearing (NH) individuals during a conversation. This study attempted to investigate the effect of conversational sound levels on the mechanisms adopted by CI and NH listeners in selective auditory attention and how it affects their daily conversation. Nine CI users (five bilateral, three unilateral, and one bimodal) and eight NH listeners participated in this study. The behavioral speech recognition scores were collected using a matrix sentences test and neural …
Attentive listening in a multispeaker environment such as a cocktail party requires suppression of the interfering speakers and the noise around. People with normal hearing perform remarkably well in such situations. Analysis of the cortical signals using electroencephalography (EEG) has revealed that the EEG signals track the envelope of the attended speech stronger than that of the interfering speech. This has enabled the development of algorithms that can decode the selective attention of a listener in controlled experimental settings. However, often these algorithms require longer trial du…
Auditory attention decoding (AAD) algorithms decode the auditory attention from electroencephalography (EEG) signals that capture the listener's neural activity. Such AAD methods are believed to be an important ingredient towards so-called neuro-steered assistive hearing devices. For example, traditional AAD decoders allow detecting to which of multiple speakers a listener is attending to by reconstructing the amplitude envelope of the attended speech signal from the EEG signals. Recently, an alternative paradigm to this stimulus reconstruction approach was proposed, in which the directional f…
Identifying the target speaker in hearing aid applications is crucial to improve speech understanding. Recent advances in electroencephalography (EEG) have shown that it is possible to identify the target speaker from single-trial EEG recordings using auditory attention decoding (AAD) methods. AAD methods reconstruct the attended speech envelope from EEG recordings, based on a linear least-squares cost function or non-linear neural networks, and then directly compare the reconstructed envelope with the speech envelopes of speakers to identify the attended speaker using Pearson correlation coef…
Scattering Transform for Auditory Attention Decoding
Institute for Signal Processing, University of Luebeck（信号处理研究所，吕贝克大学）；Department for Vision and Machine Learning, FPI Food Processing Innovation GmbH & Co. KG（视觉与机器学习系，FPI食品加工创新有限责任公司）；German Research Center for Artificial Intelligence (DFKI), AI for Assistive Health Technologies（德国人工智能研究中心（DFKI），辅助健康技术人工智能）
本文提出使用两层散射变换替代传统预处理方法，以提升听觉注意力解码性能，尤其在 KUL 数据集上表现显著。
AADNet: An End-to-End Deep Learning Model for Auditory Attention Decoding
Center for Ear-EEG, Department of Electrical and Computer Engineering, Aarhus University（耳电中心，电气与计算机工程系，奥胡斯大学）；Meta Reality Labs（Meta现实实验室）；Department of Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven and Leuven.AI - KU Leuven institute for AI（电气工程系（ESAT），动态系统、信号处理与数据分析中心，根特大学及Leuven.AI - 根特大学人工智能研究所）；Department of Neurosciences, Research Group ExpORL, Herestraat 49 box 721, B-3000 Leuven, Belgium（神经科学系，ExpORL研究组，Belgium Leuven）
BITS Pilani K K Birla Goa Campus（BITS Pilani K K Birla 果阿校区）
Auditory attention is a selective type of hearing in which people focus their attention intentionally on a specific source of a sound or spoken words whilst ignoring or inhibiting other auditory stimuli. In some sense, the auditory attention score of an individual shows the focus the person can have in auditory tasks. The recent advancements in deep learning and in the non-invasive technologies recording neural activity beg the question, can deep learning along with technologies such as electroencephalography (EEG) be used to predict the auditory attention score of an individual? In this paper…
Auditory Attention Decoding without Spatial Information: A Diotic EEG Study
Department of Electronic and Information Engineering, School of Engineering, The University of Osaka, Suita, Japan（大阪大学工学部电子信息工学科）；Graduate School of Engineering, The University of Osaka, Suita, Japan（大阪大学工学研究院）
FAConformer: Frequency-Aware Convolutional Transformer for Auditory Attention Decoding
Hubei Key Laboratory of Brain-inspired Intelligent Systems, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（湖北脑启发智能系统重点实验室，人工智能与自动化学院，华中科技大学）
