Real-Time Decoding of Auditory Attention from EEG via Bayesian Filtering

被引:0
作者
Miran, Sina [1 ,2 ]
Akram, Sahar [3 ]
Sheikhattar, Alireza [1 ,2 ]
Simon, Jonathan Z. [1 ,2 ]
Zhang, Tao [4 ]
Babadi, Behtash [1 ,2 ]
机构
[1] Univ Maryland, Dept Elect & Comp Engn, College Pk, MD 20742 USA
[2] Univ Maryland, Inst Syst Res, College Pk, MD 20742 USA
[3] Facebook, Menlo Pk, CA 94025 USA
[4] Starkey Hearing Technol, Eden Prairie, MN 55344 USA
来源
2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2018年
基金
美国国家科学基金会;
关键词
COCKTAIL PARTY; ENVIRONMENT;
D O I
暂无
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
In a complex auditory scene comprising multiple sound sources, humans are able to target and track a single speaker. Recent studies have provided promising algorithms to decode the attentional state of a listener in a competingspeaker environment from non-invasive brain recordings such as electroencephalography (EEG). These algorithms require substantial training datasets and often exhibit poor performance at temporal resolutions suitable for real-time implementation, which hinders their utilization in emerging applications such as smart hearing aids. In this work, we propose a realtime attention decoding framework by integrating techniques from Bayesian filtering, l(1)-regularization, state-space modeling, and Expectation Maximization, which is capable of producing robust and statistically interpretable measures of auditory attention at high temporal resolution. Application of our proposed algorithm to synthetic and real EEG data yields a performance close to the state-of-the-art offline methods, while operating in near real-time with a minimal amount of training data.
引用
收藏
页码:25 / 28
页数:4
相关论文
共 16 条
  • [1] Akram S., 2016, IEEE T BIOMED ENG
  • [2] Robust decoding of selective auditory attention from MEG in a competing-speaker environment via state-space modeling
    Akram, Sahar
    Presacco, Alessandro
    Simon, Jonathan Z.
    Shamma, Shihab A.
    Babadi, Behtash
    [J]. NEUROIMAGE, 2016, 124 : 906 - 917
  • [3] Auditory-Inspired Speech Envelope Extraction Methods for Improved EEG-Based Auditory Attention Detection in a Cocktail Party Scenario
    Biesmans, Wouter
    Das, Neetha
    Francart, Tom
    Bertrand, Alexander
    [J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2017, 25 (05) : 402 - 412
  • [5] The effect of head-related filtering and ear-specific decoding bias on auditory attention detection
    Das, Neetha
    Biesmans, Wouter
    Bertrand, Alexander
    Francart, Tom
    [J]. JOURNAL OF NEURAL ENGINEERING, 2016, 13 (05)
  • [6] Emergence of neural encoding of auditory objects while listening to competing speakers
    Ding, Nai
    Simon, Jonathan Z.
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2012, 109 (29) : 11854 - 11859
  • [7] Goldstein T., 2014, A field guide to forward-backward splitting with a FASTA implementation
  • [8] Middlebrooks J.C., 2017, the Springer Handbook of Auditory Research series
  • [9] Miran S., 2018, MATLAB IMPLEMENTATIO
  • [10] Decoding the attended speech stream with multi-channel EEG: implications for online, daily-life applications
    Mirkovic, Bojana
    Debener, Stefan
    Jaeger, Manuela
    De Vos, Maarten
    [J]. JOURNAL OF NEURAL ENGINEERING, 2015, 12 (04)