EEG-based auditory attention decoding using speech-level-based segmented computational models

Wang, Lei1,2; Wu, Ed X.2; Chen, Fei1

2021-08-01

10.1088/1741-2552/abfeba

Journal of Neural Engineering   影响因子和分区

1741-2560

1741-2552

Objective. Auditory attention in complex scenarios can be decoded by electroencephalography (EEG)-based cortical speech-envelope tracking. The relative root-mean-square (RMS) intensity is a valuable cue for the decomposition of speech into distinct characteristic segments. To improve auditory attention decoding (AAD) performance, this work proposed a novel segmented AAD approach to decode target speech envelopes from different RMS-level-based speech segments. Approach. Speech was decomposed into higher- and lower-RMS-level speech segments with a threshold of -10 dB relative RMS level. A support vector machine classifier was designed to identify higher- and lower-RMS-level speech segments, using clean target and mixed speech as reference signals based on corresponding EEG signals recorded when subjects listened to target auditory streams in competing two-speaker auditory scenes. Segmented computational models were developed with the classification results of higher- and lower-RMS-level speech segments. Speech envelopes were reconstructed based on segmented decoding models for either higher- or lower-RMS-level speech segments. AAD accuracies were calculated according to the correlations between actual and reconstructed speech envelopes. The performance of the proposed segmented AAD computational model was compared to those of traditional AAD methods with unified decoding functions. Main results. Higher- and lower-RMS-level speech segments in continuous sentences could be identified robustly with classification accuracies that approximated or exceeded 80% based on corresponding EEG signals at 6 dB, 3 dB, 0 dB, -3 dB and -6 dB signal-to-mask ratios (SMRs). Compared with unified AAD decoding methods, the proposed segmented AAD approach achieved more accurate results in the reconstruction of target speech envelopes and in the detection of attentional directions. Moreover, the proposed segmented decoding method had higher information transfer rates (ITRs) and shorter minimum expected switch times compared with the unified decoder. Significance. This study revealed that EEG signals may be used to classify higher- and lower-RMS-level-based speech segments across a wide range of SMR conditions (from 6 dB to -6 dB). A novel finding was that the specific information in different RMS-level-based speech segments facilitated EEG-based decoding of auditory attention. The significantly improved AAD accuracies and ITRs of the segmented decoding method suggests that this proposed computational model may be an effective method for the application of neuro-controlled brain-computer interfaces in complex auditory scenes.

EEG auditory attention decoding (AAD) RMS-level-based speech segments signal-to-mask ratio (SMR) temporal response function (TRF) support vector machine (SVM) machine learning

SCI ; EI

英语

第一 ; 通讯

National Natural Science Foundation of China[61971212] ; Basic Research Foundation of Shenzhen["KQJSCX20180319114453986","GJHZ20180928155002157"] ; High-level University Fund of Southern University of Science and Technology[G02236002]

Engineering ; Neurosciences & Neurology

Engineering, Biomedical ; Neurosciences

WOS:000655389700001

IOP PUBLISHING LTD

20212310450283

Biomedical signal processing ; Brain computer interface ; Computation theory ; Computational methods ; Decoding ; Electroencephalography ; Electrophysiology ; Speech ; Speech analysis ; Speech communication ; Support vector machines

Biomedical Engineering:461.1 ; Medicine and Pharmacology:461.6 ; Information Theory and Signal Processing:716.1 ; Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1 ; Computer Peripheral Equipment:722.2 ; Computer Software, Data Handling and Applications:723 ; Data Processing and Image Processing:723.2 ; Speech:751.5

Web of Science

1.Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen, Peoples R China
2.Univ Hong Kong, Dept Elect & Elect Engn, Hong Kong, Peoples R China

Wang, Lei,Wu, Ed X.,Chen, Fei. EEG-based auditory attention decoding using speech-level-based segmented computational models[J]. Journal of Neural Engineering,2021,18(4).

Wang, Lei,Wu, Ed X.,&Chen, Fei.(2021).EEG-based auditory attention decoding using speech-level-based segmented computational models.Journal of Neural Engineering,18(4).

Wang, Lei,et al."EEG-based auditory attention decoding using speech-level-based segmented computational models".Journal of Neural Engineering 18.4(2021).