Auditory Attention Detection with EEG Channel Attention

Su, Enze; Cai, Siqi; Li, Peiwen; Xie, Longhan; Li, Haizhou

doi:10.1109/embc46164.2021.9630508

Cited by 5 publications

(13 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the KUL dataset, BSAnet obtains a relatively high ASAD accuracy of 95.2% (SD: 3.08) for 2 s and 93.7% (SD: 4.02) for 1 s of window. These results are consistent with the previous findings that decoding accuracy positively correlates with the decision window size [10], [19], [20], [25], [29], [30]. The ASAD accuracy degrades to 90.3% (SD: 5.19) for a window of 0.5 s. It is worth noting that BSAnet obtains an acceptable ASAD performance (mean: 84.7%, SD: 6.46) despite a very narrow window of 0.25 s or 250 ms.…”

Section: B Size Of Decision Windowsupporting

confidence: 93%

“…The performance of the proposed model is evaluated using fivefold cross-validation (CV) in this study [54]. In accordance with previous studies [20], [28], [29], and [30], the ASAD accuracy is defined as the percentage of correctly classified decision windows on the test set. The average performance of the fivefold validation process is reported as the final results.…”

Section: Network Configurationmentioning

confidence: 93%

“…In a two-speaker scenario, AAD is a binary classification problem [19], [20], [25], [28], [29], [30] that takes a window of EEG data as input and makes a binary decision. An input EEG signal is represented as a sequence of shifting decision windows.…”

Section: B Neural Encodermentioning

confidence: 99%

“…For more information, see https://creativecommons.org/licenses/by/4.0/ stimulus during the decoding, thus improving the practical applicability of auditory attention for neuro-steered hearing applications. In terms of system implementation, the convolutional neural networks (CNNs) [26], [27], one of the popular models in artificial neural networks (ANNs), are among the most effective neural architectures for EEG-based ASAD that achieve the state-of-the-art performance [20], [28], [29], [30]. However, we argue that CNN is not the most suitable neural architecture for EEG signal processing.…”

mentioning

confidence: 99%

See 3 more Smart Citations

A Bio-Inspired Spiking Attentional Neural Network for Attentional Selection in the Listening Brain

Cai

2024

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

Humans show a remarkable ability in solving the cocktail party problem. Decoding auditory attention from the brain signals is a major step toward the development of bionic ears emulating human capabilities. Electroencephalography (EEG)-based auditory attention detection (AAD) has attracted considerable interest recently. Despite much progress, the performance of traditional AAD decoders remains to be improved, especially in low-latency settings. State-of-the-art AAD decoders based on deep neural networks generally lack the intrinsic temporal coding ability in biological networks. In this study, we first propose a bio-inspired spiking attentional neural network, denoted as BSAnet, for decoding auditory attention. BSAnet is capable of exploiting the temporal dynamics of EEG signals using biologically plausible neurons and an attentional mechanism. Experiments on two publicly available datasets confirm the superior performance of BSAnet over other state-of-the-art systems across various evaluation conditions. Moreover, BSAnet imitates realistic brain-like information processing, through which we show the advantage of brain-inspired computational models.

show abstract

Section: B Size Of Decision Windowsupporting

confidence: 93%

Section: Network Configurationmentioning

confidence: 93%

Section: B Neural Encodermentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

A Bio-Inspired Spiking Attentional Neural Network for Attentional Selection in the Listening Brain

Cai

2024

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

show abstract

“…AAD can be achieved based on different cortical activity measurement methods, such as electrocorticography [9], magnetoencephalography [14], and EEG [15][16][17]. Among these measures, the electroencephalography (EEG)-based one has superiority because of its high temporal resolution, noninvasive mode, and great maneuverability [15,[18][19][20][21][22][23]. The traditional linear AAD models based on EEG analysis can be classified into three types: (a) The backward models (also called decoding model), which map from EEG signals to acoustic features [24].…”

Section: Introductionmentioning

confidence: 99%

Detecting the locus of auditory attention based on the spectro-spatial-temporal analysis of EEG

Jiang

Chen²,

Jin³

2022

J. Neural Eng.

View full text Add to dashboard Cite

Objective. Auditory Attention Decoding (AAD) determines which speaker the listener is focusing on by analyzing his/her EEG. CNN was adopted to extract Spectro-Spatial-Feature (SSF) from short-time-interval of EEG to detect auditory spatial attention without stimuli. However, the following factors are not considered in SSF-CNN scheme. i) Single-band frequency analysis cannot represent the EEG pattern precisely. ii) The power cannot represent the EEG feature related to the dynamic patterns of the attended auditory stimulus. iii) The temporal feature of EEG representing the relationship between EEG and attended stimulus is not extracted. To solve these problems, SSF-CNN scheme was modified. Approach. i) Multiple-frequency bands, but not a single alpha frequency band, of EEG, were analyzed to represent the EEG pattern more precisely. ii) Differential Entropy (DE), but not power, was extracted from each frequency band to represent the disorder degree of EEG, which was related to the dynamic patterns of the attended auditory stimulus. iii) CNN and Convolutional-Long-Short-Term-Memory (ConvLSTM) were combined to extract spectro-spatial-temporal features from the 3-D descriptor sequence constructed based on the topographical activity maps of multiple-frequency bands. Main results. Experimental results on KUL, DTU, and PKU with 0.1s, 1s, 2s, and 5s decision windows demonstrated that: i) The proposed model outperformed SSF-CNN and state-of-the-art AAD models. Specifically, when the auditory stimulus was unavailable, AAD accuracy could be enhanced by at least 3:25%, 3:96%, and 5:08% on KUL, DTU, and PKU, respectively, compared with the baselines. And, on KUL, the longer decision window corresponded to lower enhancement, while on both DTU and PKU, the longer decision window corresponded to higher enhancement, except for two cases when decision window length was 2s on PKU or 5s on DTU. ii) Each modification contributed to the performance enhancement. Significance. DE feature, multi-band frequency analysis, and ConvLSTM-based temporal analysis help to enhance AAD accuracy.

show abstract

Attention-guided graph structure learning network for EEG-enabled auditory attention detection

Zeng,

Cai,

Xie

2024

J. Neural Eng.

View full text Add to dashboard Cite

Humans possess the remarkable ability to selectively focus on one sound source in a cocktail party scenario. Decoding auditory attention from brain signals is essential for the development of neuro-steered hearing aids. However, it remains challenging to extract discriminative feature representation from electroencephalography (EEG) signals for auditory attention detection (AAD) tasks, and most methods ignore the intrinsic relationship between different EEG channels. To address these challenges, we propose a novel attention-guided graph structure learning network, AGSLnet, which leverages potential relationships between EEG channels to improve AAD performance. Specifically, AGSLnet is designed to dynamically capture latent relationships between channels and construct a graph structure of EEG signals. We evaluated AGSLnet on two publicly available AAD datasets and demonstrated its superiority and robustness over state-of-the-art models. Furthermore, visualization of the graph structure trained by AGSLnet supports previous neuroscience findings, enhancing our understanding of the underlying neural mechanisms.

show abstract

Auditory Attention Detection with EEG Channel Attention

Cited by 5 publications

References 13 publications

A Bio-Inspired Spiking Attentional Neural Network for Attentional Selection in the Listening Brain

A Bio-Inspired Spiking Attentional Neural Network for Attentional Selection in the Listening Brain

Detecting the locus of auditory attention based on the spectro-spatial-temporal analysis of EEG

Attention-guided graph structure learning network for EEG-enabled auditory attention detection

Contact Info

Product

Resources

About