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

Miran, Sina; Akram, Sahar; Sheikhattar, Alireza; Simon, Jonathan Z.; Zhang, Tao; Babadi, Behtash

doi:10.1109/embc.2018.8512210

Cited by 5 publications

(6 citation statements)

References 13 publications

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“…A high similarity (correlation) indicates a good performance of the model. Two other approaches can also be mentioned: Canonical Correlation (Miran et al, 2018b), it is composed of three modules: a dynamic encoder/decoder estimation module, an attention marker extraction module, and a real-time state-space estimator module (see Miran et al, 2018a for a complete description of the model) and this approach was developed in the purpose of real-time decoding of auditory attention.…”

Section: Linear Modelsmentioning

confidence: 99%

EEG-Based Auditory Attention Detection and Its Possible Future Applications for Passive BCI

Belo

Clerc

Schön

2021

Front. Comput. Sci.

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The ability to discriminate and attend one specific sound source in a complex auditory environment is a fundamental skill for efficient communication. Indeed, it allows us to follow a family conversation or discuss with a friend in a bar. This ability is challenged in hearing-impaired individuals and more precisely in those with a cochlear implant (CI). Indeed, due to the limited spectral resolution of the implant, auditory perception remains quite poor in a noisy environment or in presence of simultaneous auditory sources. Recent methodological advances allow now to detect, on the basis of neural signals, which auditory stream within a set of multiple concurrent streams an individual is attending to. This approach, called EEG-based auditory attention detection (AAD), is based on fundamental research findings demonstrating that, in a multi speech scenario, cortical tracking of the envelope of the attended speech is enhanced compared to the unattended speech. Following these findings, other studies showed that it is possible to use EEG/MEG (Electroencephalography/Magnetoencephalography) to explore auditory attention during speech listening in a Cocktail-party-like scenario. Overall, these findings make it possible to conceive next-generation hearing aids combining customary technology and AAD. Importantly, AAD has also a great potential in the context of passive BCI, in the educational context as well as in the context of interactive music performances. In this mini review, we firstly present the different approaches of AAD and the main limitations of the global concept. We then expose its potential applications in the world of non-clinical passive BCI.

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Section: Linear Modelsmentioning

confidence: 99%

EEG-Based Auditory Attention Detection and Its Possible Future Applications for Passive BCI

Belo

Clerc

Schön

2021

Front. Comput. Sci.

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“…The neuromarkers extracted were then fed into a near real-time state-space estimator that translated them to robust and statistically interpretable estimates of the attentional state with a minimal delay [10], [11]. The forward lag was set at ~1.5 seconds, with the backward lag at ~13.5 seconds.…”

Section: Bayesian Filteringmentioning

confidence: 99%

“…The forgetting factor was set at 0.95, while the regularization parameter was set at 0.001. More details of this algorithm can be found in [10], [11].…”

Section: Bayesian Filteringmentioning

confidence: 99%

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Real-Time Tracking of Magnetoencephalographic Neuromarkers during a Dynamic Attention-Switching Task

Presacco

Miran

Babadi

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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In the last few years, a large number of experiments have been focused on exploring the possibility of using non-invasive techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), to identify auditory-related neuromarkers which are modulated by attention. Results from several studies where participants listen to a story narrated by one speaker, while trying to ignore a different story narrated by a competing speaker, suggest the feasibility of extracting neuromarkers that demonstrate enhanced phase locking to the attended speech stream. These promising findings have the potential to be used in clinical applications, such as EEG-driven hearing aids. One major challenge in achieving this goal is the need to devise an algorithm capable of tracking these neuromarkers in real-time when individuals are given the freedom to repeatedly switch attention among speakers at will. Here we present an algorithm pipeline that is designed to efficiently recognize changes of neural speech tracking during a dynamic-attention switching task and to use them as an input for a near real-time state-space model that translates these neuromarkers into attentional state estimates with a minimal delay. This algorithm pipeline was tested with MEG data collected from participants who had the freedom to change the focus of their attention between two speakers at will. Results suggest the feasibility of using our algorithm pipeline to track changes of attention in near-real time in a dynamic auditory scene.

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“…However, this method was based on the semi-classical signal analysis (SCSA), which is young and needs to be studied actively to acquire better performance. Bayesian filtering ( Miran et al, 2018 ) was a different filtering method that decoded real-time auditory attention from EEG and alleviated the need for large training datasets compared with other existing methods. This method is complicated in application to some extent.…”

Section: Introductionmentioning

confidence: 99%

A Novel Two-Stage Refine Filtering Method for EEG-Based Motor Imagery Classification

et al. 2021

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Cerebral stroke is a common disease across the world, and it is a promising method to recognize the intention of stroke patients with the help of brain–computer interface (BCI). In the field of motor imagery (MI) classification, appropriate filtering is vital for feature extracting of electroencephalogram (EEG) signals and consequently influences the accuracy of MI classification. In this case, a novel two-stage refine filtering method was proposed, inspired by Gradient-weighted Class Activation Mapping (Grad-CAM), which uses the gradients of any target concept flowing into the final convolutional layer to highlight the important part of training data for predicting the concept. In the first stage, MI classification was carried out and then the frequency band to be filtered was calculated according to the Grad-CAM of the MI classification results. In the second stage, EEG was filtered and classified for a higher classification accuracy. To evaluate the filtering effect, this method was applied to the multi-branch neural network proposed in our previous work. Experiment results revealed that the proposed method reached state-of-the-art classification kappa value levels and acquired at least 3% higher kappa values than other methods This study also proposed some promising application scenarios with this filtering method.

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

Cited by 5 publications

References 13 publications

EEG-Based Auditory Attention Detection and Its Possible Future Applications for Passive BCI

EEG-Based Auditory Attention Detection and Its Possible Future Applications for Passive BCI

Real-Time Tracking of Magnetoencephalographic Neuromarkers during a Dynamic Attention-Switching Task

A Novel Two-Stage Refine Filtering Method for EEG-Based Motor Imagery Classification

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