1. What are the contributions in "A comparison of regularization methods in forward and backward models for auditory attention decoding" ?
In this paper, the authors used AI Machine Perception ( AMP ) for the task of ear detection.
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A Comparison of Temporal Response Function Estimation Methods for Auditory Attention Decoding
Daniel D. E. Wong,Søren A. Fuglsang,Jens Hjortkjær,Enea Ceolini,Malcolm Slaney,Alain de Cheveigné +5 more
TL;DR: A comparative study of the ability of different TRF estimation methods to classify attended speakers from multi-channel EEG data using different performance metrics on a set of labeled EEG data from 18 subjects listening to mixtures of two speech streams.
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Abstract: The decoding of selective auditory attention from noninvasive electroencephalogram (EEG) data is of interest in brain computer interface and auditory perception research. The current state-of-the-art approaches for decoding the attentional selection of listeners are based on temporal response functions (TRFs). In the current context, a TRF is a function that facilitates a mapping between features of sound streams and EEG responses. It has been shown that when the envelope of attended speech and EEG responses are used to derive TRF mapping functions, the TRF model predictions can be used to discriminate between attended and unattended talkers. However, the predictive performance of the TRF models is dependent on how the TRF model parameters are estimated. There exist a number of TRF estimation methods that have been published, along with a variety of datasets. It is currently unclear if any of these methods perform better than others, as they have not yet been compared side by side on a single standardized dataset in a controlled fashion. Here, we present a comparative study of the ability of different TRF estimation methods to classify attended speakers from multi-channel EEG data. The performance of the TRF estimation methods is evaluated using different performance metrics on a set of labeled EEG data from 18 subjects listening to mixtures of two speech streams.
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Figures
FIGURE 4 | Relationship between regression accuracy and classification accuracy, using 30 s decoding segment lengths. 
FIGURE 6 | Wolpaw and Nykopp information transfer rates (ITR) as a function of decoding segment duration for the forward and backward models, using Tikhonov regularization. Notched boxplots show median, and first and third quartiles. Whiskers show 1.5× IQR. Dots indicate outliers.
Citations
Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research
TL;DR: The proposed data-driven method resulted in more accurate short-term temporal response function (TRF) estimates, higher correlations between predicted and actual neural responses, and higher attention decoding accuracies compared to existing TRF-based decoding methods.
25
Low Latency Auditory Attention Detection with Common Spatial Pattern Analysis of EEG Signals.
Siqi Cai,Enze Su,Yonghao Song,Longhan Xie,Haizhou Li +4 more
- 25 Oct 2020
TL;DR: The experiments show that it is possible to decode auditory attention within 2 seconds, with a competitive accuracy of 80.2%, even in noisy acoustic environments, which is encouraging for brain-computer interfaces, and robust AAD in complex acoustic environments.
21
Detecting the locus of auditory attention based on the spectro-spatial-temporal analysis of EEG
Yifan Jiang,Ning Chen,Jing Jin +2 more
TL;DR: The proposed model outperformed SSF-CNN and state-of-the-art AAD models 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.
19
Dynamic estimation of auditory temporal response functions via state-space models with Gaussian mixture process noise.
Sina Miran,Alessandro Presacco,Jonathan Z. Simon,Michael C. Fu,Steven I. Marcus,Behtash Babadi +5 more
TL;DR: In this article, an algorithm based on Expectation-Maximization (EM) was proposed to estimate the process noise parameters from state-space observations, which can capture several key features of biological processes (e.g., brain dynamics) such as abrupt state changes and exogenous processes that affect the states in a structured fashion.
Accurate Modeling of Brain Responses to Speech
TL;DR: This work demonstrates a match-vs-mismatch classification paradigm, in which the classifier must decide which of two stimulus samples produced a given EEG response and which is a randomly chosen stimulus sample, which captures the essential features of the more complex auditory attention decoding (AAD) task explored in many other studies.
8
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