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Journal articles on the topic 'Auditory attention decoding'

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Author: Grafiati
Published: 1 June 2024

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1

Han, Cong, James O’Sullivan, Yi Luo, Jose Herrero, Ashesh D. Mehta, and Nima Mesgarani. "Speaker-independent auditory attention decoding without access to clean speech sources." Science Advances 5, no. 5 (May 2019): eaav6134. http://dx.doi.org/10.1126/sciadv.aav6134.

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Speech perception in crowded environments is challenging for hearing-impaired listeners. Assistive hearing devices cannot lower interfering speakers without knowing which speaker the listener is focusing on. One possible solution is auditory attention decoding in which the brainwaves of listeners are compared with sound sources to determine the attended source, which can then be amplified to facilitate hearing. In realistic situations, however, only mixed audio is available. We utilize a novel speech separation algorithm to automatically separate speakers in mixed audio, with no need for the speakers to have prior training. Our results show that auditory attention decoding with automatically separated speakers is as accurate and fast as using clean speech sounds. The proposed method significantly improves the subjective and objective quality of the attended speaker. Our study addresses a major obstacle in actualization of auditory attention decoding that can assist hearing-impaired listeners and reduce listening effort for normal-hearing subjects.
2

Aldag, Nina, Andreas Büchner, Thomas Lenarz, and Waldo Nogueira. "Towards decoding selective attention through cochlear implant electrodes as sensors in subjects with contralateral acoustic hearing." Journal of Neural Engineering 19, no. 1 (February 1, 2022): 016023. http://dx.doi.org/10.1088/1741-2552/ac4de6.

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Abstract Objectives. Focusing attention on one speaker in a situation with multiple background speakers or noise is referred to as auditory selective attention. Decoding selective attention is an interesting line of research with respect to future brain-guided hearing aids or cochlear implants (CIs) that are designed to adaptively adjust sound processing through cortical feedback loops. This study investigates the feasibility of using the electrodes and backward telemetry of a CI to record electroencephalography (EEG). Approach. The study population included six normal-hearing (NH) listeners and five CI users with contralateral acoustic hearing. Cortical auditory evoked potentials (CAEP) and selective attention were recorded using a state-of-the-art high-density scalp EEG and, in the case of CI users, also using two CI electrodes as sensors in combination with the backward telemetry system of these devices, denoted as implant-based EEG (iEEG). Main results. In the selective attention paradigm with multi-channel scalp EEG the mean decoding accuracy across subjects was 94.8% and 94.6% for NH listeners and CI users, respectively. With single-channel scalp EEG the accuracy dropped but was above chance level in 8–9 out of 11 subjects, depending on the electrode montage. With the single-channel iEEG, the selective attention decoding accuracy could only be analyzed in two out of five CI users due to a loss of data in the other three subjects. In these two CI users, the selective attention decoding accuracy was above chance level. Significance. This study shows that single-channel EEG is suitable for auditory selective attention decoding, even though it reduces the decoding quality compared to a multi-channel approach. CI-based iEEG can be used for the purpose of recording CAEPs and decoding selective attention. However, the study also points out the need for further technical development for the CI backward telemetry regarding long-term recordings and the optimal sensor positions.
3

Geirnaert, Simon, Servaas Vandecappelle, Emina Alickovic, Alain de Cheveigne, Edmund Lalor, Bernd T. Meyer, Sina Miran, Tom Francart, and Alexander Bertrand. "Electroencephalography-Based Auditory Attention Decoding: Toward Neurosteered Hearing Devices." IEEE Signal Processing Magazine 38, no. 4 (July 2021): 89–102. http://dx.doi.org/10.1109/msp.2021.3075932.

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4

Fu, Zhen, Xihong Wu, and Jing Chen. "Congruent audiovisual speech enhances auditory attention decoding with EEG." Journal of Neural Engineering 16, no. 6 (November 6, 2019): 066033. http://dx.doi.org/10.1088/1741-2552/ab4340.

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5

Straetmans, L., B. Holtze, S. Debener, M. Jaeger, and B. Mirkovic. "Neural tracking to go: auditory attention decoding and saliency detection with mobile EEG." Journal of Neural Engineering 18, no. 6 (December 1, 2021): 066054. http://dx.doi.org/10.1088/1741-2552/ac42b5.

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Abstract Objective. Neuro-steered assistive technologies have been suggested to offer a major advancement in future devices like neuro-steered hearing aids. Auditory attention decoding (AAD) methods would in that case allow for identification of an attended speaker within complex auditory environments, exclusively from neural data. Decoding the attended speaker using neural information has so far only been done in controlled laboratory settings. Yet, it is known that ever-present factors like distraction and movement are reflected in the neural signal parameters related to attention. Approach. Thus, in the current study we applied a two-competing speaker paradigm to investigate performance of a commonly applied electroencephalography-based AAD model outside of the laboratory during leisure walking and distraction. Unique environmental sounds were added to the auditory scene and served as distractor events. Main results. The current study shows, for the first time, that the attended speaker can be accurately decoded during natural movement. At a temporal resolution of as short as 5 s and without artifact attenuation, decoding was found to be significantly above chance level. Further, as hypothesized, we found a decrease in attention to the to-be-attended and the to-be-ignored speech stream after the occurrence of a salient event. Additionally, we demonstrate that it is possible to predict neural correlates of distraction with a computational model of auditory saliency based on acoustic features. Significance. Taken together, our study shows that auditory attention tracking outside of the laboratory in ecologically valid conditions is feasible and a step towards the development of future neural-steered hearing aids.
6

Facoetti, Andrea, Anna Noemi Trussardi, Milena Ruffino, Maria Luisa Lorusso, Carmen Cattaneo, Raffaella Galli, Massimo Molteni, and Marco Zorzi. "Multisensory Spatial Attention Deficits Are Predictive of Phonological Decoding Skills in Developmental Dyslexia." Journal of Cognitive Neuroscience 22, no. 5 (May 2010): 1011–25. http://dx.doi.org/10.1162/jocn.2009.21232.

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Although the dominant approach posits that developmental dyslexia arises from deficits in systems that are exclusively linguistic in nature (i.e., phonological deficit theory), dyslexics show a variety of lower level deficits in sensory and attentional processing. Although their link to the reading disorder remains contentious, recent empirical and computational studies suggest that spatial attention plays an important role in phonological decoding. The present behavioral study investigated exogenous spatial attention in dyslexic children and matched controls by measuring RTs to visual and auditory stimuli in cued-detection tasks. Dyslexics with poor nonword decoding accuracy showed a slower time course of visual and auditory (multisensory) spatial attention compared with both chronological age and reading level controls as well as compared with dyslexics with slow but accurate nonword decoding. Individual differences in the time course of multisensory spatial attention accounted for 31% of unique variance in the nonword reading performance of the entire dyslexic sample after controlling for age, IQ, and phonological skills. The present study suggests that multisensory “sluggish attention shifting”—related to a temporoparietal dysfunction—selectively impairs the sublexical mechanisms that are critical for reading development. These findings may offer a new approach for early identification and remediation of developmental dyslexia.
7

Xu, Zihao, Yanru Bai, Ran Zhao, Qi Zheng, Guangjian Ni, and Dong Ming. "Auditory attention decoding from EEG-based Mandarin speech envelope reconstruction." Hearing Research 422 (September 2022): 108552. http://dx.doi.org/10.1016/j.heares.2022.108552.

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8

Aroudi, Ali, and Simon Doclo. "Cognitive-Driven Binaural Beamforming Using EEG-Based Auditory Attention Decoding." IEEE/ACM Transactions on Audio, Speech, and Language Processing 28 (2020): 862–75. http://dx.doi.org/10.1109/taslp.2020.2969779.

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9

Aroudi, Ali, Eghart Fischer, Maja Serman, Henning Puder, and Simon Doclo. "Closed-Loop Cognitive-Driven Gain Control of Competing Sounds Using Auditory Attention Decoding." Algorithms 14, no. 10 (September 30, 2021): 287. http://dx.doi.org/10.3390/a14100287.

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Recent advances have shown that it is possible to identify the target speaker which a listener is attending to using single-trial EEG-based auditory attention decoding (AAD). Most AAD methods have been investigated for an open-loop scenario, where AAD is performed in an offline fashion without presenting online feedback to the listener. In this work, we aim at developing a closed-loop AAD system that allows to enhance a target speaker, suppress an interfering speaker and switch attention between both speakers. To this end, we propose a cognitive-driven adaptive gain controller (AGC) based on real-time AAD. Using the EEG responses of the listener and the speech signals of both speakers, the real-time AAD generates probabilistic attention measures, based on which the attended and the unattended speaker are identified. The AGC then amplifies the identified attended speaker and attenuates the identified unattended speaker, which are presented to the listener via loudspeakers. We investigate the performance of the proposed system in terms of the decoding performance and the signal-to-interference ratio (SIR) improvement. The experimental results show that, although there is a significant delay to detect attention switches, the proposed system is able to improve the SIR between the attended and the unattended speaker. In addition, no significant difference in decoding performance is observed between closed-loop AAD and open-loop AAD. The subjective evaluation results show that the proposed closed-loop cognitive-driven system demands a similar level of cognitive effort to follow the attended speaker, to ignore the unattended speaker and to switch attention between both speakers compared to using open-loop AAD. Closed-loop AAD in an online fashion is feasible and enables the listener to interact with the AGC.
10

Wang, Lei, Ed X. Wu, and Fei Chen. "EEG-based auditory attention decoding using speech-level-based segmented computational models." Journal of Neural Engineering 18, no. 4 (May 25, 2021): 046066. http://dx.doi.org/10.1088/1741-2552/abfeba.

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11

Lu, Yun, Mingjiang Wang, Longxin Yao, Hongcai Shen, Wanqing Wu, Qiquan Zhang, Lu Zhang, et al. "Auditory attention decoding from electroencephalography based on long short-term memory networks." Biomedical Signal Processing and Control 70 (September 2021): 102966. http://dx.doi.org/10.1016/j.bspc.2021.102966.

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12

Yufei, Wu, Wang Dandan, and Zhu Yanwei. "Research on the Advantages of Digital Sensor Equipment in Language Audio-Visual and Oral Teaching." Journal of Sensors 2021 (November 30, 2021): 1–13. http://dx.doi.org/10.1155/2021/3006397.

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Digital sensors use biotechnology and information processing technology to strengthen the processing of relevant visual and auditory information, which is helpful to ensure that the receiver can obtain more accurate information, so as to improve the learning effect and reduce the impact on the environment. This paper designs an experiment to explore the role of digital sensors in language audio-visual teaching, which provides a reference for the application of digital sensors in the future. The impulse response function in sensor technology is introduced. The speech time domain envelope and time-varying mouth area of the sensor device are calculated. The auditory attention transfer detection based on line of sight rotation estimation is carried out through the auditory attention decoding fusion technology and the sensor auditory attention conversion detection method. At the same time, the characteristic of sensor heog signal is analyzed. The results show that the algorithm proposed in this paper has good results.
13

Treder, M. S., H. Purwins, D. Miklody, I. Sturm, and B. Blankertz. "Decoding auditory attention to instruments in polyphonic music using single-trial EEG classification." Journal of Neural Engineering 11, no. 2 (March 10, 2014): 026009. http://dx.doi.org/10.1088/1741-2560/11/2/026009.

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14

Etard, Octave, Mikolaj Kegler, Chananel Braiman, Antonio Elia Forte, and Tobias Reichenbach. "Decoding of selective attention to continuous speech from the human auditory brainstem response." NeuroImage 200 (October 2019): 1–11. http://dx.doi.org/10.1016/j.neuroimage.2019.06.029.

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15

Das, Neetha, Wouter Biesmans, Alexander Bertrand, and Tom Francart. "The effect of head-related filtering and ear-specific decoding bias on auditory attention detection." Journal of Neural Engineering 13, no. 5 (September 13, 2016): 056014. http://dx.doi.org/10.1088/1741-2560/13/5/056014.

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16

Aroudi, Ali, Bojana Mirkovic, Maarten De Vos, and Simon Doclo. "Impact of Different Acoustic Components on EEG-Based Auditory Attention Decoding in Noisy and Reverberant Conditions." IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, no. 4 (April 2019): 652–63. http://dx.doi.org/10.1109/tnsre.2019.2903404.

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17

Graña, Gilberto David, Cyrus P. Billimoria, and Kamal Sen. "Analyzing Variability in Neural Responses to Complex Natural Sounds in the Awake Songbird." Journal of Neurophysiology 101, no. 6 (June 2009): 3147–57. http://dx.doi.org/10.1152/jn.90917.2008.

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Studies of auditory processing in awake, behaving songbirds allow for the possibility of new classes of experiments, including those involving attention and plasticity. Detecting and determining the significance of plasticity, however, requires assessing the intrinsic variability in neural responses. Effects such as rapid plasticity have been investigated in the auditory system through the use of the spectrotemporal receptive field (STRF), a characterization of the properties of sounds to which a neuron best responds. Here we investigated neural response variability in awake recordings obtained from zebra finch field L, the analog of the primary auditory cortex. To quantify the level of variability in the neural recordings, we used three similarity measures: an STRF-based metric, a spike-train correlation-based metric, and a spike-train discrimination-based metric. We then extracted a number of parameters from these measures, quantifying how they fluctuated over time. Our results indicate that 1) awake responses are quite stable over time; 2) the different measures of response are complementary—specifically, the spike-train–based measures yield new information complementary to the STRF; and 3) different STRF parameters show distinct levels of variability. These results provide critical constraints for the design of robust decoding strategies and novel experiments on attention and plasticity in the awake songbird.
18

Xu, Zihao, Yanru Bai, Ran Zhao, Hongmei Hu, Guangjian Ni, and Dong Ming. "Corrigendum to “Decoding selective auditory attention with EEG using a transformer model” [Methods 204 (2022) 410–417]." Methods 205 (September 2022): 157. http://dx.doi.org/10.1016/j.ymeth.2022.07.008.

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19

Miran, Sina, Sahar Akram, Alireza Sheikhattar, Jonathan Z. Simon, Tao Zhang, and Behtash Babadi. "Robust and real-time decoding of selective auditory attention from M/EEG: A state-space modeling approach." Journal of the Acoustical Society of America 143, no. 3 (March 2018): 1743. http://dx.doi.org/10.1121/1.5035690.

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20

Geirnaert, Simon, Tom Francart, and Alexander Bertrand. "An Interpretable Performance Metric for Auditory Attention Decoding Algorithms in a Context of Neuro-Steered Gain Control." IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 1 (January 2020): 307–17. http://dx.doi.org/10.1109/tnsre.2019.2952724.

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21

Akram, Sahar, Alessandro Presacco, Jonathan Z. Simon, Shihab A. Shamma, and Behtash Babadi. "Robust decoding of selective auditory attention from MEG in a competing-speaker environment via state-space modeling." NeuroImage 124 (January 2016): 906–17. http://dx.doi.org/10.1016/j.neuroimage.2015.09.048.

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22

Schäfer, Patrick J., Farah I. Corona-Strauss, Ronny Hannemann, Steven A. Hillyard, and Daniel J. Strauss. "Testing the Limits of the Stimulus Reconstruction Approach: Auditory Attention Decoding in a Four-Speaker Free Field Environment." Trends in Hearing 22 (January 2018): 233121651881660. http://dx.doi.org/10.1177/2331216518816600.

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23

Majerus, Steve, Frédéric Péters, Marion Bouffier, Nelson Cowan, and Christophe Phillips. "The Dorsal Attention Network Reflects Both Encoding Load and Top–down Control during Working Memory." Journal of Cognitive Neuroscience 30, no. 2 (February 2018): 144–59. http://dx.doi.org/10.1162/jocn_a_01195.

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The dorsal attention network is consistently involved in verbal and visual working memory (WM) tasks and has been associated with task-related, top–down control of attention. At the same time, WM capacity has been shown to depend on the amount of information that can be encoded in the focus of attention independently of top–down strategic control. We examined the role of the dorsal attention network in encoding load and top–down memory control during WM by manipulating encoding load and memory control requirements during a short-term probe recognition task for sequences of auditory (digits, letters) or visual (lines, unfamiliar faces) stimuli. Encoding load was manipulated by presenting sequences with small or large sets of memoranda while maintaining the amount of sensory stimuli constant. Top–down control was manipulated by instructing participants to passively maintain all stimuli or to selectively maintain stimuli from a predefined category. By using ROI and searchlight multivariate analysis strategies, we observed that the dorsal attention network encoded information for both load and control conditions in verbal and visuospatial modalities. Decoding of load conditions was in addition observed in modality-specific sensory cortices. These results highlight the complexity of the role of the dorsal attention network in WM by showing that this network supports both quantitative and qualitative aspects of attention during WM encoding, and this is in a partially modality-specific manner.
24

Xi, Jie, Hongkai Xu, Ying Zhu, Linjun Zhang, Hua Shu, and Yang Zhang. "Categorical Perception of Chinese Lexical Tones by Late Second Language Learners With High Proficiency: Behavioral and Electrophysiological Measures." Journal of Speech, Language, and Hearing Research 64, no. 12 (December 13, 2021): 4695–704. http://dx.doi.org/10.1044/2021_jslhr-20-00210.

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Purpose: Although acquisition of Chinese lexical tones by second language (L2) learners has been intensively investigated, very few studies focused on categorical perception (CP) of lexical tones by highly proficient L2 learners. This study was designed to address this issue with behavioral and electrophysiological measures. Method: Behavioral identification and auditory event-related potential (ERP) components for speech discrimination, including mismatch negativity (MMN), N2b, and P3b, were measured in 23 native Korean speakers who were highly proficient late L2 learners of Chinese. For the ERP measures, both passive and active listening tasks were administered to examine the automatic and attention-controlled discriminative responses to within- and across-category differences for carefully chosen stimuli from a lexical tone continuum. Results: The behavioral task revealed native-like identification function of the tonal continuum. Correspondingly, the active oddball task demonstrated larger P3b amplitudes for the across-category than within-category deviants in the left recording site, indicating clear CP of lexical tones in the attentive condition. By contrast, similar MMN responses in the right recording site were elicited by both the across- and within-category deviants, indicating the absence of CP effect with automatic phonological processing of lexical tones at the pre-attentive stage even in L2 learners with high Chinese proficiency. Conclusion: Although behavioral data showed clear evidence of categorical perception of lexical tones in proficient L2 learners, ERP measures from passive and active listening tasks demonstrated fine-grained sensitivity in terms of response polarity, latency, and laterality in revealing different aspects of auditory versus linguistic processing associated with speech decoding by means of largely implicit native language acquisition versus effortful explicit L2 learning.
25

Chouiter, Leila, Athina Tzovara, Sebastian Dieguez, Jean-Marie Annoni, David Magezi, Marzia De Lucia, and Lucas Spierer. "Experience-based Auditory Predictions Modulate Brain Activity to Silence as Do Real Sounds." Journal of Cognitive Neuroscience 27, no. 10 (October 2015): 1968–80. http://dx.doi.org/10.1162/jocn_a_00835.

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Interactions between stimuli's acoustic features and experience-based internal models of the environment enable listeners to compensate for the disruptions in auditory streams that are regularly encountered in noisy environments. However, whether auditory gaps are filled in predictively or restored a posteriori remains unclear. The current lack of positive statistical evidence that internal models can actually shape brain activity as would real sounds precludes accepting predictive accounts of filling-in phenomenon. We investigated the neurophysiological effects of internal models by testing whether single-trial electrophysiological responses to omitted sounds in a rule-based sequence of tones with varying pitch could be decoded from the responses to real sounds and by analyzing the ERPs to the omissions with data-driven electrical neuroimaging methods. The decoding of the brain responses to different expected, but omitted, tones in both passive and active listening conditions was above chance based on the responses to the real sound in active listening conditions. Topographic ERP analyses and electrical source estimations revealed that, in the absence of any stimulation, experience-based internal models elicit an electrophysiological activity different from noise and that the temporal dynamics of this activity depend on attention. We further found that the expected change in pitch direction of omitted tones modulated the activity of left posterior temporal areas 140–200 msec after the onset of omissions. Collectively, our results indicate that, even in the absence of any stimulation, internal models modulate brain activity as do real sounds, indicating that auditory filling in can be accounted for by predictive activity.
26

Хомякова, Т. В. "The Peculiarities of Hidden Sense Decoding by Young Schoolchildren with Alalia." Психолого-педагогический поиск, no. 2(54) (October 23, 2020): 195–201. http://dx.doi.org/10.37724/rsu.2020.54.2.020.

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В статье описываются результаты научного исследования по выявлению у детей младшего школьного возраста с речевой патологией специфических трудностей декодирования речевой продукции, включающей скрытый смысл. Констатирующий эксперимент охватывал две группы учащихся: контрольную и экспериментальную. Младшим школьникам с недоразвитием речи были предложены два вида заданий, каждое их которых направлено на выявление специфических ошибок и трудностей. Предъявляемые задания экспериментального исследования разрабатывались с учетом особенностей внимания, слухоречевой памяти, речевого восприятия и психолингвистических возможностей участников эксперимента. Полученные в ходе исследовательской работы данные свидетельствуют о невысоком уровне развития языкового мышления учащихся с речевой патологией, о наличии дефицитарности комплекса логико-грамматических правил, участвующих в дешифровке скрытого смысла. Задача данной работы заключается в том, чтобы отразить выявленные специфические трудности и определить систему коррекционно-логопедического воздействия по их устранению. На базе проведенного исследования нами была разработана методика по развитию логико-грамматических конструкций языка и дешифровке скрытого смысла текстовых высказываний для младших школьников с речевой патологией, обучающихся по общеобразовательным программам The article describes the findings of a research aimed at the investigation of challenges experienced by young schoolchildren with speech disorders when decoding hidden senses. The ascertaining experiment involved two groups of learners (a control group and an experimental group). Young schoolchildren with alalia were asked to perform two types of tasks whose aim was to identify specific mistakes and challenges. Preparing the tasks for the experiment, the author took into consideration the peculiarities of children’s attention, the peculiarities of their auditory memory, speech perception, psycholinguistic abilities. The findings of the research show that speech disorders such as alalia lead to speech underdevelopment, deficient lexical-grammatical abilities, difficulties of hidden sense recognition. The aim of the article is to describe the specific challenges and to elaborate a system of correctional treatment. The findings of the research have enabled us to develop a correctional strategy for improving young alalic learners’ logic and grammar, their ability to decipher hidden senses.
27

Hou, Yao, Rongnian Tang, and Xiaofeng Xie. "A Decoding Method Using Riemannian Local Linear Feature Construction for a Lower-Limb Motor Imagery Brain–Computer Interface System." Electronics 12, no. 22 (November 18, 2023): 4697. http://dx.doi.org/10.3390/electronics12224697.

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Recently, motor imagery brain–computer interfaces (BCIs) have been developed for use in motor function assistance and rehabilitation engineering. In particular, lower-limb motor imagery BCI systems are receiving increasing attention in the field of motor rehabilitation, because these systems could accurately and rapidly identify a patient’s lower-limb movement intention, which could improve the practicability of the motor rehabilitation. In this study, a novel lower-limb BCI system combining visual stimulation, auditory stimulation, functional electrical stimulation, and proprioceptive stimulation was designed to assist patients in lower-limb rehabilitation training. In addition, the Riemannian local linear feature construction (RLLFC) algorithm is proposed to improve the performance of decoding by using unsupervised basis learning and representation weight calculation in the motor imagery BCI system. Three in-house experiment were performed to demonstrate the effectiveness of the proposed system in comparison with other state-of-the-art methods. The experimental results indicate that the proposed system can learn low-dimensional features and correctly characterize the relationship between the testing trial and its k-nearest neighbors.
28

Taranenko, Larysa, and Svitlana Aleksenko. "Prosodic means’ interaction in realising the anecdote humorous effect." Revista Amazonia Investiga 11, no. 59 (December 15, 2022): 172–83. http://dx.doi.org/10.34069/ai/2022.59.11.16.

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In the paper, on the basis of auditory analysis of English spoken anecdotes the authors come up with the system of prosodic means that serve to create the text humorous effect. To define the specificity of a complex interaction of emotional, pragmatic, structural and semantic factors of prosodic means’ functioning in English anecdotes, we substantiated two algorithmic models presenting the text story-line development: one being similar to the structure of the fairy tale (introduction → commentary → code), and the other one resembling the riddle (topic → commentary → code). By way of using these models as well as the traditional method of linguistic interpretation of the auditory analysis results, the authors substantiate the specificity of prosodic, lexico-grammatical and stylistic means interaction of an English anecdote oral actualisation functioning within its structural components. It has been found out that realisation of the anecdote humorous effect is ensured by the predominance of the unidirectional functioning of the language means of all levels with the leading role of prosodic means aimed at drawing the listeners’ attention to the anecdote’s two-plane semantics and its key lexical units, thus stimulating their thinking activities while decoding the humour of the anecdote. The authors come to the conclusion that the application of a functional-and-energetic approach to the study of a complex interaction of emotional, pragmatic, semantic and structural factors makes it possible to present a comprehensive description of invariant and variant prosodic patterns of any type of texts.
29

Slaney, Malcolm, and Richard F. Lyon. "Speech and hearing for the next billion users." Journal of the Acoustical Society of America 150, no. 4 (October 2021): A347. http://dx.doi.org/10.1121/10.0008539.

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Speech recognition has not withered in the years since John Pierce's 1969 editorial. Far from it, speech recognition is everywhere, and now works very reliably, even when speaking to a speech assistant in another room with music playing in the background. If the current speech technology is widely available to billions of users, then the next frontier is to provide the same technology to the next billion users (NBUs). In addition, all of us have limited capabilities to speak and understand the acoustic world around us, whether it rises to the level to be labeled a real disability, or arises due to fatigue or being distracted. The next frontier for speech and hearing is to accommodate all of our needs, disabled or not as they arise in the real world, for not just the first billion users, but the next billion, and the next billion after that. The available technologies include real-time speech recognition (and translation) for the hard of hearing, real-time speech enhancement, and auditory attention decoding from EEG signals.
30

Liu, Mengmeng, Jianling Tan, and Yin Tian. "Decoding Auditory Attentional States by a 3D Convolutional Neural Network Model." International Journal of Psychophysiology 168 (October 2021): S134—S135. http://dx.doi.org/10.1016/j.ijpsycho.2021.07.387.

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Tychinina, Alyona R., and Nataliia V. Nikoriak. "V. DOMONTOVYCH’S SHORT STORY “THIRST FOR MUSIC” IN THE ASPECT OF INTERDISCURSIVE METHODOLOGY." Alfred Nobel University Journal of Philology 1, no. 25 (May 30, 2023): 131–43. http://dx.doi.org/10.32342/2523-4463-2023-1-25-10.

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The current postnonclassical methodological situation draws attention to interdisciplinary practices in literary texts analysis, revealing a significant number of “interdiscursive configurations”. The purpose of this research is to analyze the short story “Thirst for Music” by one of the great intellectuals of the Ukrainian emigration, Viktor Petrov-Domontovych (1894–1969), in the aspect of interdisciplinary methodology. The research tasks are to outline the specifics of interdiscursive methodology and interdiscursive analysis of a literary text, in order to identify V. Domontovych’s novel interdiscursive codes. The chosen short story determines the author’s idiom: biographism, fragmentation, intermediality, intertextuality. Accordingly, the leading methodology of the study is interdiscursivity. It involves the use of biographical, hermeneutical, intertextual, and intermedial research methods. The study is based on the research of M. Foucault, V. Cherniavska, Y. Shevelev, I. Ilyin and others. The work outlines a set of discourses important for the general concept of the novel and evaluates their interaction in the discursive polyphony of “Thirst for Music”: biographical (a fragment of Rilke’s biography), intermedial (music, sculpture), intertextual (Rainer Maria Rilke’s Stories of the Good God, Rilke’s correspondence with Magda von Huttingberg), and architectural (Biographical novella fragment). This example convincingly proves that postmodernist methodology is productive in analyzing the literature of another cultural epoch, in this case, the modernist one. The article under studies focuses on the influence of postmodernism on literary methodology in terms of the concept of interdiscursivity. The purpose of the interdiscursive analysis is the reconstruction of all the discursive layers involved (hidden) by the author. The methodology suggests the identification of a broad range of significant bibliographical, cultural, artistic (intermedial and intertextual) architextual insertions and allusions. Through decoding the “interdiscursive configurations”, the article lays particular emphasis on the bibliographical, intertextual, intermedial, and narrative specifics of the text by the Ukrainian emigrant writer Victor Petrov-Domontovych “Thirst for Music”. It also reveals the intertextual connection of V. Domontovych’s story with Rainer Maria Rilke’s “Stories of God”, as well as Rilke’s correspondence with Magda von Huttingberg. The imagological portrait of Rilke, reconstructed from the short story, may be regarded as the essential interpretant of “interdiscursive intertextuality”. The interdiscursive analysis makes it possible to trace up directly the peculiarities of the writer’s (Rilke) relationships with his real reader (M. von Huttingberg), as well as to outline the discursive nature of story’s architextuality and its genre marking, both of which form the respective horizons of expectations. A particular attention is drawn to Rilke’s poem “Music” (1918), which condenses a wide range of themes articulated by Domontovych in his short story “Thirst for Music” - music as a special metalanguage and a timeless format of music capable of transmitting human feelings. Therefore, the musical key to reading this novel can be Domontovych’s consonance with Rilke. The “fragmentary integrity” of the short story is substantiated by means of the fragmentary, gender marked narrative, the constellation of passages, subject detail, specific phonetic coloring, tropology, and artistic syntax, all these give the prose text the rhythmic parameters of lyrics. Through synesthesia, the author creatively interprets Rilke’s literary method, leaving some figurative and musical “traces”. The veiled compositions of Handel, Bach, Schumann, and Scarlatti are seen as musical ekphrasis. The author resorts to a kind of “game” with the reader, leaving intertextual and intermedial discourses for him to decode. In this way, several receptive channels of the reader’s imagination are simultaneously activated, including visual (“seeing”), auditory (“hearing”), and kinesthetic (“feeling”).
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Seabra, A. G., G. R. Brito, and L. R. R. Carreiro. "Reading assessment in ADHD and dyslexia in Brazilian teenagers." European Psychiatry 66, S1 (March 2023): S714—S715. http://dx.doi.org/10.1192/j.eurpsy.2023.1496.

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IntroductionAttention Deficit Hyperactivity Disorder (ADHD) and Dyslexia are among the most frequent developmental disorders in school-aged students, and both often cause an impact on scholar reading performance. Therefore, it is fundamental to trace the differential profile in reading performance in such diagnoses. Competent reading occurs through the interaction of several cognitive processes, such as decoding, fluency, and oral and reading comprehension, that should be assessed in an evaluation.ObjectivesThe study aimed to characterize the performance of students with ADHD and dyslexia.MethodsWe assessed 25 adolescents, aged between 11 and 14 years old, from 6th to 9th year of middle school of public and private schools in Brazil, divided into two groups: the group with ADHD (16 students) and the group with dyslexia (9 students). The diagnoses were established by a multidisciplinary center and there were no comorbidities for any case. The instruments used were: Comprehension Test of Words and Pseudowords II (TCLPP II) to assess decoding (indicate if the word is correct or incorrect); Reading Fluency Test (TFL) to assess fluency in single words and in text reading; Cloze Reading Comprehension Test (TCCL) to measure reading comprehension; and the WISC vocabulary subtest to assess auditory comprehension.ResultsNon-parametric analyzes revealed statistically significant differences in measures of textual comprehension, especially in the tasks that involved decoding and fluency processes, evidencing superior performance of the group with ADHD in these tests. Participants with dyslexia had a significantly higher performance in relation to the number of word omissions, that is, they had lower omission errors. There was no significant difference between groups in auditory comprehension.ConclusionsA differential profile was found in reading performance, consistent with the cognitive deficits classically pointed out in the literature for each diagnosis: phonological deficits in dyslexia, with problems in decoding and fluency; and attentional deficits in ADHD, with omission errors. In the comprehension measures, dyslexic group had significant lower performance than ADHD in the Cloze Reading Comprehension Test, but there was no difference in the Vocabulary subtest-WISC. An explanatory hypothesis is that, to understand the text, it is necessary to recognize the words previously, whereas, in the WISC, it is not necessary to read, since the questions are oral. These results corroborate the hypothesis that deficits in reading comprehension in dyslexia are more related to difficulties in word recognition and fluency skills than in general listening comprehension.Financial support: CAPES Proex [grant 0426/2021, no. 23038.006837/2021-73]; CNPq [grant 310845/2021-1]Disclosure of InterestNone Declared
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Jones, Samuel A., and Uta Noppeney. "Older adults preserve audiovisual integration through enhanced cortical activations, not by recruiting new regions." PLOS Biology 22, no. 2 (February 6, 2024): e3002494. http://dx.doi.org/10.1371/journal.pbio.3002494.

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Effective interactions with the environment rely on the integration of multisensory signals: Our brains must efficiently combine signals that share a common source, and segregate those that do not. Healthy ageing can change or impair this process. This functional magnetic resonance imaging study assessed the neural mechanisms underlying age differences in the integration of auditory and visual spatial cues. Participants were presented with synchronous audiovisual signals at various degrees of spatial disparity and indicated their perceived sound location. Behaviourally, older adults were able to maintain localisation accuracy. At the neural level, they integrated auditory and visual cues into spatial representations along dorsal auditory and visual processing pathways similarly to their younger counterparts but showed greater activations in a widespread system of frontal, temporal, and parietal areas. According to multivariate Bayesian decoding, these areas encoded critical stimulus information beyond that which was encoded in the brain areas commonly activated by both groups. Surprisingly, however, the boost in information provided by these areas with age-related activation increases was comparable across the 2 age groups. This dissociation—between comparable information encoded in brain activation patterns across the 2 age groups, but age-related increases in regional blood-oxygen-level-dependent responses—contradicts the widespread notion that older adults recruit new regions as a compensatory mechanism to encode task-relevant information. Instead, our findings suggest that activation increases in older adults reflect nonspecific or modulatory mechanisms related to less efficient or slower processing, or greater demands on attentional resources.
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Geirnaert, Simon, Tom Francart, and Alexander Bertrand. "Unsupervised Self-Adaptive Auditory Attention Decoding." IEEE Journal of Biomedical and Health Informatics, 2021, 1. http://dx.doi.org/10.1109/jbhi.2021.3075631.

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Wang, Lei, Yihan Wang, Zhixing Liu, Ed X. Wu, and Fei Chen. "A Speech-Level–Based Segmented Model to Decode the Dynamic Auditory Attention States in the Competing Speaker Scenes." Frontiers in Neuroscience 15 (February 10, 2022). http://dx.doi.org/10.3389/fnins.2021.760611.

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In the competing speaker environments, human listeners need to focus or switch their auditory attention according to dynamic intentions. The reliable cortical tracking ability to the speech envelope is an effective feature for decoding the target speech from the neural signals. Moreover, previous studies revealed that the root mean square (RMS)–level–based speech segmentation made a great contribution to the target speech perception with the modulation of sustained auditory attention. This study further investigated the effect of the RMS-level–based speech segmentation on the auditory attention decoding (AAD) performance with both sustained and switched attention in the competing speaker auditory scenes. Objective biomarkers derived from the cortical activities were also developed to index the dynamic auditory attention states. In the current study, subjects were asked to concentrate or switch their attention between two competing speaker streams. The neural responses to the higher- and lower-RMS-level speech segments were analyzed via the linear temporal response function (TRF) before and after the attention switching from one to the other speaker stream. Furthermore, the AAD performance decoded by the unified TRF decoding model was compared to that by the speech-RMS-level–based segmented decoding model with the dynamic change of the auditory attention states. The results showed that the weight of the typical TRF component approximately 100-ms time lag was sensitive to the switching of the auditory attention. Compared to the unified AAD model, the segmented AAD model improved attention decoding performance under both the sustained and switched auditory attention modulations in a wide range of signal-to-masker ratios (SMRs). In the competing speaker scenes, the TRF weight and AAD accuracy could be used as effective indicators to detect the changes of the auditory attention. In addition, with a wide range of SMRs (i.e., from 6 to –6 dB in this study), the segmented AAD model showed the robust decoding performance even with short decision window length, suggesting that this speech-RMS-level–based model has the potential to decode dynamic attention states in the realistic auditory scenarios.
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Simon, Adèle, Gérard Loquet, Jan Østergaard, and Søren Bech. "Cortical Auditory Attention Decoding During Music And Speech Listening." IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023, 1. http://dx.doi.org/10.1109/tnsre.2023.3291239.

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Su, Enze, Siqi Cai, Longhan Xie, Haizhou Li, and Tanja Schultz. "STAnet: A Spatiotemporal Attention Network for Decoding Auditory Spatial Attention from EEG." IEEE Transactions on Biomedical Engineering, 2022, 1. http://dx.doi.org/10.1109/tbme.2022.3140246.

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38

Kurmanavičiūtė, Dovilė, Hanna Kataja, Mainak Jas, Anne Välilä, and Lauri Parkkonen. "Target of selective auditory attention can be robustly followed with MEG." Scientific Reports 13, no. 1 (July 6, 2023). http://dx.doi.org/10.1038/s41598-023-37959-4.

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AbstractSelective auditory attention enables filtering of relevant acoustic information from irrelevant. Specific auditory responses, measurable by magneto- and electroencephalography (MEG/EEG), are known to be modulated by attention to the evoking stimuli. However, such attention effects have typically been studied in unnatural conditions (e.g. during dichotic listening of pure tones) and have been demonstrated mostly in averaged auditory evoked responses. To test how reliably we can detect the attention target from unaveraged brain responses, we recorded MEG data from 15 healthy subjects that were presented with two human speakers uttering continuously the words “Yes” and “No” in an interleaved manner. The subjects were asked to attend to one speaker. To investigate which temporal and spatial aspects of the responses carry the most information about the target of auditory attention, we performed spatially and temporally resolved classification of the unaveraged MEG responses using a support vector machine. Sensor-level decoding of the responses to attended vs. unattended words resulted in a mean accuracy of $$79\% \pm 2 \%$$ 79 % ± 2 % (N = 14) for both stimulus words. The discriminating information was mostly available 200–400 ms after the stimulus onset. Spatially-resolved source-level decoding indicated that the most informative sources were in the auditory cortices, in both the left and right hemisphere. Our result corroborates attention modulation of auditory evoked responses and shows that such modulations are detectable in unaveraged MEG responses at high accuracy, which could be exploited e.g. in an intuitive brain–computer interface.
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Cai, Siqi, Peiwen Li, Enze Su, and Longhan Xie. "Auditory Attention Detection via Cross-Modal Attention." Frontiers in Neuroscience 15 (July 21, 2021). http://dx.doi.org/10.3389/fnins.2021.652058.

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Humans show a remarkable perceptual ability to select the speech stream of interest among multiple competing speakers. Previous studies demonstrated that auditory attention detection (AAD) can infer which speaker is attended by analyzing a listener's electroencephalography (EEG) activities. However, previous AAD approaches perform poorly on short signal segments, more advanced decoding strategies are needed to realize robust real-time AAD. In this study, we propose a novel approach, i.e., cross-modal attention-based AAD (CMAA), to exploit the discriminative features and the correlation between audio and EEG signals. With this mechanism, we hope to dynamically adapt the interactions and fuse cross-modal information by directly attending to audio and EEG features, thereby detecting the auditory attention activities manifested in brain signals. We also validate the CMAA model through data visualization and comprehensive experiments on a publicly available database. Experiments show that the CMAA achieves accuracy values of 82.8, 86.4, and 87.6% for 1-, 2-, and 5-s decision windows under anechoic conditions, respectively; for a 2-s decision window, it achieves an average of 84.1% under real-world reverberant conditions. The proposed CMAA network not only achieves better performance than the conventional linear model, but also outperforms the state-of-the-art non-linear approaches. These results and data visualization suggest that the CMAA model can dynamically adapt the interactions and fuse cross-modal information by directly attending to audio and EEG features in order to improve the AAD performance.
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Rotaru, Iustina, Simon Geirnaert, Nicolas Heintz, Iris Van de Ryck, Alexander Bertrand, and Tom Francart. "What are we really decoding? Unveiling biases in EEG-based decoding of the spatial focus of auditory attention." Journal of Neural Engineering, January 24, 2024. http://dx.doi.org/10.1088/1741-2552/ad2214.

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Abstract Objective. Spatial auditory attention decoding (Sp-AAD) refers to the task of identifying the direction of the speaker to which a person is attending in a multi-talker setting, based on the listener's neural recordings, e.g., electroencephalography (EEG). The goal of this study is to thoroughly investigate potential biases when training such Sp-AAD decoders on EEG data, particularly eye-gaze biases and latent trial-dependent confounds, which may result in Sp-AAD models that decode eye-gaze or trial-specific fingerprints rather than spatial auditory attention. Approach. We designed a two-speaker audiovisual Sp-AAD protocol in which the spatial auditory and visual attention were enforced to be either congruent or incongruent, and we recorded EEG data from sixteen participants undergoing several trials recorded at distinct timepoints. We trained a simple linear model for Sp-AAD based on common spatial patterns (CSP) filters in combination with either linear discriminant analysis (LDA) or k-means clustering, and evaluated them both across- and within-trial. Main results. We found that even a simple linear Sp-AAD model is susceptible to overfitting to confounding signal patterns such as eye-gaze and trial fingerprints (e.g., due to feature shifts across trials), resulting in artificially high decoding accuracies. Furthermore, we found that changes in the EEG signal statistics across trials deteriorate the trial generalization of the classifier, even when the latter is retrained on the test trial with an unsupervised algorithm. Significance. Collectively, our findings confirm that there exist subtle biases and confounds that can strongly interfere with the decoding of spatial auditory attention from EEG. It is expected that more complicated non-linear models based on deep neural networks, which are often used for Sp-AAD, are even more vulnerable to such biases. Future work should perform experiments and model evaluations that avoid and/or control for such biases in Sp-AAD tasks.
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Xu, Zihao, Yanru Bai, Ran Zhao, Hongmei Hu, Guangjian Ni, and Dong Ming. "Decoding selective auditory attention with EEG using a transformer model." Methods, April 2022. http://dx.doi.org/10.1016/j.ymeth.2022.04.009.

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Zeng, Xianzhang, Siqi Cai, and Longhan Xie. "Attention-guided graph structure learning network for EEG-enabled auditory attention detection." Journal of Neural Engineering, May 22, 2024. http://dx.doi.org/10.1088/1741-2552/ad4f1a.

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Abstract 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.
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Raghavan, Vinay S., James O’Sullivan, Jose Herrero, Stephan Bickel, Ashesh D. Mehta, and Nima Mesgarani. "Improving auditory attention decoding by classifying intracranial responses to glimpsed and masked acoustic events." Imaging Neuroscience, 2024. http://dx.doi.org/10.1162/imag_a_00148.

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Abstract Listeners with hearing loss have trouble following a conversation in multitalker environments. While modern hearing aids can generally amplify speech, these devices are unable to tune into a target speaker without first knowing to which speaker a user aims to attend. Brain-controlled hearing aids have been proposed using auditory attention decoding (AAD) methods, but current methods use the same model to compare the speech stimulus and neural response, regardless of the dynamic overlap between talkers which is known to influence neural encoding. Here, we propose a novel framework that directly classifies event-related potentials (ERPs) evoked by glimpsed and masked acoustic events to determine whether the source of the event was attended. We present a system that identifies auditory events using the local maxima in the envelope rate of change, assesses the temporal masking of auditory events relative to competing speakers, and utilizes masking-specific ERP classifiers to determine if the source of the event was attended. Using intracranial electrophysiological recordings, we showed that high gamma ERPs from recording sites in auditory cortex can effectively decode the attention of subjects. This method of AAD provides higher accuracy, shorter switch times, and more stable decoding results compared with traditional correlational methods, permitting the quick and accurate detection of changes in a listener’s attentional focus. This framework also holds unique potential for detecting instances of divided attention and inattention. Overall, we extend the scope of AAD algorithms by introducing the first linear, direct-classification method for determining a listener’s attentional focus that leverages the latest research in multitalker speech perception. This work represents another step toward informing the development of effective and intuitive brain-controlled hearing assistive devices.
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Tanveer, M. Asjid, Martin A. Skoglund, Bo Bernhardsson, and Emina Alickovic. "Deep learning-based auditory attention decoding in listeners with hearing impairment." Journal of Neural Engineering, May 10, 2024. http://dx.doi.org/10.1088/1741-2552/ad49d7.

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Abstract Objective: This study develops a deep learning method for fast auditory attention decoding (AAD) using electroencephalography (EEG) from listeners with hearing impairment. It addresses three classification tasks: differentiating noise from speech-in-noise, classifying the direction of attended speech (left vs. right) and identifying the activation status of hearing aid noise reduction (NR) algorithms (OFF vs. ON). These tasks contribute to our understanding of how hearing technology influences auditory processing in the hearing-impaired population.
Method: Deep convolutional neural network (DCNN) models were designed for each task. Two training strategies were employed to clarify the impact of data splitting on AAD tasks: inter-trial, where the testing set used classification windows from trials that the training set hadn't seen, and intra-trial, where the testing set used unseen classification windows from trials where other segments were seen during training. The models were evaluated on EEG data from 31 participants with hearing impairment, listening to competing talkers amidst background noise.
Results: Using 1-second classification windows, DCNN models achieve accuracy (ACC) of 69.8\%, 73.3\% and 82.9\% and area-under-curve (AUC) of 77.2\%, 80.6\% and 92.1\% for the three tasks respectively on inter-trial strategy. In the intra-trial strategy, they achieved ACC of 87.9\%, 80.1\% and 97.5\%, along with AUC of 94.6\%, 89.1\%, and 99.8\%. Our DCNN models show good performance on short 1-second EEG samples, making them suitable for real-world applications.
Conclusion: Our DCNN models successfully addressed three tasks with short 1-second EEG windows from participants with hearing impairment, showcasing their potential. While the inter-trial strategy demonstrated promise for assessing AAD, the intra-trial approach yielded inflated results, underscoring the important role of proper data splitting in EEG-based AAD tasks.
Significance: Our findings showcase the promising potential of EEG-based tools for assessing auditory attention in clinical contexts and advancing hearing technology, while also promoting further exploration of alternative deep learning architectures and their potential constraints.
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Thornton, Mike, Danilo Mandic, and Tobias Reichenbach. "Robust decoding of the speech envelope from EEG recordings through deep neural networks." Journal of Neural Engineering, June 16, 2022. http://dx.doi.org/10.1088/1741-2552/ac7976.

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Abstract Objective. Smart hearing aids which can decode the focus of a user's attention could considerably improve comprehension levels in noisy environments. Methods for decoding auditory attention from electroencephalography (EEG) have attracted considerable interest for this reason. Recent studies suggest that the integration of deep neural networks (DNNs) into existing auditory attention decoding algorithms is highly beneficial, although it remains unclear whether these enhanced algorithms can perform robustly in different real-world scenarios. To this end, we sought to characterise the performance of DNNs at reconstructing the envelope of an attended speech stream from EEG recordings in different listening conditions. In addition, given the relatively sparse availability of EEG data, we investigate possibility of applying subject-independent algorithms to EEG recorded from unseen individuals. Approach. Both linear models and nonlinear DNNs were employed to decode the envelope of clean speech from EEG recordings, with and without subject-specific information. The mean behaviour, as well as the variability of the reconstruction, was characterised for each model. We then trained subject-specific linear models and DNNs to reconstruct the envelope of speech in clean and noisy conditions, and investigated how well they performed in different listening scenarios. We also established that these models can be used to decode auditory attention in competing-speaker scenarios. Main results. The DNNs offered a considerable advantage over their linear counterpart at reconstructing the envelope of clean speech. This advantage persisted even when subject-specific information was unavailable at the time of training. The same DNN architectures generalised to a distinct dataset, which contained EEG recorded under a variety of listening conditions. In competing-speakers and speech-in-noise conditions, the DNNs significantly outperformed the linear models. Finally, the DNNs offered a considerable improvement over the linear approach at decoding auditory attention in competing-speakers scenarios. Significance. We present the first detailed study into the extent to which DNNs can be employed for reconstructing the envelope of an attended speech stream. We conclusively demonstrate that DNNs have the ability to improve the reconstruction of the attended speech envelope. The variance of the reconstruction error is shown to be similar for both DNNs and the linear model. Overall, DNNs are demonstrated to show promise for real-world auditory attention decoding, since they perform well in multiple listening conditions and generalise to data recorded from unseen participants.
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Geirnaert, Simon, Tom Francart, and Alexander Bertrand. "Time-adaptive Unsupervised Auditory Attention Decoding Using EEG-based Stimulus Reconstruction." IEEE Journal of Biomedical and Health Informatics, 2022, 1. http://dx.doi.org/10.1109/jbhi.2022.3162760.

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de Vries, Ingmar E. J., Giorgio Marinato, and Daniel Baldauf. "Decoding object-based auditory attention from source-reconstructed MEG alpha oscillations." Journal of Neuroscience, August 24, 2021, JN—RM—0583–21. http://dx.doi.org/10.1523/jneurosci.0583-21.2021.

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48

Kuruvila, Ivine, Jan Muncke, Eghart Fischer, and Ulrich Hoppe. "Extracting the Auditory Attention in a Dual-Speaker Scenario From EEG Using a Joint CNN-LSTM Model." Frontiers in Physiology 12 (August 2, 2021). http://dx.doi.org/10.3389/fphys.2021.700655.

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Human brain performs remarkably well in segregating a particular speaker from interfering ones in a multispeaker scenario. We can quantitatively evaluate the segregation capability by modeling a relationship between the speech signals present in an auditory scene, and the listener's cortical signals measured using electroencephalography (EEG). This has opened up avenues to integrate neuro-feedback into hearing aids where the device can infer user's attention and enhance the attended speaker. Commonly used algorithms to infer the auditory attention are based on linear systems theory where cues such as speech envelopes are mapped on to the EEG signals. Here, we present a joint convolutional neural network (CNN)—long short-term memory (LSTM) model to infer the auditory attention. Our joint CNN-LSTM model takes the EEG signals and the spectrogram of the multiple speakers as inputs and classifies the attention to one of the speakers. We evaluated the reliability of our network using three different datasets comprising of 61 subjects, where each subject undertook a dual-speaker experiment. The three datasets analyzed corresponded to speech stimuli presented in three different languages namely German, Danish, and Dutch. Using the proposed joint CNN-LSTM model, we obtained a median decoding accuracy of 77.2% at a trial duration of 3 s. Furthermore, we evaluated the amount of sparsity that the model can tolerate by means of magnitude pruning and found a tolerance of up to 50% sparsity without substantial loss of decoding accuracy.
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Wilroth, Johanna, Bo Bernhardsson, Frida Heskebeck, Martin A. Skoglund, Carolina Bergeling, and Emina Alickovic. "Improving EEG-based decoding of the locus of auditory attention through domain adaptation." Journal of Neural Engineering, November 21, 2023. http://dx.doi.org/10.1088/1741-2552/ad0e7b.

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Abstract Objective: This paper presents a novel domain adaptation framework to enhance the accuracy of EEG-based auditory attention classification, specifically for classifying the direction (left or right) of attended speech. The framework aims to improve the performances for subjects with initially low classification accuracy, overcoming challenges posed by instrumental and human factors. Limited dataset size, variations in EEG data quality due to factors such as noise, electrode misplacement or subjects, and the need for generalization across different trials, conditions and subjects necessitate the use of domain adaptation methods. By leveraging domain adaptation methods, the framework can learn from one EEG dataset and adapt to another, potentially resulting in more reliable and robust classification models.
Approach: This paper focuses on investigating a domain adaptation method, based on parallel transport, for addressing the auditory attention classification problem. The EEG data utilized in this study originates from an experiment where subjects were instructed to selectively attend to one of the two spatially separated voices presented simultaneously.
Main results: Significant improvement in classification accuracy was observed when poor data from one subject was transported to the domain of good data from different subjects, as compared to the baseline. The mean classification accuracy for subjects with poor data increased from 45.84% to 67.92%. Specifically, the highest achieved classification accuracy from one subject reached 83.33%, a substantial increase from the baseline accuracy of 43.33%. 
Significance: The findings of our study demonstrate the improved classification performances achieved through the implementation of domain adaptation methods. This brings us a step closer to leveraging EEG in neuro-steered hearing devices.
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Dolhopiatenko, Hanna, and Waldo Nogueira. "Selective attention decoding in bimodal cochlear implant users." Frontiers in Neuroscience 16 (January 11, 2023). http://dx.doi.org/10.3389/fnins.2022.1057605.

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The growing group of cochlear implant (CI) users includes subjects with preserved acoustic hearing on the opposite side to the CI. The use of both listening sides results in improved speech perception in comparison to listening with one side alone. However, large variability in the measured benefit is observed. It is possible that this variability is associated with the integration of speech across electric and acoustic stimulation modalities. However, there is a lack of established methods to assess speech integration between electric and acoustic stimulation and consequently to adequately program the devices. Moreover, existing methods do not provide information about the underlying physiological mechanisms of this integration or are based on simple stimuli that are difficult to relate to speech integration. Electroencephalography (EEG) to continuous speech is promising as an objective measure of speech perception, however, its application in CIs is challenging because it is influenced by the electrical artifact introduced by these devices. For this reason, the main goal of this work is to investigate a possible electrophysiological measure of speech integration between electric and acoustic stimulation in bimodal CI users. For this purpose, a selective attention decoding paradigm has been designed and validated in bimodal CI users. The current study included behavioral and electrophysiological measures. The behavioral measure consisted of a speech understanding test, where subjects repeated words to a target speaker in the presence of a competing voice listening with the CI side (CIS) only, with the acoustic side (AS) only or with both listening sides (CIS+AS). Electrophysiological measures included cortical auditory evoked potentials (CAEPs) and selective attention decoding through EEG. CAEPs were recorded to broadband stimuli to confirm the feasibility to record cortical responses with CIS only, AS only, and CIS+AS listening modes. In the selective attention decoding paradigm a co-located target and a competing speech stream were presented to the subjects using the three listening modes (CIS only, AS only, and CIS+AS). The main hypothesis of the current study is that selective attention can be decoded in CI users despite the presence of CI electrical artifact. If selective attention decoding improves combining electric and acoustic stimulation with respect to electric stimulation alone, the hypothesis can be confirmed. No significant difference in behavioral speech understanding performance when listening with CIS+AS and AS only was found, mainly due to the ceiling effect observed with these two listening modes. The main finding of the current study is the possibility to decode selective attention in CI users even if continuous artifact is present. Moreover, an amplitude reduction of the forward transfer response function (TRF) of selective attention decoding was observed when listening with CIS+AS compared to AS only. Further studies to validate selective attention decoding as an electrophysiological measure of electric acoustic speech integration are required.

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