Abstract
This study looks into listener attention in a more realistic four-talker setting to make up for the shortcomings of previous Auditory Attention Decoding (AAD) research, which mostly looked at simplified two-talker situations. A new AAD database was created that records the electroencephalogram (EEG) responses of listeners while they listen to four different dialogues at the same time in different locations. We used cortical spatial lateralization and stimulus reconstruction (SR)-based AAD methods to figure out what people were paying attention to. The results show that SR works very well, with a decoding accuracy of 77.5% over a 60-second period, which is much higher than the 25% chance level. Also, Auditory Spatial Attention Detection (ASAD) methods did an amazing job of generalizing, getting 94.7% accuracy with DenseNet-3D in just one second. These results show that it is possible to accurately decode auditory attention in very complicated sound environments. This opens the door to more advanced real-world uses in brain-computer interfaces and assistive listening devices.
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References
Cai S, Zhang R, Zhang M, Wu J, Li H (2024) EEG-based auditory attention detection with spiking graph convolutional network. IEEE Trans Cogn Dev Syst
Choudhari V, Han C, Bickel S, Mehta AD, Schevon C, McKhann GM, Mesgarani N (2024) Brain‐controlled augmented hearing for spatially moving conversations in multi‐talker environments. Adv Sci 2401379
Falaschetti L, Alessandrini M, Turchetti C (2024) Forward nonlinear model for deep learning of EEG auditory attention detection in cocktail party problem. In: Advances in intelligent disease diagnosis and treatment: research papers in Honour of Prof. Janusz Kacprzyk for Invaluable Contributions. Springer Nature Switzerland, Cham, pp 143–165
Fan C, Zhang H, Huang W, Xue J, Tao J, Yi J, Wu X (2024) DGSD: dynamical graph self-distillation for EEG-based auditory spatial attention detection. Neural Netw 179:106580
Geirnaert S, Zink R, Francart T, Bertrand A (2024) Fast, accurate, unsupervised, and time-adaptive EEG-based auditory attention decoding for neuro-steered hearing devices. In: Brain-computer interface research: a state-of-the-art summary 11. Springer Nature Switzerland, Cham, pp 29–40
Ha J, Baek SC, Lim Y, Chung JH (2023) Validation of cost-efficient EEG experimental setup for neural tracking in an auditory attention task. Sci Rep 13(1):22682
He X, Raghavan VS, Mesgarani N (2024) Decoding the unintelligible: neural speech tracking in low signal-to-noise ratios. bioRxiv, 2024-10
Hjortkjær J, Wong DD, Catania A, Märcher-Rørsted J, Ceolini E, Fuglsang SA, de Cheveigné A (2024) Real-time control of a hearing instrument with EEG-based attention decoding. bioRxiv, 2024-03
Nanthini K, Sivabalaselvamani D, MadhanKumar M, Kaviya R (2024) Comprehensive EEG signal feature extraction for neurological disorder diagnosis: focus on alzheimer’s parkinson’s and seizure disorders. Int Res J Multidiscip Tech 6(5):80–93. https://doi.org/10.54392/irjmt2456
Nimbare S, Paygude P, Dhumane A, Rathi S, Bidve V (2024) Deep learning model to evaluate alzheimer’s disease through multi-view clustering. Int Res J Multidiscip Tech 7(1):33–46. https://doi.org/10.54392/irjmt2513
Niu Y, Chen N, Zhu H, Jin J, Li G (2024) Music-oriented auditory attention detection from electroencephalogram. Neurosci Lett 818:137534
Rajawat AS, Rawat R, Barhanpurkar K, Shaw RN, Ghosh A (2021) Depression detection for elderly people using AI robotic systems leveraging the Nelder–Mead method. In: Artificial intelligence for future generation robotics. Elsevier, pp 55–70. ISBN 9780323854986. https://doi.org/10.1016/B978-0-323-85498-6.00006-X
Roebben A, Heintz N, Geirnaert S, Francart T, Bertrand A (2024) ‘Are you even listening?’-EEG-based decoding of absolute auditory attention to natural speech. J Neural Eng
Shames MA, Kamil MY (2024) Early diagnosis of lung infection via deep learning approach. Int Res J Multidiscip Tech 6(3):216–224. https://doi.org/10.54392/irjmt24316
Shri Bharathi SV, Parasuraman T, Akila S, Ramakrishnan R, Shivaraju K, Krishnakumar S, Sreedev SA, Vijayalakshmi C, Vijayalakshmi C (2024) Yoga poses identification and classification based on convolutional neural network and transfer learning with media pipe. Int Res J Multidiscip Tech 6(2):156–167. https://doi.org/10.54392/irjmt24212
Singh SP, Singh P, Diwakar M, Kumar P (2024) Improving quality of service for Internet of Things (IoT) in real life application: a novel adaptation based hybrid evolutionary algorithm. Internet of Things 27:101323
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Kishore Kanna, R., Singh, P., Ghosh, A., Nath Shaw, R., Sathea Sree, S. (2026). Performance Analysis of Stimulus Reconstruction and Spatial Lateralization for EEG-Based AAD Using DL Applications. In: Das, S., Paprzycki, M., Ghosh, A., Bianchini, M. (eds) Advanced Computing and Intelligent Technologies. ICACIT 2024. Lecture Notes in Networks and Systems, vol 1359. Springer, Singapore. https://doi.org/10.1007/978-981-96-4933-4_42
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DOI: https://doi.org/10.1007/978-981-96-4933-4_42
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