Martin Skoglund

M. Asjid Tanveer, Martin Skoglund, Bo Bernhardsson, Emina Alickovic,  Deep learning-based auditory attention decoding in listeners with hearing impairment, Journal of Neural Engineering 21:036022 (2024)  https://doi.org/10.1088/1741-2552/ad49d7

Anton Kullberg, Martin Skoglund, Isaac Skog, Gustaf Hendeby,  On the Relationship Between Iterated Statistical Linearization and Quasi–Newton Methods, IEEE Signal Processing Letters 30:1777-1781 (2023)  https://doi.org/10.1109/lsp.2023.3336559

Emina Alickovic, Carlos Francisco Mendoza, Andrew Segar, Maria Sandsten, Martin Skoglund,  DECODING AUDITORY ATTENTION FROM EEG DATA USING CEPSTRAL ANALYSIS, 2023 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING WORKSHOPS, ICASSPW, IEEE (2023)  https://doi.org/10.1109/ICASSPW59220.2023.10193192

Emina Alickovic, Tobias Dorszewski, Thomas U. Christiansen, Kasper Eskelund, Leonardo Gizzi, Martin Skoglund, Dorothea Wendt,  Predicting EEG Responses to Attended Speech via Deep Neural Networks for Speech, 2023 45TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY, EMBC, IEEE Engineering in Medicine and Biology Society Conference Proceedings, IEEE (2023)  https://doi.org/10.1109/EMBC40787.2023.10340027

Johanna Wilroth, Bo Bernhardsson, Frida Heskebeck, Martin Skoglund, Carolina Bergeling, Emina Alickovic,  Improving EEG-based decoding of the locus of auditory attention through domain adaptation, Journal of Neural Engineering 20:066022 (2023)  https://doi.org/10.1088/1741-2552/ad0e7b