Martin Skoglund

Martin Skoglund is an Adjunct Associate Professor at Linköping University, affiliated with the Department of Electrical Engineering and the Automatic Control (RT) research group. His work bridges engineering and neuroscience, focusing on advanced signal processing for hearing-impaired listeners.

• Institution: Linköping University
• Department: Department of Electrical Engineering (ISY)
• Research Group: Automatic Control (RT)
• Email: martin.skoglund@liu.se

His research interests lie at the intersection of biomedical signal processing and cognitive neuroscience, particularly in decoding auditory attention using EEG signals. He specializes in enhancing speech tracking for hearing aid users through deep learning, contrastive learning, and nonlinear signal compensation techniques. His work addresses real-world challenges such as noise interference and hardware-induced nonlinearities in neural recordings.

The recent publications highlight a strong trend in developing machine learning models that improve brain-computer interfaces for hearing assistance. These studies predominantly apply deep learning and statistical methods to EEG data, aiming to decode which speaker a listener is focusing on in complex acoustic environments. The research is highly interdisciplinary, combining elements of electrical engineering, neuroscience, and clinical audiology.

Martin Skoglund actively collaborates with experts in auditory neuroscience and control systems. While no formal awards or students are listed in the provided texts, his contributions appear in high-impact journals such as Journal of Neural Engineering and eNeuro, as well as top-tier conferences like ICASSP.

He is involved in research projects related to auditory attention decoding, EEG-based speech tracking, and signal enhancement for hearing-impaired individuals. These efforts are likely part of broader initiatives in assistive neurotechnology and intelligent hearing systems at Linköping University.