@article{StoberSternin2018, author = {Stober, Sebastian and Sternin, Avital}, title = {Decoding music perception and imagination using deep-learning techniques}, series = {Signal processing and machine learning for brain-machine interfaces}, volume = {114}, journal = {Signal processing and machine learning for brain-machine interfaces}, publisher = {Institution of Engineering and Technology}, address = {London}, isbn = {978-1-78561-399-9}, doi = {10.1049/PBCE114E}, pages = {271 -- 299}, year = {2018}, abstract = {Deep learning is a sub-field of machine learning that has recently gained substantial popularity in various domains such as computer vision, automatic speech recognition, natural language processing, and bioinformatics. Deep-learning techniques are able to learn complex feature representations from raw signals and thus also have potential to improve signal processing in the context of brain-computer interfaces (BCIs). However, they typically require large amounts of data for training - much more than what can often be provided with reasonable effort when working with brain activity recordings of any kind. In order to still leverage the power of deep-learning techniques with limited available data, special care needs to be taken when designing the BCI task, defining the structure of the deep model, and choosing the training method. This chapter presents example approaches for the specific scenario of music-based brain-computer interaction through electroencephalography - in the hope that these will prove to be valuable in different settings as well. We explain important decisions for the design of the BCI task and their impact on the models and training techniques that can be used. Furthermore, we present and compare various pre-training techniques that aim to improve the signal-to-noise ratio. Finally, we discuss approaches to interpret the trained models.}, language = {en} } @misc{LowHentschelStoberetal.2017, author = {Low, Thomas and Hentschel, Christian and Stober, Sebastian and Sack, Harald and N{\"u}rnberger, Andreas}, title = {Exploring large movie collections}, series = {Lecture notes in computer science}, volume = {10133}, journal = {Lecture notes in computer science}, editor = {Amsaleg, Laurent and Guðmundsson, Gylfi Þ{\´o}r and Gurrin, Cathal and J{\´o}nsson, Bj{\"o}rn Þ{\´o}r and Satoh, Shin'ichi}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-51814-5}, issn = {0302-9743}, doi = {10.1007/978-3-319-51814-5_17}, pages = {198 -- 208}, year = {2017}, abstract = {We compare Visual Berrypicking, an interactive approach allowing users to explore large and highly faceted information spaces using similarity-based two-dimensional maps, with traditional browsing techniques. For large datasets, current projection methods used to generate maplike overviews suffer from increased computational costs and a loss of accuracy resulting in inconsistent visualizations. We propose to interactively align inexpensive small maps, showing local neighborhoods only, which ideally creates the impression of panning a large map. For evaluation, we designed a web-based prototype for movie exploration and compared it to the web interface of The Movie Database (TMDb) in an online user study. Results suggest that users are able to effectively explore large movie collections by hopping from one neighborhood to the next. Additionally, due to the projection of movie similarities, interesting links between movies can be found more easily, and thus, compared to browsing serendipitous discoveries are more likely.}, language = {en} } @misc{OfnerStober2019, author = {Ofner, Andre and Stober, Sebastian}, title = {Hybrid variational predictive coding as a bridge between human and artificial cognition}, series = {ALIFE 2019: The 2019 Conference on Artificial Life}, journal = {ALIFE 2019: The 2019 Conference on Artificial Life}, publisher = {MIT Press}, address = {Cambridge}, pages = {68 -- 69}, year = {2019}, abstract = {Predictive coding and its generalization to active inference offer a unified theory of brain function. The underlying predictive processing paradigmhas gained significant attention in artificial intelligence research for its representation learning and predictive capacity. Here, we suggest that it is possible to integrate human and artificial generative models with a predictive coding network that processes sensations simultaneously with the signature of predictive coding found in human neuroimaging data. We propose a recurrent hierarchical predictive coding model that predicts low-dimensional representations of stimuli, electroencephalogram and physiological signals with variational inference. We suggest that in a shared environment, such hybrid predictive coding networks learn to incorporate the human predictive model in order to reduce prediction error. We evaluate the model on a publicly available EEG dataset of subjects watching one-minute long video excerpts. Our initial results indicate that the model can be trained to predict visual properties such as the amount, distance and motion of human subjects in videos.}, language = {en} } @article{Stober2017, author = {Stober, Sebastian}, title = {Model-based frameworks for user adapted information exploration}, series = {Companion technology : a paradigm shift in human-technology interaction}, volume = {Cham}, journal = {Companion technology : a paradigm shift in human-technology interaction}, publisher = {Springer}, isbn = {978-3-319-43664-7}, pages = {37 -- 56}, year = {2017}, language = {en} } @misc{BridwellCavanaghCollinsetal.2018, author = {Bridwell, David A. and Cavanagh, James F. and Collins, Anne G. E. and Nunez, Michael D. and Srinivasan, Ramesh and Stober, Sebastian and Calhoun, Vince D.}, title = {Moving Beyond ERP Components}, series = {Frontiers in human neuroscienc}, volume = {12}, journal = {Frontiers in human neuroscienc}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1662-5161}, doi = {10.3389/fnhum.2018.00106}, pages = {17}, year = {2018}, abstract = {Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or "components" derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.}, language = {en} } @misc{BridwellCavanaghCollinsetal.2018, author = {Bridwell, David A. and Cavanagh, James F. and Collins, Anne G. E. and Nunez, Michael D. and Srinivasan, Ramesh and Stober, Sebastian and Calhoun, Vince D.}, title = {Moving beyond ERP components}, series = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe}, number = {656}, issn = {1866-8364}, doi = {10.25932/publishup-45966}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-459667}, pages = {19}, year = {2018}, abstract = {Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or "components" derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.}, language = {en} } @article{Stober2017, author = {Stober, Sebastian}, title = {Toward Studying Music Cognition with Information Retrieval Techniques}, series = {Frontiers in psychology}, volume = {8}, journal = {Frontiers in psychology}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1664-1078}, doi = {10.3389/fpsyg.2017.01255}, year = {2017}, abstract = {As an emerging sub-field of music information retrieval (MIR), music imagery information retrieval (MIIR) aims to retrieve information from brain activity recorded during music cognition-such as listening to or imagining music pieces. This is a highly inter-disciplinary endeavor that requires expertise in MIR as well as cognitive neuroscience and psychology. The OpenMIIR initiative strives to foster collaborations between these fields to advance the state of the art in MIIR. As a first step, electroencephalography (EEG) recordings of music perception and imagination have been made publicly available, enabling MIR researchers to easily test and adapt their existing approaches for music analysis like fingerprinting, beat tracking or tempo estimation on this new kind of data. This paper reports on first results of MIIR experiments using these OpenMIIR datasets and points out how these findings could drive new research in cognitive neuroscience.}, language = {en} } @misc{Stober2017, author = {Stober, Sebastian}, title = {Toward Studying Music Cognition with Information Retrieval Techniques}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-402762}, pages = {17}, year = {2017}, abstract = {As an emerging sub-field of music information retrieval (MIR), music imagery information retrieval (MIIR) aims to retrieve information from brain activity recorded during music cognition-such as listening to or imagining music pieces. This is a highly inter-disciplinary endeavor that requires expertise in MIR as well as cognitive neuroscience and psychology. The OpenMIIR initiative strives to foster collaborations between these fields to advance the state of the art in MIIR. As a first step, electroencephalography (EEG) recordings of music perception and imagination have been made publicly available, enabling MIR researchers to easily test and adapt their existing approaches for music analysis like fingerprinting, beat tracking or tempo estimation on this new kind of data. This paper reports on first results of MIIR experiments using these OpenMIIR datasets and points out how these findings could drive new research in cognitive neuroscience.}, language = {en} } @article{Stober2017, author = {Stober, Sebastian}, title = {Toward Studying Music Cognition with Information Retrieval Techniques: Lessons Learned from the OpenMIIR Initiative}, series = {Frontiers in psychology}, volume = {8}, journal = {Frontiers in psychology}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1664-1078}, doi = {10.3389/fpsyg.2017.01255}, pages = {17}, year = {2017}, abstract = {As an emerging sub-field of music information retrieval (MIR), music imagery information retrieval (MIIR) aims to retrieve information from brain activity recorded during music cognition-such as listening to or imagining music pieces. This is a highly interdisciplinary endeavor that requires expertise in MIR as well as cognitive neuroscience and psychology. The OpenMIIR initiative strives to foster collaborations between these fields to advance the state of the art in MIIR. As a first step, electroencephalography (EEG) recordings ofmusic perception and imagination have beenmade publicly available, enabling MIR researchers to easily test and adapt their existing approaches for music analysis like fingerprinting, beat tracking or tempo estimation on this new kind of data. This paper reports on first results of MIIR experiments using these OpenMIIR datasets and points out how these findings could drive new research in cognitive neuroscience.}, language = {en} }