@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{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}
}