Auditory attention decoding (AAD) through a brain-computer interface has had a flowering of developments since it was first introduced by Mesgarani and Chang (2012) using electrocorticograph recordings. AAD has been pursued for its potential application to hearing-aid design in which an attention-guided algorithm selects, from multiple competing acoustic sources, which should be enhanced for the listener and which should be suppressed. Traditionally, researchers have separated the AAD problem into two stages: reconstruction of a representation of the attended audio from neural signals, followed by determining the similarity between the candidate audio streams and the reconstruction. Here, we compare the traditional two-stage approach with a novel neural-network architecture that subsumes the explicit similarity step. We compare this new architecture against linear and non-linear (neural-network) baselines using both wet and dry electroencephalogram (EEG) systems. Our results indicate that the new architecture outperforms the baseline linear stimulus-reconstruction method, improving decoding accuracy from 66% to 81% using wet EEG and from 59% to 87% for dry EEG. Also of note was the finding that the dry EEG system can deliver comparable or even better results than the wet, despite the latter having one third as many EEG channels as the former. The 11-subject, wet-electrode AAD dataset for two competing, co-located talkers, the 11-subject, dry-electrode AAD dataset, and our software are available for further validation, experimentation, and modification.

1In individuals with major depressive disorder, neurophysiological changes often alter motor control and thus affect the mechanisms controlling speech production and facial expression. These changes are typically associated with psychomotor retardation, a condition marked by slowed neuromotor output that is behaviorally manifested as altered coordination and timing across multiple motor-based properties. Changes in motor outputs can be inferred from vocal acoustics and facial movements as individuals speak. We derive novel multi-scale correlation structure and timing feature sets from audio-based vocal features and videobased facial action units from recordings provided by the 4th International Audio/Video Emotion Challenge (AVEC). The feature sets enable detection of changes in coordination, movement, and timing of vocal and facial gestures that are potentially symptomatic of depression. Combining complementary features in Gaussian mixture model and extreme learning machine classifiers, our multivariate regression scheme predicts Beck depression inventory ratings on the AVEC test set with a root-mean-square error of 8.12 and mean absolute error of 6.31. Future work calls for continued study into detection of neurological disorders based on altered coordination and timing across audio and video modalities.

This paper reviews the current state of several formal models of speech motor control, with particular focus on the low-level control of the speech articulators. Further development of speech motor control models may be aided by a comparison of model attributes. The review builds an understanding of existing models from first principles, before moving into a discussion of several models, showing how each is constructed out of the same basic domain-general ideas and components—e.g., generalized feedforward, feedback, and model predictive components. This approach allows for direct comparisons to be made in terms of where the models differ, and their points of agreement. Substantial differences among models can be observed in their use of feedforward control, process of estimating system state, and method of incorporating feedback signals into control. However, many commonalities exist among the models in terms of their reliance on higher-level motor planning, use of feedback signals, lack of time-variant adaptation, and focus on kinematic aspects of control and biomechanics. Ongoing research bridging hybrid feedforward/feedback pathways with forward dynamic control, as well as feedback/internal model-based state estimation, is discussed.

Reliable, real-time heart and respiratory rates are key vital signs used in evaluating the physiological status in many clinical and non-clinical settings. Measuring these vital signs generally requires superficial attachment of physically or logistically obtrusive sensors to subjects that may result in skin irritation or adversely influence subject performance. Given the broad acceptance of ingestible electronics, we developed an approach that enables vital sign monitoring internally from the gastrointestinal tract. Here we report initial proof-of-concept large animal (porcine) experiments and a robust processing algorithm that demonstrates the feasibility of this approach. Implementing vital sign monitoring as a stand-alone technology or in conjunction with other ingestible devices has the capacity to significantly aid telemedicine, optimize performance monitoring of athletes, military service members, and first-responders, as well as provide a facile method for rapid clinical evaluation and triage.