People enjoy listening to music as part of their life. This makes music an excellent choice for designing a user-friendly brain-computer interface (BCI) for long-term use. We propose a novel BCI system using music stimuli that relies on brain signals collected via Smartfones, an EEG recording device integrated into a pair of headphones. In a user study of the proposed system, participants were asked to pay attention to one of three musical instruments playing simultaneously from separate spatial directions. We used a stimulus reconstruction method to decode attention from EEG signals. Results show that the proposed system can achieve good decoding accuracy (>70%) while providing superior user-friendliness compared to a traditional EEG setup.

We demonstrate an open, extensible platform for developing and studying multimodal, integrative-AI systems. The platform provides a time-aware, stream-based programming model for parallel coordinated computation, a set of tools for data visualization, processing, and learning, and an ecosystem of pluggable AI components. The demonstration will showcase three applications built on this platform and highlight how the platform can significantly accelerate development and research in multimodal interactive systems.

Practical mapping and navigation solutions for large indoor environments continue to rely on relatively expensive range scanners, because of their accuracy, range and field of view. Microsoft Kinect on the other hand is inexpensive, is easy to use and has high resolution, but suffers from high noise, shorter range and a limiting field of view. We present a mapping and navigation system that uses the Microsoft Kinect sensor as the sole source of range data and achieves performance comparable to state-of-the-art LIDAR-based systems. We show how we circumvent the main limitations of Kinect to generate usable 2D maps of relatively large spaces and to enable robust navigation in changing and dynamic environments. We use the Benchmark for Robotic Indoor Navigation (BRIN) to quantify and validate the performance of our system.

We propose a method for evaluating the localization accuracy of an indoor navigation system in arbitrarily large environments. Instead of using externally mounted sensors, as required by most ground-truth systems, our approach involves mounting only landmarks consisting of distinct patterns printed on inexpensive foam boards. A pose estimation algorithm computes the pose of the robot with respect to the landmark using the image obtained by an on-board camera. We demonstrate that such an approach is capable of providing accurate estimates of a mobile robot's position and orientation with respect to the landmarks in arbitrarily-sized environments over arbitrarily-long trials. Furthermore, because the approach involves minimal outfitting of the environment, we show that only a small amount of setup time is needed to apply the method to a new environment. Experiments involving a state-of-the-art navigation system demonstrate the ability of the method to facilitate accurate localization measurements over arbitrarily long periods of time.