Commercially available EEG-based Brain-Computer Interface (BCI) wearable headsets are always-on and are thus power hungry, requiring users to charge the headsets multiple times a day. In this paper, we tackle the problem of wake-up command design and detection for BCI headsets, and explore how battery life can be made to last for approximately a whole day. The key challenge that we address is enabling the headset to operate in a near-sleep mode but still reliably detect and interpret an EEG-based wake-up command from the user. Towards addressing the challenge, we present a solution that is built upon eye-blinks. Our core contribution is Trance, a user-friendly and robust wake-up command for BCI headsets that is computationally lightweight. We show using experimental results coupled with multiple data sets collected through user-studies that Trance can extend battery life by approximately 2.7x or to approximately 10 hours for a typical wearable battery, while remaining user-friendly.

Biometric authentication is becoming popular in a varied range of applications because of its unique specificity for an individual user. In this context, electro-encephalogram (EEG) signals from a user is an interesting candidate for authentication. In this work, we specifically focus on the EEG signal corresponding to the human eye-blink to create an authentication system that could be used to distinguish between multiple users accurately and efficiently while also being burden-less and convenient to the users. We use a dataset of eye-blink related EEG signals, collected from 20 users, to study our solution. Our results show that blink signals can be used for accurately distinguishing between different users and hence can be used for authentication.

YouTube is the most popular video sharing platform with more than 2 billion active users and 1 billion hours of video content watched daily. The dominance of YouTube has had a big impact on the performance of Internet protocols, algorithms, and systems. Understanding the interaction of users with YouTube is thus of much interest to the research community. In this context, we collect YouTube watch history data from 243 users spanning a 1.5 year period. The dataset comprises of a total of 1.8 million videos. We use the dataset to analyze and present key insights about user-level usage behavior. We also show that our analysis can be used by researchers to tackle a myriad of problems in the general domains of networking and communication. We present baseline characteristics and also substantiated directions to solve a few representative problems related to local caching techniques, prefetching strategies, the performance of YouTube's recommendation engine, the variability of user's video preferences and application specific load provisioning.

Line-of-sight (LOS) is a critical requirement for mmWave communication. In this work, we make the case for a ceilingmounted mobile (CMM) AP by comparing its performance with other types of AP mobility and single static AP. We then present Hawkeye to solve the optimal location discovery problem for a CMM AP using a machine learning (ML) algorithm. Hawkeye relies purely on the connectivity matrix between STAs and the AP to decide if and where the AP should move to for maximizing LOS connectivity. Using a prototype implementation, we show that the throughput of Hawkeye is 219% and 129% compared with single static AP and other approaches for AP mobility, respectively.

Brain-computer interfaces (BCIs) allow users to communicate to a nearby computing device (computer, smartphone, etc.) using thoughts or other covert actions that result in a detectable change in brain-waves. Consider a BCI command to be a word consisting of a sequence of characters. Each character is a thought or action that can be reliably detected through brain waves. For this work, we specifically consider eye-blinks as the user action of interest. Eye-blinks are an interesting modality for BCI commands because of their easy detectability and naturalness (and hence covertness). It turns out that there is an interesting trade-off between the complexity of characters and the length of words. In this work, we perform a user-study to answer a simple, but important, question pertaining to eye-blinks based BCI command design: do users prefer shorter characters (and hence longer words) or shorter words (and hence longer characters) when performing commands?. We present a simple eye-blink language consisting of words and characters and use real user-experiments to study the aforementioned trade-off.