Assessing the feasibility of online SSVEP decoding in human walking using a consumer EEG headset.

BackgroundBridging the gap between laboratory brain-computer interface (BCI) demonstrations and real-life applications has gained increasing attention nowadays in translational neuroscience. An urgent need is to explore the feasibility of using a low-cost, ease-of-use electroencephalogram (EEG) headset for monitoring individuals' EEG signals in their natural head/body positions and movements. This study aimed to assess the feasibility of using a consumer-level EEG headset to realize an online steady-state visual-evoked potential (SSVEP)-based BCI during human walking.MethodsThis study adopted a 14-channel Emotiv EEG headset to implement a four-target online SSVEP decoding system, and included treadmill walking at the speeds of 0.45, 0.89, and 1.34 meters per second (m/s) to initiate the walking locomotion. Seventeen participants were instructed to perform the online BCI tasks while standing or walking on the treadmill. To maintain a constant viewing distance to the visual targets, participants held the hand-grip of the treadmill during the experiment. Along with online BCI performance, the concurrent SSVEP signals were recorded for offline assessment.ResultsDespite walking-related attenuation of SSVEPs, the online BCI obtained an information transfer rate (ITR) over 12 bits/min during slow walking (below 0.89 m/s).ConclusionsSSVEP-based BCI systems are deployable to users in treadmill walking that mimics natural walking rather than in highly-controlled laboratory settings. This study considerably promotes the use of a consumer-level EEG headset towards the real-life BCI applications

Remaining As A Leader or Not? Technology Spillover Answers

Much has been discussed about the advantages and disadvantages of being a first mover. However, discussion regarding the incentives encouraging a firm to remain as leader is absent in most studies. This paper aims to fill this gap and provide theoretical and empirical evidences vis-à-vis the incentives of remaining as a leader, by comparing profits between leader and follower when taking technology spillover and technological efficiency into consideration. The findings show that in regard to effective technological efficiency: (i) under the condition of a weak (strong) technology spillover, retaining the position of leader firm is (not) a dominant strategy because of higher (lower) profits than a follower; (ii) the half-shared technology spillover leads to an equal profit between firms, and implies a critical time of action for the leader firm to break the evenly-matched status by reconsidering first mover advantages; (iii) an empirical study on a data set of 352 high-tech and non-high-tech SMEs in manufacturing industry from 1999 until 2006 lends strong support to these results and may also provide useful clues for technology managers or practitioners to make better policies to benefit their market competitiveness

Block Switching: A Stochastic Approach for Deep Learning Security

Recent study of adversarial attacks has revealed the vulnerability of modern deep learning models. That is, subtly crafted perturbations of the input can make a trained network with high accuracy produce arbitrary incorrect predictions, while maintain imperceptible to human vision system. In this paper, we introduce Block Switching (BS), a defense strategy against adversarial attacks based on stochasticity. BS replaces a block of model layers with multiple parallel channels, and the active channel is randomly assigned in the run time hence unpredictable to the adversary. We show empirically that BS leads to a more dispersed input gradient distribution and superior defense effectiveness compared with other stochastic defenses such as stochastic activation pruning (SAP). Compared to other defenses, BS is also characterized by the following features: (i) BS causes less test accuracy drop; (ii) BS is attack-independent and (iii) BS is compatible with other defenses and can be used jointly with others.Comment: Accepted by AdvML19: Workshop on Adversarial Learning Methods for Machine Learning and Data Mining at KDD, Anchorage, Alaska, USA, August 5th, 2019, 5 page