The dorsolateral pre-frontal cortex bi-polar error-related potential in a locked-in patient implanted with a daily use brain–computer interface

While brain computer interfaces (BCIs) offer the potential of allowing those suffering from loss of muscle control to once again fully engage with their environment by bypassing the affected motor system and decoding user intentions directly from brain activity, they are prone to errors. One possible avenue for BCI performance improvement is to detect when the BCI user perceives the BCI to have made an unintended action and thus take corrective actions. Error-related potentials (ErrPs) are neural correlates of error awareness and as such can provide an indication of when a BCI system is not performing according to the user’s intentions. Here, we investigate the brain signals of an implanted BCI user suffering from locked-in syndrome (LIS) due to late-stage ALS that prevents her from being able to speak or move but not from using her BCI at home on a daily basis to communicate, for the presence of error-related signals. We first establish the presence of an ErrP originating from the dorsolateral pre-frontal cortex (dLPFC) in response to errors made during a discrete feedback task that mimics the click-based spelling software she uses to communicate. Then, we show that this ErrP can also be elicited by cursor movement errors in a continuous BCI cursor control task. This work represents a first step toward detecting ErrPs during the daily home use of a communications BCI.

Natural Speech Signal Recognition Algorithm

Speech recognition technologies are becoming more and more part of our lives, providing a convenient way to control a variety of electronic devices - voice control. One of the current problems that is solved in the development of such control systems is the problem of insufficient accuracy of voice command recognition. Improvements are being made to increase reliability, independence from individual voice characteristics, and reduce the negative impact of background noise on recognition quality. The paper presents an algorithm for recognizing and processing user intentions using a neural network built on the principle of understanding natural language and processing audio signals for use in the user support system.

Design of Tendon-Actuated Robotic Glove Integrated with Optical Fiber Force Myography Sensor

People taken by upper limb disorders caused by neurological diseases suffer from grip weakening, which affects their quality of life. Researches on soft wearable robotics and advances in sensor technology emerge as promising alternatives to develop assistive and rehabilitative technologies. However, current systems rely on surface electromyography and complex machine learning classifiers to retrieve the user intentions. In addition, the grasp assistance through electromechanical or fluidic actuators is passive and does not contribute to the rehabilitation of upper-limb muscles. Therefore, this paper presents a robotic glove integrated with a force myography sensor. The glove-like orthosis features tendon-driven actuation through servo motors, working as an assistive device for people with hand disabilities. The detection of user intentions employs an optical fiber force myography sensor, simplifying the operation beyond the usual electromyography approach. Moreover, the proposed system applies functional electrical stimulation to activate the grasp collaboratively with the tendon mechanism, providing motion support and assisting rehabilitation.

Digital nativism: Twitter, migration discourse and the 2019 election

Given its significance for society’s character, future and identity, migration has dominated media discourse. At present, the ascendance of digital platforms which broaden opportunities to produce, share and access content online has ignited debates about migration’s discursive construction. Often approached as promoting tolerance and inclusivity, social media are also believed to unleash xenophobia and intergroup antagonism. Working with a cross-section of tweets from the 2019 Canadian Federal election, this article asks how was migration framed, which users influenced the flow and substance of discourse and did Twitter diverge from conventional media space? It finds, while chains of citizen-users overwhelmingly employed Twitter to distribute original content, anti-immigration communications and actors were disproportionately featured. Considering these results, this article introduces the concept of digital nativism to clarify how technical affordances, user intentions and wider socio-political conditions intersect to produce emergent patterns of anti-immigration discourse and mobilization that are participatory, interactive and broadly distributed.

Bayesian Networks for software testing

A software design methodology is proposed that involves development of approximate models based on Bayesian Networks capturing probabilistic representations of expected behavior, which are further used in developing and running tests that can dynamically diagnose bugs and attacks during production. While automation of Software design is still a very remote goal, it can already benefit from AI tools and ideas. One of the main challenges with automating software design methods, for any product with modest complexity, is the mere intractability of enumerating all scenarios of the product usage when also taking into account user intentions. This leads to an intractability of generating exact specifications and exhaustive tests. We show how approximate models of the design can exploit AI techniques to represent the system and to derive meaningful tests, warning when the environment is not behaving as designed, detecting bugs and attacks. The representation can use Bayesian Networks that are rather simple, enabling usage by novice practitioners. We validate the methodologies with on two different applications: a device driver for Wi-Fi Direct, and a website, MindBlog.com. In the Wi-Fi Direct use case, we successfully built a test ensuring the connection is fair and contrasted it experimentally to earlier work where we created a robust Bayes network based on expert knowledge. In the MindBlog.com use case, we show that the procedure is flexible and can detect when the developers found a bug and were attempting to debug their application yielding anomalous behavior.

An Enhanced Model For E-Service Quality of Mobile Banking

The increasing growth in mobile device users and the rapid drop in conventional and mobile data charges have given way for a provision of banking services and mobile banking to be precise. Banks are now extending their services from traditional means of banking to a self-service system. Recently, mobile banking has been growing exponentially, but there is still a lack of confidence by the users because of low e-service quality of mobile banking. Researchers have done many works on mobile banking but were focused more on adoption and user intentions which contributes to the marketing and promotion of mobile banking. Mobile banking has some specific characteristic which makes it different from other web-based e-services. So the previous studies lack specific in-depth mobile banking e-service quality, such as failure to define the e-service quality of mobile banking and identification of mobile banking dimensions. This study identified mobile banking's dimensions and attributes that received less attention and suggested an improved model for mobile banking's electronic service quality.