BrainData – Modular software for synchronous data recording from BCI (original) (raw)
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Mathematics
Advances in the field of Brain-Computer Interfaces (BCIs) aim, among other applications, to improve the movement capacities of people suffering from the loss of motor skills. The main challenge in this area is to achieve real-time and accurate bio-signal processing for pattern recognition, especially in Motor Imagery (MI). The significant interaction between brain signals and controllable machines requires instantaneous brain data decoding. In this study, an embedded BCI system based on fist MI signals is developed. It uses an Emotiv EPOC+ Brainwear®, an Altera SoCKit® development board, and a hexapod robot for testing locomotion imagery commands. The system is tested to detect the imagined movements of closing and opening the left and right hand to control the robot locomotion. Electroencephalogram (EEG) signals associated with the motion tasks are sensed on the human sensorimotor cortex. Next, the SoCKit processes the data to identify the commands allowing the controlled robot loc...
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Real-Time Monitoring of Neural State in Assessing and Improving Software Developers' Productivity
Productivity has always been considered a crucial factor for the success of any business, and the same applies to software development. As a result of software development being almost entirely a cognitive task, problems in cognition highly correlate to problems in productivity. Being able to monitor the neural state of developers in real-time can aid in detecting and handling such cognitive problems before they occur and cause any damage. This also means aiding software developers in taking sufficient breaks, assigning tasks appropriate to their knowledge level, managing deadlines and stress, and so on. In this paper we propose Emendo-a conceptual system for continuous monitoring of developers' neural state using an off-the-shelf device. Furthermore, we provide a pilot study on the usability and feasibility of the proposed device for continuous monitoring. We also provide a short discussion of the ethical and acceptance issues of monitoring systems. Our goal is to introduce the possibility of real-time neural state monitoring and its potential benefits to the research community, hopefully attracting more researchers in this research field.
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2017
In this report we present a dataset that combines multimodal biosignals and eye tracking information gathered under a human-computer interaction framework. The dataset was developed in the vein of the MAMEM project that aims to endow people with motor disabilities with the ability to edit and author multimedia content through mental commands and gaze activity. The dataset includes EEG, eye-tracking, and physiological (GSR and Heart rate) signals along with demographic, clinical and behavioral data collected from 36 individuals (18 able-bodied and 18 motor-impaired). Data were collected during the interaction with specifically designed interface for web browsing and multimedia content manipulation and during imaginary movement tasks. Alongside these data we also include evaluation reports both from the subjects and the experimenters as far as the experimental procedure and collected dataset are concerned. We believe that the presented dataset will contribute towards the development ...
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