Algebra and Analysis with applications
Automated Software language and Software engineering
Complex Real-Time Embedded Systems
Simulation and optimisation for future industrial applications (SOFIA)
Energy efficiency and reduction of emissions
Learning and Optimisation
Real-Time Systems Design
Software Testing Laboratory
RECOG: Closed-loop neurofeedback innovation for cognitive rehabilitation
The objective of RECOG is to develop new technology for innovative brain-training for people with cognitive deficits. The ability to focus our attention on relevant information, maintain and manipulate this information during a short period of time (our Working Memory, WM), is central for human cognition.
Project manager at MDH
The objective of RECOG is to develop new technology for innovative brain-training for people with cognitive deficits. The ability to focus our attention on relevant information, maintain and manipulate this information during a short period of time (our Working Memory, WM), is central for human cognition. It has been shown to influence reasoning and academic performance and in many psychiatric disorders, such as ADHD, depression, substance abuse, and stress, WM capacity is degraded. An efficient technique to enhance our cognitive abilities related to attention and WM capacity would bring substantial benefits for a large number of people. RECOG will apply novel neurotechnology in which cognitive information, extracted from brain and eye-tracking data using Machine-Learning (ML) and Artificial Intelligence (AI), is provided in real time as feedback (neurofeedback). This technology allows to directly train brain networks related to cognition in order to promote long-term changes in the brain to treat different brain disorders.
As a technical advancement, RECOG will develop a closed-loop feedback system with real-time data acquisition, signal processing (applying ML and AI) and feedback generation in LabVIEW with MATLAB-integrated scripts. The potentials and scalability of such a system, carrying out real-time signal processing and pattern recognition of time series data with the generation of continuous feedback directly related to the ongoing process, are immense to the industry, particularly in complex processes that require continuous monitoring for fault detection.
RECOG will be carried out in close collaboration between the academy and the industry. The project is composed of leading actors with complementing expertise; Mälardalen University, experts in neural signal processing, ML and AI; Smart Eye AB, global leader in providing AI-powered EYE-tracking technology; National Instruments Sweden AB, leading experts in test and acquisition systems and Prevas AB, experts in AI and feedback systems. RECOG aims at creating valuable research collaboration between the four partners with the aim to output novel knowledge of high scientific quality into company activities. The long-term intention is to create a strong collaboration platform that will attract other stakeholders and lead to new innovations.
RECOG is further expected to establish a new dimension to the research area of Sensor Systems and Health at Mälardalen University with main focus on neural engineering research. It has the potential to increase visibility both at a national and international level attracting both young researchers and new students. Cutting-edge neural engineering research carried out in RECOG in close collaboration with industry will be directly transferred to the educational environment creating a strong bond between education and research.