BIT&BRAIN TECHNOLOGIES

Human-Centric Marketing is the current trend in marketing that looks at customers holistically, recognizing their emotions as one of the most important dimensions. All experts indicate that only the companies that adapt to this trend will successfully survive the current competitive and global environment. However, traditional market methodologies cannot objectively measure emotional insights as this information is unconscious and cannot be accessed under rational processes. BitBrain has already developed a neurotechnology solution able to measure emotions completely adapted to the market research sector, and the objective of the usenns project is to penetrate the market. This solution has 3 main innovations: i) two new measurement devices (neuroheadset and a “ring”) adapted to the market research sector; ii) the encapsulation and integration within the technology of all the neuroscience knowledge on design-execution of experiments and on elaborated data analysis, in a completely transparent manner for the user; and iii) presentation of the results on emotions of consumers in a comprehensible and valuable language for the market researcher and its client. This results in a technology easy to implement in market research companies without incurring a risk due to high initial investments (clients pay an affordable price for the measurement devices), and a pay-per-use with a reasonable price only charged at the end of a study. This pay-per-use service with a visualization and interaction web-based application make this business model highly scalable. The objectives of the feasibility project are to accelerate the process of corroborating the business plan, and to define an action plan to reach mainstream commercial use from the current prototype state. After the feasibility study, the action plan will bring to the market a solution that will revolutionize the global market research sector by helping companies to design better strategies adapted to their customers

Cognitive decline (progressive loss of mental cognitive abilities) is a natural event that affects us all after the age of 40. Although age is a main factor, low-demanding brain activity can speed up the process & end up in cognitive impairment, which is a major cause of disability worldwide. Importantly, the onset & progress of cognitive decline can be hindered with preventive training that stimulates cognitive abilities. This realization is driving the market of cognitive training, foreseeing a $7.5 billion turnover and 83% population adoption by 2020. This growth will be mostly driven by the revolution of wearables & biometric technologies, which are increasing rapidly as people become more prone to manage their own health. However, current market offerings for brain training either need the presence of a clinical specialist or lack any scientific relevance. In response, we at BitBrain Technologies have developed ELEVVO, a ground-breaking neuro-wearable technology that enables self-management of cognitive welfare and yields real measurable enhancement. ELEVVO uses a gaming approach to train cognitive-related patterns, relying on intelligent big-data mining to automatically tailor the challenges to the users’ brain rhythms and to measure the progress achieved. Importantly, ELEVVO will be the only scientifically-validated product for cognitive training in the mass market. Indeed, we have proved the ELEVVO’s efficiency in healthy adults (students, workers, elderly) and in neuropathology cases. Altogether, with this project we aim to finalise the development of ELEVVO and to scale-up our deployment capability. The Feasibility Study planned in Phase 1 will help us warrant the project from all technical, commercial & financial standpoints. Upon completion, ELEVVO will bring a major social & economic impact to society, enhancing life-quality & well-being. In turn, it will boost the growth of our company, projecting a R..

30% of adults in industrialized countries such as the United States, United Kingdom, Japan, Germany, and Canada do not get good quality sleep on a regular basis. Insufficient and poor sleep has been declared a "public health problem" as it is associated with a range of negative health and social outcomes, including unsuccess in school and in the labor market. In economic terms, poor sleep - or the lack of it - leads to significant economic losses (up to 411 and 138 B$/year in the US and Japan, respectively), and there is growing evidence indicating a strong association between short sleep duration and high mortality risks. This project explores the implementation of a new neurotechnology to improve sleep quality, aiming to penetrate the Global Sleep Tech Device Market that exceeded USD 9 billion in 2018 and is set to achieve over 16% CAGR by 2025, surpassing USD 27 billion. The novel, consumer-grade device is based on a unique EEG/TCS developed by Bitbrain and is a very recent research breakthrough finding in sleep quality improvement. The rationale of this project is Bitbrain’s need to hire highly qualified personnel in Sleep and EEG/TCS technology to explore this innovation, in combination with the difficulty of the company to access this type of personnel. The impact of the recruitment will be the implementation of a sleep R&D area in the company, and he/she will benefit from working in a very interdisciplinary R&D team with many training possibilities. The specific objectives of the innovation associate will explore the feasibility of employing this new wearable EEG/TCS for sleep quality improvement, evaluate its effectiveness in laboratory settings, and verify the potential to transfer these results to the real world. If successful, this new technology will be the first of its class to potentially impact a very important social issue effectively.

The long term vision in BAYFLEX is to create a radically new technology that uses low cost, green organic electronics for probabilistic computing in order to allow continuous and private monitoring of bio-signals on flexible substrates. The vision of flexible green AI sensors with on chip classification extends well beyond biomedical devices and the democratization of health care, with the possibility to transform sensor data at the edge of large networks. To achieve our goal, BAYFLEX will demonstrate a patch using active physiological sensors based on organic materials that interface with the soft human body and that also includes classification circuits (~ 100 transistors) fabricated using Thin Organic Large Area Electronics (TOLAE) processes. These circuits use spiking neurons realized in Organic Thin Film Transistors (OTFTs) to transform the non-stationary electrical signals from the sensors into stochastic bit streams. Bayesian inference is then used to classify the data using circuits of cascaded Muller C-elements. Taking advantage of the unique properties of organic electrochemical transistors (OECTs), low transistor count dynamic Muller C-elements are targeted. The patch will be tested on a simple task using healthy humans. The project brings together an interdisciplinary consortium with expertise in modeling emerging devices, biologically inspired circuit design, experts in machine learning involving electrophysiological data (including an SME) and teams with expertise in OTFT and OECT fabrication. BAYFLEX targets dissemination to a variety of publics including: scientists via publications in (open access) high impact journals and conferences; industrials and end-users through an industrial advisory board, a workshop and demonstrations at targeted conferences; the general public with the creation of a transferable workshop for non-scientific communities and training the next generation of experts through specialized schools and workshops.

More than half of the persons with spinal cord injuries (SCI) are suffering from impairments of both hands, which results in a tremendous decrease of quality of life (QoL) and represents a major barrier for inclusion in society. Functional restoration is possible with neuroprostheses based on functional electrical stimulation (FES). However, current systems are non-intelligent, non-intuitive open loop systems without sensory feedback. MoreGrasp aims at developing a multi-adaptive, multimodal user interface including brain-computer interfaces (BCIs) for intuitive control of a semi-autonomous motor and sensory grasp neuroprosthesis to support activities of daily living in individuals with SCI. With such a system a bilateral grasp restoration may become reality. The multimodal interfaces will be based on non-invasive BCIs for decoding of movements intentions with gel-less electrodes and wireless amplifiers. The neuroprosthesis will include FES electrode arrays and different sensors to allow for implementation of predefined or autonomously learned sequences. MoreGrasp will consequently follow the concept of the user-centered design by providing a scalable, modular, user-specific neuroprosthesis together with personalized EEG recording technology. Novel multimodal software architectures including interoperability standards will be defined to integrate neuroprostheses into the field of assistive technology. Long-term end user studies will demonstrate the reliability, usefulness and impact on QoL of the MoreGrasp technology. A web-based service infrastructure including a discussion forum will be set up for assessing user priorities and screening of users’ status. The evaluation of the training and patterns of use will allow for user modeling to identify factors for successful use. The highly interdisciplinary MoreGrasp consortium consists of members from universities, industry and rehabilitation centers, which have a long history of successful cooperation.