This lesson describes the current state of brain-computer interface (BCI) standards, including the present obstacles hindering the forward movement of BCI standardization as well as future steps aimed at solving this problem. 

This lecture covers the ethical implications of the use of brain-computer interfaces, brain-machine interfaces, and deep brain stimulation to enhance brain functions and was part of the Neuro Day Workshop held by the NeuroSchool of Aix Marseille University.

Panel discussion by leading scientists, engineers and philosophers discuss what brain-computer interfaces are and the unique scientific and ethical challenges they pose. hosted by Lynne Malcolm from ABC Radio National's All in the Mind program and features:

• Dr Hannah Maslen, Deputy Director, Oxford Uehiro Centre for Practical Ethics, University of Oxford
• Prof. Eric Racine, Director, Pragmatic Health Ethics Research Unity, Montreal Institute of Clinical Research
• Prof Jeffrey Rosenfeld, Director, Monash Institute of Medical Engineering, Monash University
• Dr Isabell Kiral-Kornek, AI and Life Sciences Researcher, IBM Research
• A/Prof Adrian Carter, Neuroethics Program Coordinator, ARC Centre of Excellence for Integrative Brain Function

In this module you will learn the basics of Brain Computer Interface (BCI). You will read an introduction to the different technologies available, the main components and steps required for BCI, associated safety and ethical issues, as well as an overview about the future of the field.

In this module, users will learn about the different types of neurotechnology and how each of them works. This will be done through the metaphor of going to a symphony... in your brain. Like a symphony, brain processes emerge from collections of neural activity. This video encourages us to imagine ourselves moving to different areas in the concert hall to understand where different technologies interface. Once the concert ends, we talk about underlying neural mechanisms and technology that allow researchers and innovators to interact with the brain.

This module addresses how neurotechnology is currently used for medical and non-medical applications, and how it might advance in the future.

This module covers a brief history of the neurotechnology industry, bringing the history of brain-computer interfacing to life through engaging skits and stories. 

This module covers many types of invasive neurotechnology devices/interfaces for the central and peripheral nervous systems. Invasive neurotech devices are crucial, as they often provide the greatest accuracy and long-term use applicability.

This module covers many of the types of non-invasive neurotech and neuroimaging devices including electroencephalography (EEG), electromyography (EMG), electroneurography (ENG), magnetoencephalography (MEG), and more. 

Neuromodulation refers to devices that influence the firing of neurons which can be useful in many medical applications. This modules covers what neuromodulation is, how it affects the functioning of neurons, and the many forms that these devices take on.

This modules covers neuroprosthetic and cognitive enhancement devices that can help augment our capabilities by enhancing memory, as well as restoring or improving our senses. 

This module goes over the methods that neurotechnologists use to turn brain data into commands a computer or a machine can understand. We cover data collection, processing, filtering, analysis, how to generate an action in a device, asynchronous BCIs that use population encoding, and synchronous BCIs that use P300, SSVEP, N100, and N400 signals. 

This module covers the many things that brain-computer interfaces can and will be able to do, including motor neuroprosthetics like prosthetic arms, exosuits, and vehicle control, as well as computer and machine interfacing use-cases.

This module covers how neurotechnology is perceived in media today. We discuss a few specific films and talk about how the perception of neurotechnology changes with our media. Finally, we introduce a few interesting terms related to ethics and address some future issues the technology may cause.

What will happen to the mind and our personalities when we start modifying our brains and bodies with technology? What is the mind and how should we think about it? What is a cyborg and what makes them human? Where is the line between these? This video invites us to think about what the future of consciousness might look like. 

In this session the Medical Informatics Platform (MIP) federated analytics is presented. The current and future analytical tools implemented in the MIP will be detailed along with the constructs, tools, processes, and restrictions that formulate the solution provided. MIP is a platform providing advanced federated analytics for diagnosis and research in clinical neuroscience research. It is targeting clinicians, clinical scientists and clinical data scientists. It is designed to help adopt advanced analytics, explore harmonized medical data of neuroimaging, neurophysiological and medical records as well as research cohort datasets, without transferring original clinical data. It can be perceived as a virtual database that seamlessly presents aggregated data from distributed sources, provides access and analyze imaging and clinical data, securely stored in hospitals, research archives and public databases. It leverages and re-uses decentralized patient data and research cohort datasets, without transferring original data. Integrated statistical analysis tools and machine learning algorithms are exposed over harmonized, federated medical data.

The Medical Informatics Platform (MIP) is a platform providing federated analytics for diagnosis and research in clinical neuroscience research. The federated analytics is possible thanks to a distributed engine that executes computations and transfers information between the members of the federation (hospital nodes). In this talk the speaker will describe the process of designing and implementing new analytical tools, i.e. statistical and machine learning algorithms.  Mr. Sakellariou will further describe the environment in which these federated algorithms run, the challenges and the available tools, the principles that guide its design and the followed general methodology for each new algorithm. One of the most important challenges which are faced is to design these tools in a way that does not compromise the privacy of the clinical data involved. The speaker will show how to address the main questions when designing such algorithms: how to decompose and distribute the computations and what kind of information to exchange between nodes, in order to comply with the privacy constraint mentioned above. Finally, also the subject of validating these federated algorithms will be briefly touched.

The Medical Informatics Platform (MIP) Dementia had been installed in several memory clinics across Europe allowing them to federate their real-world databases. Research open access databases had also been integrated such as ADNI (Alzheimer’s Dementia Neuroimaging Initiative), reaching a cumulative case load of more than 5,000 patients (major cognitive disorder due to Alzheimer’s disease, other major cognitive disorder, minor cognitive disorder, controls). The statistic and machine learning tools implemented in the MIP allowed researchers to conduct easily federated analyses among Italian memory clinics (Redolfi et al. 2020) and also across borders between the French (Lille), the Swiss (Lausanne) and the Italian (Brescia) datasets.

This talks presents ethics requirements of the Medical Informatics Platform, a data sharing platform for medical data using data federation mechanisms. The talk presents how the Medical Informatics Platform (MIP) works and which ethical requirements need to be considered when working with federated data.

This lecture talks about the usage of knowledge graphs in hospitals and related challenges of semantic interoperability.