Affect Aware Brain++ Computer Interfaces

 

Innovative solutions in the intersection of neuroinformatics, brain computer interfaces, affective computing. Scale and democratize applications for human-computer interaction in the context of well-being, assistive technologies and mental health.

 

Our Mission

  • Brain is linked to unresolved mysteries. Neural signals are not reproduced across individuals. AI and machine learning (ML) are still superficially or not employed at all, without any large scale proven generalization across subjects. This generalization is kind of a "holy grail", to support the democratization and applicability of such systems at scale.

 

  • At the same time affective, mental, cognitive states are challenging to monitor, classify or detect, whereas Human Computer Interaction is still in the "stone age" since interfaces are far away from being natural and aware of the mental/affective states.

 

  • Our mission is to tackle within this context, the generalization across individuals blending the areas of brain neural signals, neuroinformatics, BCIs, eye-tracking, virtual reality while leveraging machine/deep learning superpowers.
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Use Cases / Solutions

We introduce novel ML/DL-powered interfaces that employ non-invasive brain computer sensors to function across users  requiring minimal calibration data.

Our aim is to interface  as seamlessly and efficiently with systems/devices via AI solution that generalize. This is downstream  linked to novel multimodal affective aware applications within an VR/AR setting in well-being related use cases. Up to now developed cases include the following:

  1. Motor imagery for brain-controlled applications; Assistive Technology
    • Accessibility, actual or virtual mobility,
  2. Cognitive rehabilitation
    • Monitor cognition, predict training outcomes, BCI-triggered interventions for cognitive functions e.g., Attention, Memory, Visuospatial abilities, Enhanced BCI-based Neurofeedback, e.g. Attention deficits, Learning, Stress
  3. Mental state monitoring*
    • Detect mental/physical workload, prevent overload.

Such pipelines can be employed for  Motor/Cognitive Rehabilitation, Relaxation Therapies, Pain Reduction.

Some of these approaches can be also employed in “milder” well-able cases out of the rehabilitation context such as personal mental state, emotion recognition, work-cycle fatigue and so on and so forth.

The above use cases are co-developed with Panagiotis Kourtesis, Serafeim Perdikis; *+Ioanna Zioga.

 

More use cases available upon request.

Stay tuned or talk to us for further custom development; upcoming focused use-cases  are on the way.

The opportunity

Technology 

> State- of-the- art ML/DL

>Transfer Learning, Self-supervised learning, ++

>Explainable AI

> Leverage massive datasets

 

Applications

> Brain-controlled devices / BCI for Assistive Technology

> Neurorehabilitation - Motor Rehabilitation

>Cognitive Rehabilitation/Training

> Mental State Monitoring/ Relaxation/ Pain reduction

 

EEG integrated with other modalities such as eye tracking all together embedded into AR/VR headsets empowered by state-of the art deep learning and transfer learning approaches can open new directions  towards novel affect-aware human computer interfaces, and assistive techologies.

Why Us?

Team 

Exceptional combination of top-tier outstanding senior specialists with rich experience and backgrounds across all interdisciplinary areas involved.

Technology

Large-scale data and advanced Deep Learning  to boost the domain related expertise are uniquely combined.

Potential

Plenty of downstream use cases in multiple directions: well-being applications, mental health, entertainment, personal monitoring, virtual working experience, work-cycle fatigue, meetings' attention,  neuromarketing to name but a few.

The Team

Internal Team

AgamemnonKrassoulis
NikosAntonopoulos
gkinis
sartzetaki (1)
antoniadis-main

Agamemnon Krasoulis 

Senior ML Research Engineer

Nikos Antonopoulos

ML Research Engineer

Ioannis Gkinis

ML Engineer/SW developer

Christina Sartzetaki 

ML Engineer

Panos Antoniadis

ML Engineer

External collaborators/advisors

perdikis
kourtsis
Screenshot 2022-09-22 at 16.54.13-modified
klados
dimitriadis

Serafeim Perdikis

Experienced BCI Research Engineer, Lecturer at Uni of Essex

Linkedin

Panagiotis Kourtesis

Experienced VR & HCI, Researcher, and Cognitive Neuroscientist, at Inria

Linkedin 

Ioanna Zioga

Postdoctoral Researcher at the Donders Institute for Brain, Cognition and Behaviour, the Netherlands

Linkedin 

Manousos Klados

Experienced BCI, Aff. Comp. Research Scientist, Prof. at CITY College/York Uni.

Linkedin

Stavros Dimitriadis

Experienced in Neuroinformatics
Experienced in Multi-modal Neuroimaging in Healthy and Clinical Populations
University of Barcelona, Catalonia, Spain

Linkedin

 

Call for participation

Are you an expert in the above fields ?

Work for or represent a related industry and seek for a partnership?

Do you seek for a team with high-expertise in the interdisciplinary fields of AI/deep-learning, neuroinformatics/BCIs ?

We are in the process of further fund-raising, building of partnerships, of joint ventures, together with strategic advisors and collaborators.