Postdoc Enhancing Human-Robot Interactions using BCI – DelftUniversityofTechnology(TUDelft) – Delft

  • Delft

DelftUniversityofTechnology(TUDelft)

When it comes to brain activity (electroencephalohgraphy – EEG) based brain-computer interfaces (BCIs), the high individual variability in EEG activity patterns, caused by (bio)neurological factors, often leads to the failure of classification algorithms. Research indicates that around 30% of individuals are entirely incapable of operating a BCI, a phenomenon termed as ‘BCI illiteracy’. Consequently, while advancements in portable and user-friendly EEG headsets may make BCIs available for everyday use, and even for enhancing industrial performance in the near future, the current classification algorithms still require considerable enhancements to enable BCI use for everyone.

A key aspect often overlooked in this pursuit is the use of attentional mechanisms, such as signals that can be recorded using eye-tracking. Real-time attentional measurements could provide valuable data streams to inform the development and operation of BCIs. However, current systems do not capitalize on this wealth of information, which could be a potential game-changer for the technology. Moreover, there appears to be an excessive focus solely on algorithms rather than considering algorithms in their actual context: It is therefore crucial to obtain a further understanding of how BCIs can be used in real-world scenarios.

As a postdoc on this project you will obtain a detailed image of the brain processing and unravel features determining individual differences in BCI task performance. By employing a combination of structural MRI, EEG, and eye-tracking we want to trace the differences in elicited EEG signals between subjects, when exposed to identical stimuli, back to their respective bio-neurological basis through a technique called source localisation. The results should enable you to explain individual differences in BCI performance based on existing classification algorithms and pave the way for the development of improved classification techniques.

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