ON THE INTERPLAY BETWEEN BRAIN-COMPUTER INTERFACES AND MACHINE LEARNING ALGORITHMS: A SYSTEMS PERSPECTIVE

Today, computer algorithms use traditional human-computer interfaces (e.g., keyboard, mouse, gestures, etc.), to interact with and extend human capabilities across all knowledge domains, allowing them to make complex decisions underpinned by massive datasets and machine learning. Machine learning has seen remarkable success in the past decade in obtaining deep insights and recognizing unknown patterns in complex data sets, in part by emulating how the brain performs certain computations. As we increase our understanding of the human brain, brain-computer interfaces can benefit from the power of machine learning, both as an underlying model of how the brain performs computations and as a tool for processing high-dimensional brain recordings. The technology (machine learning) has come full circle and is being applied back to understanding the brain and any electric residues of the brain activity over the scalp (EEG). Similarly, domains such as natural language processing, machine translation, and scene understanding remain beyond the scope of true machine learning algorithms and require human participation to be solved. In this work, we investigate the interplay between brain-computer interfaces and machine learning through the lens of end-user usability. Specifically, we propose the systems and algorithms to enable synergistic and user-friendly integration between computers (machine learning) and the human brain (brain-computer interfaces). In this context, we provide our research contributions in two interrelated aspects by, (i) applying machine learning to solve challenges with EEG-based BCIs, and (ii) enabling human-assisted machine learning with EEG-based human input and implicit feedback.Ph.D

Proceedings of the 19th Sound and Music Computing Conference

Proceedings of the 19th Sound and Music Computing Conference - June 5-12, 2022 - Saint-Étienne (France). https://smc22.grame.f

Investigating emerging self-awareness : its neural underpinnings, the significance of self-recognition, and the relationship with social interactions

Up until now, self-recognition in the mirror, achieved at around 18 months, has been used to assess self-awareness in infancy. Even though the significance of this test is not universally accepted, this field has progressed very little over the last decades, in contrast to a broad volume of literature on the self in adults. However, a relationship between self-other differentiation and social cognitive abilities has been recently hypothesized, renewing the interest in mechanisms underlying emerging self-awareness. Adult studies have highlighted that brain networks, instead of isolated brain areas, support self-processing. Therefore, the first two studies of this thesis validated the use of advanced connectivity analyses on infant fNIRS data. Making use of these methods, one study demonstrated that functional connectivity between regions belonging to a network that has been related to abstract self-processing in adults gradually increases over the first two years of life. The same network was found to characterise infants who recognise themselves in the mirror. In another study, crucial regions of this network were shown to be engaged during self-recognition in 18-month-olds. As social interactions have been suggested to be fundamental for the construction of the self, the last two studies of this thesis investigated the relationship between emerging self-awareness and social interactions. To test this, I focused on mimicry, known to play an important role in affiliation and in mediating relationships. One study demonstrated that emerging selfawareness may affect infants’ tendency to selectively mimic in-group members, which may indicate a possible role of self-comparison and identification processes. The last study did not find evidence for a relationship between mothers’ tendency to imitate their infants at 4 months and emerging selfawareness. Taken together, these studies enrich our understanding of the mechanisms underlying emerging self-awareness and they represent a pioneering starting point for further investigations into this topic