EEG-based endogenous online co-adaptive brain-computer interfaces: strategy for success?

A Brain-Computer Interface (BCI) translates patterns of brain signals such as the electroencephalogram (EEG) into messages for communication and control. In the case of endogenous systems the reliable detection of induced patterns is more challenging than the detection of the more stable and stereotypical evoked responses. In the former case specific mental activities such as motor imagery are used to encode different messages. In the latter case users have to attend sensory stimuli to evoke a characteristic response. Indeed, a large number of users who try to control endogenous BCIs do not reach sufficient level of accuracy. This fact is also known as BCI “inefficiency” or “illiteracy”. In this paper we discuss and make some conjectures, based on our knowledge and experience in BCI, on whether or not online co-adaptation of human and machine can be the solution to overcome this challenge. We point out some ingredients that might be necessary for the system to be reliable and allow the users to attain sufficient control.C. Vidaurre was supported by grant number RyC-2014-15671 of the Spanish MINECO

Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

Sensorimotor rhythm brain-computer interface – A game-based online co-adaptive training

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica) Universidade de Lisboa, Faculdade de Ciências, 2018Brain-Computer Interface (BCI) technology translates brain signals into messages. BCI users are thus enabled to interact with the environment by thought, or more generally speaking by mental processes. Event-related desynchronization (ERD) based BCIs use the detection of changes in the spontaneous electroencephalogram (EEG) signal. Different mental processes induce power decreases (ERD) or increases (event-related synchronization, ERS) in different frequencies and different areas of the brain. These differences can be measured and classified. Operating a non-invasive EEG based sensorimotor rhythm BCI is a skill that typically requires extensive training. Lately, online co-adaptive feedback training approaches achieved promising results after short periods of training. Does this also mean that users can have meaningful BCI-based interactions after training, when the BCI is no longer adapting, like in a real- life scenario? To answer this question an online study was conducted with 20 naïve (first time) users. After a short (less than 20 minutes) setup, the users trained to gain BCI control by playing a Whack- A-Mole game where they would have to perform Motor Imagery (imagination of a specific movement- MI) to control a hammer to hit a mole. The game was played for about 30 minutes. During this time, the user learns to perform MI with online feedback from the game and the BCI parameters recurrently adapt to the user’s EEG patterns every~1minute. This recurrent adaptation allows different users to use slightly different strategies and produce ERDs in different frequencies and brain areas without loss of performance. After 30 minutes of training the adaptation was stopped and the users continued playing the game with the trained BCI for another 20 minutes. The BCI parameters were calibrated with data from the adaptive stage and kept fixed in the last 20 minutes. Our hypothesis is that once a system was co-adaptively trained it can maintain its performance without recurrent adaptation. Eighteen out of the twenty users were able to control the BCI and play the game. Seventeen out of the eighteen were able to improve or keep performance between adaptive and non-adaptive stage. These results seem to suggest that online co-adaptation is an effective way to gain BCI control

Multisensory learning in adaptive interactive systems

The main purpose of my work is to investigate multisensory perceptual learning and sensory integration in the design and development of adaptive user interfaces for educational purposes. To this aim, starting from renewed understanding from neuroscience and cognitive science on multisensory perceptual learning and sensory integration, I developed a theoretical computational model for designing multimodal learning technologies that take into account these results. Main theoretical foundations of my research are multisensory perceptual learning theories and the research on sensory processing and integration, embodied cognition theories, computational models of non-verbal and emotion communication in full-body movement, and human-computer interaction models. Finally, a computational model was applied in two case studies, based on two EU ICT-H2020 Projects, "weDRAW" and "TELMI", on which I worked during the PhD

Spatio–Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification

The brain plays a pivotal role in locomotion by coordinating muscles through interconnections that get established by the peripheral nervous system. To date, many attempts have been made to reveal the underlying mechanisms of humans' gait. However, decoding cortical processes associated with different walking conditions using EEG signals for gait-pattern classification is a less-explored research area. In this paper, we design an EEG-based experiment with four walking conditions (i.e., free walking, and exoskeleton-assisted walking at zero, low, and high assistive forces by the use of a unilateral exoskeleton to right lower limb). We proposed spatio-spectral representation learning (SSRL), a deep neural network topology with shared weights to learn the spatial and spectral representations of multi-channel EEG signals during walking. Adoption of weight sharing reduces the number of free parameters, while learning spatial and spectral equivariant features. SSRL outperformed state-of-the-art methods in decoding gait patterns, achieving a classification accuracy of 77.8%. Moreover, the features extracted in the intermediate layer of SSRL were observed to be more discriminative than the hand-crafted features. When analyzing the weights of the proposed model, we found an intriguing spatial distribution that is consistent with the distribution found in well-known motor-activated cortical regions. Our results show that SSRL advances the ability to decode human locomotion and it could have important implications for exoskeleton design, rehabilitation processes, and clinical diagnosis