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

Controlling Assistive Machines in Paralysis Using Brain Waves and Other Biosignals

The extent to which humans can interact with machines significantly enhanced through inclusion of speech, gestures, and eye movements. However, these communication channels depend on a functional motor system. As many people suffer from severe damage of the motor system resulting in paralysis and inability to communicate, the development of brain-machine interfaces (BMI) that translate electric or metabolic brain activity into control signals of external devices promises to overcome this dependence. People with complete paralysis can learn to use their brain waves to control prosthetic devices or exoskeletons. However, information transfer rates of currently available noninvasive BMI systems are still very limited and do not allow versatile control and interaction with assistive machines. Thus, using brain waves in combination with other biosignals might significantly enhance the ability of people with a compromised motor system to interact with assistive machines. Here, we give an overview of the current state of assistive, noninvasive BMI research and propose to integrate brain waves and other biosignals for improved control and applicability of assistive machines in paralysis. Beside introducing an example of such a system, potential future developments are being discussed

Comparing EEG-neurofeedback visual modalities between screen-based and immersive head-mounted VR

Tese de Mestrado Integrado, Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas), 2022, Universidade de Lisboa, Faculdade de CiênciasNeurofeedback (NF) can be defined as a form of biofeedback that trains subjects to have self-control over brain their functions, by providing real-time feedback of their own cerebral activity. This activity can be presented in various forms, with auditory and visual feedback being the most common. Recently, NF has been investigated as a potential treatment for various clinical conditions associated with abnormal brain activity or cognitive capacities. However, the greater research focus is not discussing how the feedback should be presented. The chosen modality for any NF training system may strongly influence the training protocol and consequently the outcome of the experiment. In this thesis, a systematical comparison between two different type of visual modalities (ScreenBased vs. immersive-virtual reality (VR) ) was performed with the goal to evaluate the effectiveness of each modality on the NF training results. Data from two previous studies, recorded on healthy participants, in protocols that targeted the increase in the upper alpha (UA) band power measured at the EEG electrode Cz was used. This was then divided into two modality groups: Screen-Based modality group (N = 8) and the Immersive-VR group (N = 4). An extensive data processing and cleaning protocol was applied to both groups and the training effectiveness was measured through band power calculation, the definition of learning ability indexes and the application of statistical tests. Results showed that, both groups had a generally positive training effect within sessions, however data regarding different sessions is inconclusive and does not show clear evidence of up-regulation of the target feature. Additionally, when only considering within-session evolution, only the Immersive-VR modality group was able to maintain an increasing trend in all sessions. One of the main limitations of this study was the sample size, which was too small to determine the precise effect of NF training. Future work requires, not only an increase in sample size but also, the definition and incorporation of learning predictors that allow the pre-selection of subjects before the training sessions, in order to prevent high number of non-learners