The BCMI-MIdAS (Brain-Computer Music Interface for Monitoring and Inducing Affective States<\/em>) is a collaborative project between the Universities of\u00a0Plymouth<\/a>\u00a0and\u00a0Reading. The work is funded by two 54-month\u00a0EPSRC<\/a>\u00a0grants, with additional support from the host institutions. The project aims to use coupled EEG-fMRI to inform a Brain-Computer Interface for music… The ability to anticipate future input seems to underpin many cognitive processes, allowing cognitive agents to respond adequately to fast input signals. Thus, a fundamental question in cognitive neuroscience is what mechanisms might give rise to such signal processing capabilities… Design and implementation of Empirical Mode Decomposition (EMD) signal processing method for application on neurological signals. The development takes great emphasis on to mathematical framework of EMD as well as its parallelization, allowing for Graphical Processing Unit (GPU) usage… What might cognition be if not computation? Criticisms of the computational metaphor in cognition are well known. This project will review an alternative metaphor for cognitive processes\u2014Stochastic Diffusion Processes (SDP), grounded upon a metaphor of communication and interaction\u2014and evaluate claims that it is robust to such a prior critiques… The imaging modalities used in the many recent studies suffer from complementary limitations of both technologies; EEG has a poor spatial resolution and fMRI has a low temporal resolution. An important aspect of this research project is the use of simultaneous EEG and fMRI registration on the semantic and syntactic information processing in the brain which will allow to improve the characterization of cognitive processes by combining the strengths of both techniques… Sensory integration is the fusion of information from different sensory modalities in the brain. The project aims to investigate the processes that underlie sensory integration by studying body ownership and agency of motion. Body ownership refers to the ability to recognize ones own body parts as belonging to on to one… While the motion of our limbs may often feel continuous and smooth, there is significant evidence to suggest that this is not how the brain actually encodes our movements; rather, it instructs the muscles to make a series of discrete movements, which blend together into what appears to be a fluid action. These discrete movements are known as submovements; they can be observed in infants who are still learning basic motor skills, stroke patients who have lost motor function, and healthy adults when forced to make a rapid adjustment to an ongoing movement (e.g. tracking an erratic target with their hand)… Train drivers are exposed to many repeated sequences of line side signals colour light signals which follow formalised rules. This project aims to use EEG and novel data analysis methods, to measure how the cortices of the brain perform the task… One of the most challenging problems in neuroscience is understanding how the brain performs computations. This requires understanding of how the network of neurons takes a set of inputs and transforms it into a set of outputs. The use of cultured neuronal networks is used as a model to study their in vivo counterparts. It allows researchers to investigate neuronal activity in a much more controlled environment than would be possible in a live organism… Regarding the assistance for motion of the subjects, there is a growing interest in using robotic devices to deliver effective assistance during the intensive and repetitive therapies. However, no such system has been demonstrated so far in practice, largely due to lack of the hardware solutions able to generate sufficient forces yet producing compliant interaction with a human (“soft-ware”), as well as nonexistence of appropriate control laws to drive such robotic devices. To produce the compliant interaction, we aim at implementation of \u2018structural transparency\u2019 whose concept is such that the mass of the robotic arm is so small that patients performing spontaneous motion do not feel the inertia of the robotic arm… Recent work has shown that motor rehabilitation during the acute stages can decrease the effect of stroke. It is important to consider motor learning in the context of brain plasticity. The signal flow in the brain’s motor control system can be described as: a motor command generated in motor areas, that goes via subcortical and brainstem nuclei, to the spinal cord, and finally to specific muscles… The proposed project will focus on the development of non-standard control theory formulation for human like reaching behaviour based on the integration of multi-sensory information, overcoming disadvantages of a classical control theory which is bound to kinematics of rigid bodies. To fulfill the aim, the transformation matrices will be intensively examined as they directly link the sensory spaces with the work space in which the arm performs an action at a time…. How can human as well as other animals respond to constantly changing environment in spite of inevitable time-delay required for processing and transferring information in sensory-motor system? A predictive mechanism should exist in order to compensate this delay or to generate appropriate behavior. In relation to the predictive function for compensating the delay in visual-motor system, a number of psychophysical studies on hand\/eye-tracking behaviour have been carried out by using a variety of stimuli, including simple harmonic frequency. Yasuji et. al investigated particularly the proactive nature of the visual-motor system by steady and transient experiments of a hand-tracking task, and confirmed that the hand-motion precedes on the average the target-motion in steady runs within a finite frequency range of the sinusoidal target-motion… The concept of anticipation synchronisation in coupled systems is being seen as an area of expanding interest; in particular through the influence of a common external stimulus on such coupled systems. There are two types of anticipation, weak and strong anticipation. The future state in weak anticipation is governed by a model and the response of a system can be attributed to the running of the simulation for that model. Strong anticipation relies on the behaviour of past and\/or present states and is independent from predictions on the bases of assumptions… Emergence of autonomy for changing environment is a fundamental question in the living systems, and it is a challenging filed in robotics to implement the autonomy into the robotic system. Recent technological progress made it possible to develop the hybrid systems which integrate biological neurons and electric components. A hybrid system incorporating closed-loop control of a mobile robot by neuronal cultures can be created by Sensory Motor Coupling; the electric activity of neurons are recorded and converted to produce the motor commands to the mobile robot and the sensory information is fed back to the neuronal culture…
\nread more…<\/a><\/p>\nAnticipation<\/h2>\n
\nread more…<\/a><\/p>\nFast Parallel Processing for Neurological Signals<\/h2>\n
\nread more…<\/a><\/p>\nCognition as Communication and Interaction<\/h2>\n
\nread more…<\/a><\/p>\nJoint EEG\/fMRI on Extracting Integrated Semantic and Syntactic Information<\/h2>\n
\nread more…<\/a><\/p>\nVirtual Hand: Sensory Integration through body ownership and agency of motion<\/h2>\n
\nread more…<\/a><\/p>\nInvestigation of Submovement Microstates via Phase Synchronisation<\/h2>\n
\nread more…<\/a><\/p>\nVisual Attention in Train Driving<\/h2>\n
\nread more…<\/a><\/p>\nModelling of Neuronal Culture Activity Using Ising Models<\/h2>\n
\nread more…<\/a><\/p>\nDevelopment of Soft Modular Robotics<\/h2>\n
\nread more…<\/a><\/p>\nTemporal EEG analysis to detect the motor command generation<\/h2>\n
\nread more…<\/a><\/p>\nIntegration of Visual and Somatosensory Feedback through Learning<\/h2>\n
\nread more…<\/a><\/p>\nBrain mechanism of proactive control<\/h2>\n
\nread more…<\/a><\/p>\nFast and Furious: the nature of strong anticipation<\/h2>\n
\nread more…<\/a><\/p>\nSimulation of Autonomous Mobile Robot using Biological Brain<\/h2>\n
\nread more…<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"