Understand the experiment design for hyper connectivity studies Learning Objectives: Obtain the skills of electro-physiological signal processing Obtain the skills for hyper connectivity analysis Number of vacancies: 1 Description of student required: Prerequisites and As above requirements (if any): Financial Support (from host): Nil PROJECT 11 Concurrent EEG/fMRI: Neuroimaging Of Brain Activity and Connectivity with Research Title: High Spatial And Temporal Resolution Professor Tassos Bezerianos Supervisor / Department: Singapore Institute for Neurotechnology (SINAPSE), Cognitive Engineering Lab. Link to host’s online bio / research page: Functional neuroimaging has been a major tool for cognitive neuroscience, experimental psychology, and psychiatry. Among non-invasive imaging techniques, EEG and fMRI provide tremendous benefits to better understanding of the brain mechanisms behind mental processes, such as perception, attention, learning, etc. Although they both have limitations when used alone, their combination becomes a powerful tool for studying brain functions with high spatial and temporal resolution. The Cognitive Engineering Group in SINAPSE uses concurrent EEG/fMRI recordings to investigate brain activity and connectivity responsible for mental fatigue. For multi-modal data analysis, the Group is first looking for an effective Project Description: method to remove gradient artefacts and BCG artefacts from EEG signals acquired inside the MRI scanner. After the data is cleaned, cortical connectivity patterns from each modality, i.e., electrophysiological and metabolic, will be investigated based on graph theoretical analysis. The Group is also developing advanced methods to implement multi-modal data fusion. The Cognitive Engineering Group in SINAPSE is seeking a highly motivated and committed student for EEG/fMRI data analysis. The candidate is expected to be confident with applied mathematics and programming for the data analysis using Matlab and shell scripts. Students who are familiar with machine learning techniques are also welcome.
Learning Objectives: Understand the experiment design for hyper connectivity studies Obtain the skills of electro-physiological signal processing Obtain the skills for hyper connectivity analysis Number of vacancies: 1 Description of student required: Prerequisites and requirements (if any): As above Financial Support (from host): Nil PROJECT 11 Research Title: Concurrent EEG/fMRI: Neuroimaging Of Brain Activity and Connectivity with High Spatial And Temporal Resolution Supervisor / Department: Professor Tassos Bezerianos Singapore Institute for Neurotechnology (SINAPSE), Cognitive Engineering Lab. Link to host’s online bio / research page: Project Description: Functional neuroimaging has been a major tool for cognitive neuroscience, experimental psychology, and psychiatry. Among non-invasive imaging techniques, EEG and fMRI provide tremendous benefits to better understanding of the brain mechanisms behind mental processes, such as perception, attention, learning, etc. Although they both have limitations when used alone, their combination becomes a powerful tool for studying brain functions with high spatial and temporal resolution. The Cognitive Engineering Group in SINAPSE uses concurrent EEG/fMRI recordings to investigate brain activity and connectivity responsible for mental fatigue. For multi-modal data analysis, the Group is first looking for an effective method to remove gradient artefacts and BCG artefacts from EEG signals acquired inside the MRI scanner. After the data is cleaned, cortical connectivity patterns from each modality, i.e., electrophysiological and metabolic, will be investigated based on graph theoretical analysis. The Group is also developing advanced methods to implement multi-modal data fusion. The Cognitive Engineering Group in SINAPSE is seeking a highly motivated and committed student for EEG/fMRI data analysis. The candidate is expected to be confident with applied mathematics and programming for the data analysis using Matlab and shell scripts. Students who are familiar with machine learning techniques are also welcome.
20160225104654_NUS%20Projects%20&%20Form.docx