Robust single-trial evoked potential detection for brain-computer interfaces using computational intelligence techniques
This proposal aim to develop computational intelligence techniques for pattern recognition of graphic elements (e.g. event-related potential, auditory evoked potential, k-complex, spindle) included in electro-encephalographic signals. More precisely, we want to develop adaptive computational intelligence techniques based on artificial neural networks, support vector machines and classical data analysis techniques to robustly detect evoked potentials in a single trial from noisy and multi-sources electro-encephalographic signals. The results of this work will be a comparison of the most powerful techniques developed and a complete procedure specifying the acquisition system parameters, the preprocessing techniques and a robust learning technique able to faster detect evoked potentials for brain-computer interfaces. This methodology will be available for other problems such as, in the medical domain, auditory evoked potential detection for automated newborn hearing screening and k-complex detection for automatic sleep scoring.
ERP detection needs to average responses of repeated stimulations. New computational intelligence techniques such as flexible networks and averaging techniques can lead to a faster detection of graphic elements. Ensemble models based on support vector machines or neural networks can also help to increase the robustness of the system. These methods will be investigated to improve brain-computer interfaces.
Cortex project-team, INRIA Nancy – Grand Est, France
Laboratory of Engineering Rehabilitation and Neuromuscular and Sensorial Research (L.I.R.I.N.S), Facultad de Ingeniería, Universidad Nacional de Entre Ríos, Argentina
Department of Biomedical Engineering, Valparaíso University, Valparaiso, Chile
Computer Science Department, Federico Santa María University, Valparaiso, Chile
Laboratory of Neuro Imaging Research, Autonomous Metropolitan University, Mexico DF, Mexico