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Multi-class Classification of Movement Intention for building a Brain Computer Interface A Brain Computer Interface (BCI) is a detection/classification system allowing sending commands without using any motor output. The most efficient BCIs are based on Electroencephalography (EEG), the study of the brain electric fields recorded by electrodes placed on the scalp. In a BCI the participant learns how to perform discrete mental tasks. Meanwhile the system continuously extracts some features of the EEG relevant to the tasks and attempts to classify them. For instance, the imagination of movement of body parts engender electric fields in different portion of the primary motor cortex, each one corresponding to the body part which movement is imagined. For a BCI based on movement intention the goal is to detect what portion of the motor cortex is being involved. We have a large dataset of EEG recorded on 8 individuals performing movement intention of either one of four body parts: left hand, right hand, feet and tongue. To obtain a good classification accuracy it is important to extract and select relevant features from the noisy single-trial EEG. The goal of the stage is to investigate several strategies to accomplish both feature extraction and feature selection in a noisy environment. The candidate is expected to have a strong background in Linear Algebra and Digital Signal Processing. Previous familiarity with Fourier Analysis and independent Component Analysis is a plus. No previous knowledge of the EEG is requested. On the other hand proficiency with Matlab or C++ or PASCAL is necessary. Practical issues ● The duration of the stage is 4-5 months. ● It will be supervised by Marco Congedo and Christian Jutten, at GIPSA-lab, Department Images and Signal, Team SIGMA-PHY, 46 avenue Félix Viallet. [email protected] ou [email protected] ● A all-in grant of 1095 Euros for the whole stage will be paid to the trainee.