Download Sensitivity and Specificity of Electroencephalographic Norms

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
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The duration of the stage is 4-5 months.
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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]
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A all-in grant of 1095 Euros for the whole stage will be paid to the trainee.