The use of Electroencephalogram (EEG) signals in the field of Brain Computer Interface (BCI) has obtained a lot of interest with diverse applications ranging from medicine to entertainment. In this paper, BCI is designed using electroencephalogram (EEG) signals where the subjects have to think of only a single mental task. EEG signals are recorded from 16 channels and studied during several mental and motor tasks. Features are extracted from those signals using several methods: Time Analysis, Frequency Analysis, Time-Frequency Analysis and Time-Frequency-Space Analysis. Extracted EEG features are classified using an artificial neural network trained with the back propagation algorithm. Classification rates that reach 99% between two tasks and 96% between three tasks using Space-Time-Frequency-Analysis and Time-Frequency-Analysis were obtained.
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