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The development of suitable EEG-based emotion recognition systems has become a main target in the last decades for Brain Computer Interface applications (BCI). However, there are scarce algorithms and procedures for real-time classification of emotions. The present study aims to investigate the feasibility of real-time emotion recognition implementation by the selection of parameters such as the appropriate time window segmentation and target bandwidths and cortical regions. We recorded the EEG-neural activity of 24 participants while they were looking and listening to an audiovisual database composed of positive and negative emotional video clips. We tested 12 different temporal window sizes, 6 ranges of frequency bands and 60 electrodes located along the entire scalp. Our results showed a correct classification of 86.96% for positive stimuli. The correct classification for negative stimuli was a little bit less (80.88%). The best time window size, from the tested 1[Formula: see text]s to 12[Formula: see text]s segments, was 12[Formula: see text]s. Although more studies are still needed, these preliminary results provide a reliable way to develop accurate EEG-based emotion classification.

A simple architecture for a fully customizable cortical stimulator is presented. The whole device uses a 3D penetrating multielectrode array, which will be implanted in primary visual cortex (V1), offering different signal amplitude and waveforms sets. The system has been proved for injecting current (charge) in a safe, secure and precise way during acute animal experimentation. The architecture is based on a transistor based current injection stage and a microprocessor circuit with programmable waveforms. The dynamic characteristic of the stimulator provide the possibility to adapt the current level to the different working electrodes and tissue impedances. The system is specific tailored for a cortical visual neuroprosthesis for the blind.