Fabien Lotte

This chapter presents an introductory overview and a tutorial of signal-processing techniques that can be used to recognize mental states from electroencephalographic (EEG) signals in brain–computer interfaces. Expand

Brain-Computer Interfaces (BCI) are systems that can translate the brain activity patterns of a user into messages or commands for an interactive application. Expand

A major limitation of current Brain-Computer Interfaces (BCI) based on Motor Imagery (MI) is that they are subject-specific BCI, which require data recording and system training for each new user. Expand

We explored promising classification algorithms, both existing and new, to classify mental workload and emotions (valence and arousal) from EEG signals, with both subject-specific and subject-independent calibration. Expand

We present SEREEGA, Simulating Event-Related EEG Activity, a free and open-source MATLAB-based toolbox dedicated to the generation of simulated epochs of EEG data. Expand

Rapid serial visual presentation (RSVP) based brain-computer interfaces (BCIs) can detect target images among a continuous stream of rapidly presented images, by classifying a viewer’s event related potentials (ERPs) associated with the target and non-target images. Expand

We present a general-purpose open-source toolbox to simulate EEG data. Expand

This chapter describes the classical structure of the brain signal processing chain employed in BCIs, notably presenting the typically used preprocessing (spatial and spectral filtering, artefact removal), feature extraction and classification algorithms. Expand