Single-trial detection of realistic images with magnetoencephalography

The detection of brain responses corresponding to the presentation of a particular class of images is a challenge in Brain-Machine Interface (BMI). Brain decoding is nowadays possible thanks to advanced brain recording devices (fMRI, EEG, MEG), and the use of appropriate signal processing and machine learning techniques. Current systems based on the detection of brain responses during rapid serial visual presentation (RSVP) tasks use EEG recording. We propose to evaluate the performance of single-trial detection with signal recorded with magnetoencephalography (MEG) during an RSVP task where participants were asked to detect images containing a person. We compare several classifiers (LDA, BLDA, k-nearest neighbor, support-vector machines) with spatial filtering, and with different sets of channels (magneto-meters, gradio-meters, all the channels). The results suggest that single-trial detection can be obtained with an AUC superior to 0.95, while typical studies based on EEG recordings using the same type of tasks, the AUC is often around 0.8. The present results show that MEG can be successfully used for target detection during a difficult RSVP task.