Hubert Cecotti, Ulster University, UK
The detection of brain responses corresponding to the presentation of a particular class of images is a challenge in Brain-Machine Interface (BMI). Current systems based on the detection of brain responses during rapid serial visual presentation (RSVP) tasks possess advantages for both healthy and disabled people, as they are gaze-independent and can offer a high throughput. We propose a novel paradigm based on a dual RSVP task that assumes a low target probability. Two streams of images are presented simultaneously on the screen, the second stream is identical to the first one, but delayed in time. Participants were asked to detect images containing a person. They follow the first stream until they see a target image, then change their attention to the second stream until the target image reappears, finally they change their attention back to the first stream. The performance of single-trial detection was evaluated on both streams and their combination of the decisions with signal recorded with magnetoencephalography (MEG) during the dual RSVP task. We compare classification performance across different sets of channels (magnetometers, gradiometers) with a BLDA classifier with inputs obtained after spatial filtering. The results suggest that single-trial detection can be obtained with an area under the ROC curve superior to 0.95, and that an almost perfect accuracy can be obtained with some subjects thanks to the combination of the decisions from two trials, without doubling the duration of the experiment. The present results show that a reliable accuracy can be obtained with MEG for target detection during a dual RSVP task.