A hybrid method for the decoding of spatial attention using the MEG brain signals

Cognitive factors like attention can modulate the brain activities in different cortical areas. The brain activities can be measured using different systems with different spatial and temporal resolutions. The magnetoencephalography (MEG) is one of those systems that can measure the brain activities in a high temporal resolution. Here the brain signals have been recorded using the MEG system from different brain areas of human subjects while doing a visual spatial attention task. These signals have been forwarded to a pattern recognition system for the possibility of predicting the attentional state of the subjects in two different positions. The proposed hybrid system consists of channel selection using Bayesian approach, feature extraction using the wavelet packet and feature selection based on entropy-based method. The final classifier was selected to be Naive Bayesian classifier for attentional state prediction. The results indicate that the proposed system can predict the location of the attended stimulus with a high accuracy, so it can be helpful for brain–computer interface (BCI) applications.

► A hybrid method for the decoding of spatial attention is proposed in this paper. ► The brain signals are collected using the magnetoencephalography (MEG) system. ► The hybrid system consists of channel selection using the Bayesian approach. ► The system uses the wavelet packet for the feature extraction and the entropy for the feature selection. ► The decoding of the spatial attention is done using the Naïve Bayesian classifier.