Adaptive neural network classifier for decoding MEG signals
Research output: Contribution to journal › Article
We introduce two Convolutional Neural Network (CNN )classifiers optimized for inferring brain states from magnetoencephalographic (MEG) measurements. Network design follows a generative model of the electromagnetic (EEG and MEG) brain signals allowing explorative analysis of neural sources informing classification. The proposed networks outperform traditional classifiers as well as more complex neural networks when decoding evoked and induced responses to different stimuli across subjects. Importantly, these models can successfully generalize to new subjects in real-time classification enabling more efficient brain–computer interfaces (BCI).
|Number of pages||10|
|Publication status||Published - 15 Aug 2019|
|MoE publication type||A1 Journal article-refereed|
- Brain–computer interface, Convolutional neural network, Magnetoencephalography