Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion.

ABSTRACT: Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the propos

SUBMITTER: Zafar R 

PROVIDER: S-EPMC5448783 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature