A deep learning-based multi-model ensemble method for eye state recognition from EEG

Eye state recognition plays an important role in biomedical informatics e.g., smart home devices controlling, drowsy driving detection, etc. The change in cognitive states is reflected by the changing in electroencephalogram (EEG) signals. There are some works for eye state recognition using traditional shallow neural networks and manually extracted features. The useful features extraction from EEG and the selection of appropriate classifiers are challenging tasks due to the variable nature of EEG signals. The deep learning algorithms automatically extracts features and often reported better performance than traditional classifiers in some recognition and recognition tasks. In this paper, we have proposed three architectures of a deep learning model using ensemble technique: convolution neural network, gated recurrent unit, and long short term memory for eye state recognition (open or close) from EEG directly. The study has been performed on a freely available public EEG eye state dataset of 14980 samples. The individual performance of each classifier has been observed, and also performance of recognition performance of the ensemble networks has also been compared with the existing prominent methods. The average accuracy 99.86% was obtained by the proposed method, and it is the highest performance ever reported in the literature.