Folded ensemble deep learning based text generation on the brain signal

The text generation technique employs the transformation of the word document from the source to the targeted document based on the sequence to sequence generation. Video captioning, language identification, image captioning, recognition of speech, machine translation, and several other natural language generations are the application areas of the text generation techniques. The Electroencephalographic (EEG) signals record brain activity and are considered the source of information for using the brain-computer interface. Several kinds of research were developed for text generation. The most challenging task is more accurate text generation by considering the large contextual information and the significant features for generating the text. Hence, in this research, text generation using Folded deep learning is proposed for generating the text through text prediction and suggestion through the non-invasive technique. The EEG signal recorded from the patients is utilized for the prediction of the first letter using the proposed Folded Ensemble Deep convolutional neural network (DeepCNN), in which the hybrid ensemble activation function along with the folded concept in validating the training data to obtain the network stability and to solve the class imbalance issue. Then, the next letter suggestion is employed using the proposed Folded Ensemble Bidirectional long short-term memory (BiLSTM) approach based on the eye-blink criteria for generating the sequence-to-sequence text generation. The enhanced performance is evaluated using accuracy, precision, and recall and acquired the maximal values of 97.22%, 98.00%, and 98.00%, respectively. The proposed method can be utilized for real-time processing applications due to its non-invasive nature.